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Mike Hadjadj, a retail and SaaS expert, has just published a book (in French) called SaaSpocalypse. Every year, he supports two to three hundred start-ups as a mentor, advisor and investor, mostly in the retail sector. In his own words, he isn’t predicting the end of software vendors, but he does see their value being hit hard by the changes AI is bringing. I interviewed him to understand what this shake-up means for vendors, their customers and the whole SaaS ecosystem.

SaaSpocalypse Now! And The Value Drain

Mike Hadjadj presentinghis book SaaSpocalypse (in French) at the Visionary Marketing offices in October 2026

TL;DR

• With SaaSpocalypse, Mike Hadjadj isn’t predicting the end of software vendors. He’s saying the price of software is now negotiable.
• French e-commerce giant La Redoute rebuilt a €360,000-a-year tool in 48 hours using AI.
• Three factors can shield a SaaS vendor, namely urgency, value and stickiness.
• Big vendors are not spared. Some retailers have halved their Salesforce budget, or even cut it to a third.
• Gartner is looking in the rear-view mirror, while Mike is watching the 2027 contract renewals.
• His advice to vendors is simple. Talk to your customers now and ask them why they pay.

SaaSpocalypse Now! or How to Build a SaaS Without Coding

M.H. The book started with a chat at my physio’s. I’d had an accident and needed rehab. He’s a bit of a geek and told me he was setting up a new business line without buying any software or paying for a single SaaS subscription. Using Base44, a tool owned by Wix, he’d rebuilt a small CRM, a training tool and a small accounting and invoicing package himself. That intrigued me.

I run La Retail Tech, a community that has been going for four and a half years, with lots of content and around 3,000 members. Our community platform was grossly outdated. It had stopped evolving and every small request was sitting in a support ticket for three weeks.

We Dreamed It, and AI Delivered

I don’t sleep much, so I gave it a go and I spent a few hours talking to an LLM, asking it to rework parts of the site and give it a modern look. I ended up with a front end, which I showed my team after the weekend. They recognised the site they’d been dreaming of.

We had almost launched a tender, complete with requirements definition and technical provisos. That meant bickering over compromises with the team, because not everything is possible. With AI, everything is crystal clear. I talk and hey presto! It’s done.

SaaSpocalypse Now! Mike Hadjadj during a demo for his community, La Retail Tech. Image by Optimalways

I’ve kept going since the book came out. The front end is done and I’m now tackling the back end. The agent kept misunderstanding me, so I found another agent that handled my agent, which I talk to on WhatsApp. It knows my whole environment, runs the tests and writes very detailed prompts that I just copy and paste. I can’t code, and neither can my physio. So the question isn’t whether people can do it. It’s about who will still pay, for what, and where the value sits. That’s one of the book’s main stances.

A French E-commerce Giant Rebuilt a €360,000-a-Year Tool in 48 Hours

M.H. The other trigger was meeting French e-commerce giant La Redoute at an event. They used a visual product discovery tool, created by an Israeli start-up. The tool worked very well. Other French retailers used it too, such as Monoprix and Castorama, but you could tell it was nearing the end of its life. They were all looking for a replacement and asking me if I knew of one, before launching a tender.

La Redoute put two people on it for 48 hours, with AI. They studied how the tool worked, rebuilt it, tested it and released it. The copy is better than the original, because they could tune it to their own environment, something that a software vendor seldom allows. La Redoute was spending €360,000 a year on the tool, now they don’t.

SaaSpocalypse Companies of All Sizes Are Following Suit

Digging deeper, I found out that companies of all sizes, including large ones with the right skills, are already going after commoditised tools that aren’t core to the business. If the tool falls over, it’s no big deal. The savings are huge. When I talk about value destruction, it’s because the rebuild cost nothing like the old price. Two days, two people, a bit of maintenance, updates, testing and tokens. There’s simply no comparison. A small part of the value shifts to in-house skills, and some is bought externally, at a very different price. Most of it is destroyed.

There’s also a whole industry around SaaS spending. Agencies, consultancies, integrators and, above all, start-ups, which means funds and fundraising. If the pie shrinks, all of that ecosystem will be hit.

Urgency, Value or Stickiness, What Protects a Software Vendor?

M.H. I’ve been supporting start-ups for a very long time, as a mentor, an advisor, in incubators and accelerators, and as an investor too. Until now, I judged them on unwritten criteria, to decide whether I believed in them, wanted to back them or even invest. How innovative they were, how good the team was. That has changed, and I’ve come up with a new three-part test.

The first criterion is urgency. The need has to be pressing, driven for instance by regulation or competition. The second is value, which must be blatant, immediate and measurable. The third is new to me, and it’s stickiness. This is what will refrain end customers or competitors from copying your product in a few minutes.

SaaSpocalypse, an Innovator “Disrupted” by Its Own Customers

In the book, I cite Smartway, a pioneer in the fight against food waste. Its software tracked the best-before dates of perishable products in stores, to decide when to pull an item from the shelf and what discount would guarantee a sale. A salad or a yoghurt might go at 22% off one day and 40% off the next.

It first sold well to independent supermarkets and hypermarkets, whose owners are managing their own money. Then the central buying groups perceived the value of that piece of software. The IT divisions of the big French retailers, such as Leclerc, Intermarché or Carrefour, built more or less the same thing, which didn’t work as well but cost far less. It took them two to three years to get there.

SaaSpocalypse, version 1. The author warns that “this book is best read quickly, as it may age badly.”

The Better a Product Is, the More Likely It Is to Be Copied

The difference is that today, the better your product is and the more widespread, the easier it is to copy. The bulk of the work is no longer the code. It’s modelling what you can help the customer earn, the processes, the interfaces. Smartway was smart, because it moved into other areas, such as fresh produce and ordering, with higher barriers to entry. Commoditised SaaS companies, on the other hand, are finished. The better they are, the faster they get copied. A young company with no stickiness will sell pilots, their customers will love the result and decide to rebuild the software. You can’t blame them.

“The better your product is and the more widespread, the easier it is to copy”

Mike Hadjadj

For English retailer Save Your Wardrobe, stickiness is part of the equation. Its platform handles repairs for big leather goods and clothing houses, a field that’s still archaic. Anyone can digitise appointment bookings but Save Your Wardrobe also built and runs a network of approved repairers. They didn’t mean to do that at the outset. But now, it’s become a significant barrier to entry. Other barriers exist, such as standards and certifications, notably in payment systems and hardware. There is also external data customers need to buy to operate properly. Every vendor should ask itself why its customers wouldn’t have a good reason to copy its products.

Are Big Vendors as Vulnerable as Small Ones?

M.H. I was invited to talk about my book a few weeks ago at an event a vendor was hosting for its partners, on the eve of a trade show. I said not everyone would be hit in the same way. Small SaaS vendors would be hit straight away, while the big ones such as Salesforce would be hit later, and on core issues. The next morning, at the show, a friend told me a big brand had just cut its Salesforce budget to a third. That afternoon, the boss of another retailer told me he had renegotiated his contract and halved it.

Mandatory Modules They Never Needed

Their reasoning is simple. They were paying for mandatory modules they never used. Others were useful, but never quite satisfying. What interests them is the engine. They tell Salesforce, Oracle or SAP that they’ll only buy the engine, that they’ll rebuild the rest themselves by vibe coding, and that they therefore want the price divided by two, three or four. These big vendors aren’t stupid. They’ve understood that the value has shifted.

I’ve worked for software vendors before, including one that pioneered CRM before Salesforce. All our value rested on two things. What we brought the customer, meaning process modelling, and how we delivered it, meaning the code, its robustness and its ease of use. To extend the product range, we were better off buying small companies than developing in-house, which would have taken years and cost a fortune. Between 1999 and 2006, we took over twelve small vendors, covering marketing, emailing and other functions.

SaaSpocalypse and Why the Value Is No Longer in the Code

Today, it’s the complete opposite. I show an agent an existing feature, ask for the same thing but improved, tailored to my needs, and it’s done almost before I’ve pressed the transmit button. The value is no longer in the code. It’s in the ability to express, visualise, project and model what you want. Anyone can write prompts, but they’re often mediocre, and many people give up because it’s too complicated. But the people who used to write the specs of the software know how to do it, and this will save a huge amount of time and budget.

“The value is no longer in the code. It’s in the ability to express, visualise, project and model what you want”

Mike Hadjadj

Will Adobe Creative Suite Fall Victim to the SaaSpocalypse?

Photoshop Cloned With AI. Is It SaaSpocalypse for Adobe?

In early October, developer Brandon Thomas released ArtCraft, a set of free, open-source clones of seven Adobe programs, including Photoshop, Illustrator, Premiere Pro and Lightroom. He wrote them in Rust with Claude Opus 5.5, running several agents in parallel. The first repositories date from late September.

The thirteen apps in the ArtCraft suite (screenshot)

What Backs Up the SaaSpocalypse Thesis

• The look and the everyday features can be copied very quickly. According to Digital Camera World, testers say anyone coming from Photoshop gets up to speed almost instantly.
• PhotoCraft, the Photoshop clone, opens most native .psd files, according to Developpez.
• The project passed 40,000 stars on GitHub within a few days, which shows the appetite for a free alternative.
• The trigger is the one Mike describes. A customer fed up with a vendor’s prices and terms, in this case Adobe’s cancellation fees, would rather rebuild the product themselves.

What Undermines the SaaSpocalypse Thesis

• The project is in alpha, unstable and incomplete. Brandon Thomas promised feature parity within a month, and now talks about several months.
• The analysis by BiGGo Finance says PhotoCraft covers around 76% of features, but only 45% of what professional use demands. Of 256 files created with Photoshop, 133 display pixel-for-pixel identically, and filters match in only 5 cases out of 30.
• Mature software is valued above all for its edge cases, handled over decades, which appear on no feature list. An Adobe engineering director reportedly said this year that rewriting Photoshop would be almost impossible and pointless, according to the same source.
• The legal position is far from settled. Jonathan Bailey of Plagiarism Today, who stresses that he isn’t a lawyer, points out that copyright protects code, not functions. He sees the main risk in Adobe’s terms of use, which forbid studying the software in order to recreate it. Trademarks and patents remain open questions, and none of the articles I read reports an official reaction from Adobe.
• Sources differ on some figures, such as the number of commits, the number of stars or the exact list of apps. I’ve only kept what is corroborated.

My Take on the Adobe SaaSpocalypse

This case doesn’t prove Photoshop is doomed. It shows that copying the look has become almost free, and that a vendor’s defence lies elsewhere, in reliability, file compatibility, edge cases, support and the law. That’s the stickiness Mike is talking about.

Sources

AppleInsider, Vice, Digital Camera World, BiGGo Finance, Plagiarism Today, Developpez.

If Value Leaves Software, Where Does It Go?

M.H. It moves, but I’m convinced part of it is destroyed, because a whole ecosystem lives off software. A big slice of the pie is going to vanish. Another slice shifts to infrastructure, networks and people, meaning the ability to model, manage the project, support and maintain. It could mark the return of the freelancer.

People always talk about make or buy, building in-house or purchasing. I think a middle way is going to appear, which I’d call “buying make”. A large company shall give freelancers or consultants a very simple brief to copy this application or rebuild it in two or three days. It costs far less. There’s no tender and not even a specification, since you’re asking for something that already runs.

This morning I read about a new tool designed to copy existing applications. In the prompt, you no longer even say what to build, just to do what that tool does. You describe the environment and what it needs to integrate with, then you improve it, simply by talking. So there’ll be value in people, in agility, a bit in tokens, and perhaps in teams, internal or external, that check, integrate, validate and secure. But it won’t be the same value.

Retailers Don’t Build Software, So Aren’t You Living in a Bubble?

M.H. This is laughable. A retailer’s job wasn’t to build e-commerce sites either, and they all did.

I’ve taken retailers to China several times, and they were shocked. Over there, a big retailer has no IT team. In France, the checkout system is the most sensitive thing there is, and everyone has their own. In China, at a self-checkout, you see machines in three colours, depending on whether you pay with Alipay, WeChat Pay or something else, each running on a different information system. That would be unthinkable in France. What’s more, these systems are run by outsiders, Alibaba or Tencent, which means sales data is shared. The retail boss I took there couldn’t believe it.

A Cultural Divide

But it’s a cultural thing. They all share the same website and the same architecture. When a new feature comes out, everyone gets it. The same goes for marketing, built around two or three big platforms such as https://www.jd.com, Tencent and Alibaba. Their job, they say, isn’t to be IT people. It’s to buy the right products, price them right and keep the promise, for example a thirty-minute delivery to the other side of Beijing.

In France, we did the opposite. For years, we built up oversized IT, development and innovation teams. They have to be kept busy, so we redevelop things, then we maintain them, and the teams keep growing. Saying retailers aren’t developers is wrong. They already do a lot of make or buy. They built big systems and bought innovation from outside, because it often comes from start-ups. Today, they risk rebuilding innovation before they’ve even bought it. They’ll face the same shift, because their teams include a lot of developers they will no longer need. You no longer create code by developing it, but by prompting it and setting objectives.

AI Cuts the Cost of Building, Not of Maintaining, and Your Portal Isn’t Live Yet, So Isn’t That What You Are Still Testing?

M.H. My answer is that it depends. First, the build is generally far heavier than the annual maintenance. When that cost goes, you’ve already gained a lot. Then you need to look at what maintenance actually involves. There’s hosting, but when it’s in-house, you do what you want. There are new versions, imposed for security reasons or to meet the needs of other customers. Here, you only deploy what you need, for example when a tool in the environment has changed. If I need a new feature, I build it, test it and release it, on demand. Sometimes I just swap an API, and that’s it.

The amounts are hard to pin down, because we’ll mostly use internal teams, so we’re talking internal cost, not selling price. Even if it took the same time, I’m convinced the cost will dwindle. For my portal, which we’re rebuilding, I paused to write the book, and then I tackled the back end. I’m not aware of the maintenance costs at this stage. There is a learning curve.

Then there’s the cost of tokens, which people say will multiply. Today, we obviously pay far less for tokens than they’re really worth. But the big LLM players are in such fierce competition that they can’t let prices soar. There’s open source, DeepSeek, the approach taken by Mistral, and others will follow. Costs won’t fly to the moon. What may happen is that some of the players of the AI scene may go under. They raised billions, they have outsized valuations, and not all of them will survive.

Gartner Forecasts 15% More Software Spending for 2026, Yet a Trillion in Market Value Has Vanished, So Where Is the Value Destruction?

M.H. Gartner only sees the multi-year contracts already under way, and possibly those being signed. It can’t see what the renewals will look like. Yet we’re living through an extremely violent change, not to say a revolution. We’ve seen others, but never on this timescale. Spending is in the rear-view mirror, while the markets look ahead.

The figure I’m watching is the 2027 renewals. By then, end customers will have run tests. Some will simply renew, others will renew but negotiate hard. They’ll ask their vendor to divide maintenance or the SaaS price by two, three or four, and vendors with their backs to the wall will say yes. I don’t think Gartner has anticipated this, because the customers themselves haven’t done it yet.

SaaSpocalypse and the Wake-Up Call

When I talk again to people I saw three or four months ago, they’ve already changed their minds, because they’ve tested. When I spent a night on Base44 rebuilding the La Retail Tech portal, I didn’t know I was going to replace everything, or how long it would take. It’s the same for them. They’ll build one piece, then another, and gain confidence. They’ll make mistakes too, inevitably. But they aren’t stupid. They’ll start with low-risk topics to get a feel for it.

Gartner can’t survey that. Usually, you ask decision-makers about their spending over one, two or three years, and they have budgets and strategic plans. Here we’re in a different world, because these are end customers who are making money. The IT and AI teams will bring in money by cancelling SaaS contracts and replacing them. That cash, obvious, immediate and permanent, will largely flow back up to group level, and show up in prices, competitive position or investment. Another part will be used to build other tools and chase even more savings. As far as I know, that’s completely new. I’ve never seen a support function, a cost centre, suddenly end up with positive budget lines.

Isn’t Telling Vendors to Call Their Customers Putting Ideas in Their Heads?

M.H. This book is a first version. There’ll be a second, maybe a third. It’s there to start a discussion, which gets richer every day with the people I meet. I tell the SaaS vendors most at risk to go and see their customers right now and start debating. That isn’t a risk. It builds trust faster and puts the debate back where it belongs, because a vendor needs to know why its customers pay. The paradigm has shifted so much that a far more collaborative approach is needed. The customer can copy and intellectual property no longer counts, fine. But the vendor still has assets, outside expertise and contact with a good part of the market.

An Innovation Boss Is Bringing Tools Back In-House

The innovation boss of a big retailer told me last week that they’re going to bring a lot of tools back in-house, but that if they take in too many, innovation will dry up. It comes from outside, from small start-ups, from young people with brilliant ideas whom I meet in incubators. If nobody buys from outside any more, it disappears. The second point is the outside view the vendor brings, the pooling of experience and the external data. You have to sit down with your customers and work out how to keep bringing them value tomorrow, when it will no longer come from the code.

I’m not predicting the death of software vendors. I’m saying the price of software is negotiable and nobody has told the shareholders. We’re going to go through a fairly brutal reality check, and something else will come out of it. As usual, the best will pull through, and the best are those who saw it coming.

A Technological Lead Won’t Save You From the SaaSpocalypse

When a start-up boss tells me his technological lead protects him, I get very worried. When he tells me he’s been talking to his customers and partners for six months, that he’s reworked his offer and is testing it, I like that. You need constant reinvention and agility. If these founders have them, it’s because they were very good to begin with, and there’s no reason for that to change. You have to look up, not waste time and tackle this together. That’s why we’ll be running working groups between start-ups, big vendors and big retailers, to create value for everyone, a new landscape and new ways of working together, without too much damage.

“I’m not predicting the death of software vendors. I’m saying the price of software is negotiable and nobody has told the shareholders”

Mike Hadjadj

Mike Hadjadj is thus waiting to observe who has engaged in dialogue with their customers. Until that point, the SaaSpocalypse remains on the agenda.

THE BOOK (IN FRENCH)

The Guest

Mike Hadjadj, thirty years in B2B technology (Coheris, Wincor Nixdorf, Generix, Aurea), founder of https://iloveretail.fr and co-founder of La Retail Tech (around 220 companies, more than 3,000 members), author of “SaaSpocalypse” (in French, version 1, August 2026) and speaker at some fifty events a year.

The post SaaSpocalypse Now! And The Value Drain appeared first on Marketing and Innovation.

What if the future of work were about giving people a rela benefit for getting back to the office? On Thursday 24 September, the first International Workplace Summit (IWS 2026) took place at the Campus Cyber in La Défense, Paris. The audience was made up of corporate real estate directors, workplace and facility managers and HR heads from all over Europe, all there to tackle what sounds like a simple question. What needs to change in the way we design and run workplaces?

It is anything but an academic question. Since Covid, the way we behave has changed, and society is shifting faster than ever. People now like to talk about a BANI world (brittle, anxious, non-linear, incomprehensible). The acronym is a blatant overstatement, but there is some truth behind it. The years before 2010 were fairly stable, and since then one disruption has followed another at breakneck speed. What we still need to give young people is a future worth working for, not just doom and gloom or a sales pitch for the latest tech. Here is my take on that morning in La Défense.

IWS 2026: Making the Office Worth the Commute

Future of work? On Thursday 24 September, the first International Workplace Summit (IWS 2026) was held at the Campus Cyber in La Défense.

Artificial intelligence is at the heart of the future of work. We have been writing about it on Visionary Marketing since 2016, long before large language models came along and sparked something close to mass panic, helped along by the tech giants who keep telling us chatbots are about to wipe out humanity.

IWS 2026: what does the future of work look like? And will the office be worth the commute?

These predictions are overblown, as Forrester’s J.P. Gownder told us a few months ago. Still, something is clearly going on, and the IWS morning gave us three takes on it which, put together, paint a pretty full picture of the future of work. First a diagnosis, then a tech pitch, and finally a vision. And God knows we all desperately need such a vision.

Future of Work at The Campus Cyber

A pet project of Emmanuel Macron’s, the Campus Cyber opened on 15 February 2022 in La Défense. It sits in the Eria tower, easy to spot thanks to its petal-like shape, and is home to some 1,800 people, including teams from ANSSI, France’s national cybersecurity agency. Around a third of the building is made up of collaborative spaces open to the whole sector, where big corporations, start-ups, government agencies, schools and research labs rub shoulders.

The interior was designed by Projective Architecture, which took care of client-side project support, space planning and design and build on some 208,000 sq ft (19,300 m²) of the tower’s 280,000 sq ft (26,000 m²) and 13 floors. That includes the restaurants, a library, event spaces, a showroom and terraces.

A Former Labour Minister’s Diagnosis at IWS 2026

The former French Labour Minister, who spent earlier stints at Danone and 3DS, brought out a graphic novel on the subject in 2025 with journalist Mathieu Charrier and artist Nicoby, Travailler demain (“Working Tomorrow”, in French). She asked the audience to “zoom out” from the workplace for a moment and look at the decade ahead. As she sees it, four waves are hitting us at once. AI, going green, demographics and our changing relationship with work.

Fopremer French Ministry of Labour Muriel Pénicaud at IWS 2026 sharing her views about the future of work.

Three numbers show how big this is. According to the IMF and the OECD, AI will affect 50 to 70% of jobs in developed economies over the next ten years, and by “affect” she means “destroy, create or transform”. The World Economic Forum reckons two thirds of the jobs our kids will do don’t exist yet. The third figure is the least known and the most striking.

Since the late 1980s, the OECD has been tracking how long the skills students pick up in vocational higher education stay useful. A generation ago, the answer was thirty years. Today, it is apparently two. Muriel Pénicaud draws the obvious conclusion. The old model of studying first and working afterwards is dead, and we will all be learning for the rest of our lives.

Mind you, I have a feeling the best people have always worked that way. Is it really that new?

IWS 2026 host Lionel Cottin with Carole de Crozet and Jérôme Galletti. 10 trends showing us a desirable future of work

What Is Really New

What is genuinely new is who bears the brunt. Earlier waves of automation targeted factories. This one is aimed first at white-collar workers, back-office functions, finance, HR, marketing and middle management, which she calls “the glue that holds companies together”. She sees an opportunity here, getting rid of the drudgery, and a risk, losing sight of why our work matters. A large teaching hospital in Montreal shows how this can play out. It brought in AI to handle its logistics and saved around 25 to 30%. Instead of pocketing the money, management put that time back into patient care, and patients now get better faster. People came first again.

Finally, some good news.

Going green in La Défense, as seen from the Campus Cyber where IWS 2026 was held. Looks like there is still some way to go.

The rest of her talk ran through the move to net zero, which “HR is still barely dealing with” (how do you retrain Silesian coal miners in a country that gets 80% of its energy from coal?), falling birth rates, with 670,000 young people about to enter the French job market against 956,000 at the peak, and remote work. The studies she quotes show that working from home boosts individual productivity but hurts creativity, problem-solving and the sense of belonging. For her, whispering into a headset in an open-plan office to talk to Singapore is pointless. If you come into the office, it should be to do things together.

All of this is spot on, well researched and pretty unsettling. Over coffee, quite a few of us were thinking the same thing. If there had been more young graduates in the room, they would have walked out believing most of what they just learned is already out of date. This kind of diagnosis, which well-informed people repeat at one conference after another, is not a vision of the future. It doesn’t make anyone want to get behind a project. Muriel Pénicaud admits as much. Young people aren’t checking out, they are just committing differently, to projects that mean something rather than to institutions. But somebody has to offer them those projects.

Futureof Work: Tom Ryckaert’s Tech Promise at IWS 2026

The second take came from Tom Ryckaert, a director at Planon, which makes real estate and facility management software, and author of Disruptive Workplaces. His argument boils down to one line. The question is no longer whether AI will help us work, but whether work will still happen in the workplace.

Tom Ryckaert, author of Disruptive Workplaces, came over from Belgium to give his talk.

He went through it at a cracking pace. Predictive maintenance, with 40% less downtime and 25% lower repair bills, a new generation of platforms, which research firm Verdantix calls CPIPs (connected portfolio intelligence platforms), hooking building management systems up to the company’s IT. The air conditioning throws an alarm, a work order goes straight to the right contractor, and nobody in the building ever had to pick up the phone. The employee journey runs itself too. Your app warns you about a traffic jam, books you a parking space with a charger, offers you the desk next to your usual one when that is taken, and orders your Tuesday chicken poke bowl.

It’s All Here Already

All of this is already out there, Tom Ryckaert insists. Robots and cobots (collaborative robots built to work next to people without a safety cage) cost between €10,000 and €50,000 today and will soon come down to €5,000. He even dusts off “facility as a service”, an idea he was pushing ten years ago, where you call on services when you need them, just like software.

Ryckaert also reminds us that 20% of a building’s total cost is locked in at the design stage and the other 80% over decades of running it. He says we should cut “the AI elephant” into bite-sized, practical use cases instead of trying to do everything at once. Above all, he quotes Tariq Chauhan, who runs the Middle Eastern service provider EFS and told him it will always come down to people, because they are the ones keeping the services going every single day.

Yet all this accumulation of tech leaves a big question unanswered. You hear it at most trade shows, and it doesn’t really address the issues raised an hour earlier. A building that orders your lunch tells you nothing about why you are there, or why a young person who has just turned down a permanent job would ever come back. Many of the solutions on show make sense, and some are genuinely appealing. But they deal with running the building, not with meaning. The problem Muriel Pénicaud put her finger on is a societal one, almost a question of civilisation, and no connected platform is going to fix it on its own.

Ten Trends Worth the Commute

The third take came from the people who designed the very building we were sitting in. Jérôme Galletti and Carole de Crozet of Projective Architecture came up with the Campus Cyber around ten years ago and fitted it out five years ago. Rather than give us a slideshow tour, they used it to pull out what Jérôme Galletti calls “ten or so desirable trends for the future of office buildings”. That word matters. The idea is no longer to put up with an uncertain future, but to spell out what we actually want to build, and to make the office worth the commute.

Touring the Campus Cyber boardroom during IWS 2026. No pecking order around this table.

From this project and our research, we came up with ten or so desirable trends for the future of office buildings.

Jérôme Galletti, Projective Architecture

The first trend turns an old habit on its head. Office buildings are too often fortresses that turn their backs on the street. At the Campus Cyber, says Jérôme Galletti, anyone can walk in without showing ID, whether they are passing by, looking for a job or studying. They can grab a coffee, work for a few hours, catch an event and ask questions about cybersecurity. Carole de Crozet explained how the building then becomes the hub of a whole sector, where private firms, public bodies, start-ups and schools gather around the same issue, so they can work together and do business faster.

Getting people under one roof isn’t enough, you have to give them a reason to mingle. A typical office building runs a handful of events a year. The campus runs six hundred, two or three a day, with some thirty people working on nothing else. The lecture theatre built for one-way speeches gives way to a flexible hub that can host several stages, a hackathon or a dinner. The showroom full of products going back to the 1950s makes way for an innovation centre where in-house teams and clients work together on what comes next. And food is no longer just a lunchtime affair, with five different spaces that double up for client meetings, training sessions or brainstorming.

Seventh desirable trend: digital has to earn its place. Couldn’t agree more.

Digital is on the list, but it plays a different part.

It is no longer stuck on the screen at your desk and should add something to every space. The cyber-experience room on the third floor puts executive teams through crisis drills, with sensors that let the facilitators see how participants are holding up as the exercise unfolds. The last few trends are strikingly simple. Get rid of long corridors and turn the spaces in between into places where people actually talk and work, deal with acoustics everywhere (noise is the number one complaint in offices, and at the Campus Cyber even the ceiling decor soaks up sound), and build in flexibility from day one. The campus spaces change use as needs change, with no need to knock anything down.

What makes this talk stand out isn’t so much any single idea as the approach behind it. Technology is just one tool among many. The starting point was how people actually use the place and what they experience there. We love saying young people are disengaged, and there is some truth in it. We are far less good at giving them an experience that makes them want to turn up. Galletti and Crozet showed, in a real building we walked around straight after, that it can be done.

The Desk, From Object to Experience

The workshop on workstations brought the same thinking down to the individual desk. Alban-Michel Moreno of Ministry of Makers pointed out that ergonomics is about how the body relates to objects, but it also covers acoustics and, beyond that, the whole office experience, right down to going for a lunchtime run with colleagues. Nobody comes to the office for the kit anymore. Crédit Agricole‘s project at its SQY Park campus in Guyancourt, just outside Paris, is a good example. Staff tried out six different monitor arms themselves, on a simple principle. The people who use them know best.

Hot-desking has a catch, though. The desk doesn’t belong to anyone anymore. Everything looks the same. It is nice, clever, well thought out, and completely soulless. People don’t like using a keyboard or mouse that isn’t theirs, and Fellowes says you can’t work properly on a laptop for more than two hours. These projects only work if people make the space their own. You have to leave room for the kids’ macaroni necklace and the holiday snaps, or the office will never be worth the commute.

Future and Present of Work: From Global to Local

The opening round table, with Kantar, Tetra Pak, Servier, consultancy Jaicost and Korus Group talking about running sites in several countries, had already raised the issue at nine in the morning.

The speakers described a constant tug of war between global standards and local cultures, with shared rules on accessibility, sustainability and ergonomics on one side and each country’s own habits on the other. Work doesn’t look the same everywhere. And yet wherever you go, offices look strikingly similar, and the hybrid debate keeps going round in circles because nobody has really pinned down what is hybrid about these organisations.

A multi-country round table on how work is changing around the world.

The International Workplace Summit

IWS 2026 is a hybrid event run by Agora Managers Groupe on the initiative of trade publication Anews Workwell. Its organisers bill it as the G20 of the workplace and facility management, bringing together business leaders, policymakers, researchers, philosophers and industry players. Its tagline is “Experience the future. Now.”

ESEIS and Korus Group are its Platinum partners, alongside Planon, Jaicost, DEYA, Fellowes, MerciYanis, SaaSOffice, Smart Impulse and Projective Architecture, which also ran the building tours. Topics ranged from circular buildings and AI and robotics in smart buildings to employee experience and the regenerative workplace. The full programme is on the official website.

What the Future of Work Is Missing: an Office Worth the Commute

So the IWS morning gave us, in that order, a clear-eyed diagnosis, a whole lot of tech and a proposal. We need the first two. Muriel Pénicaud is right that uncertainty is now the only certainty, and Tom Ryckaert was right to remind us that the tools to run buildings on autopilot are here. But neither of them is enough to get teams on board, let alone a generation that puts meaning ahead of job security.

And by the way, Gen Z doesn’t have a monopoly on wanting work to mean something. According to Apec, the French executive employment agency, 95% of managers of all ages say it matters to them that their job makes sense. Another survey, by marketing research company Ifop in January 2023, found that only 21% of French workers see work as “very important” in their lives, down from 60% in 1990. That figure is 21% for 18-24 year-olds and 23% for 50-65 year-olds, barely any gap between generations (source: summary on Harmonie Santé, in French). Blaming the kids is a bit lazy, and a convenient way of ignoring what most of the workforce quite rightly wants.

What the future of work is missing is a positive, can-do vision, from people who can say what they want to build and then go and show it. That is exactly what Jérôme Galletti and Carole de Crozet did when they talked about “desirable” trends in a building they dreamed up ten years ago. Muriel Pénicaud said much the same thing with her Montreal story. Automation only pays off if the gains go back into what makes people valuable. Food for thought…

Business leaders have all the data they need. Now it is up to them to make the office worth the commute.

The post Future of Work: Making the Office Worth the Commute appeared first on Marketing and Innovation.

It’s back to school and back to the blog too for the 31st time since 1995, and today’s topic will be AI and Creativity. The idea is to deal with the topic of artificial intelligence, not from a technical perspective, but from a more philosophical one, drawing both on my digital and artistic backgrounds. In 2026, the fourth version of my programme entitled “Content Creation in the Age of AI” will be delivered to all the students of the Omnes Education Group. A previous video was recorded in 2024 on that subject, and a serious refresh was needed. I have therefore gathered enough hindsight to learn more about the impact of AI, especially on music and the creative arts. And I decided to focus on just three things, to avoid getting lost in too broad a subject. First, the seeming popularity of AI artists on streaming platforms like Spotify, taking the example of an act called Breaking Rust. Second, the Stanford GSB study on visual arts and the impact of AI on creators. Third, my own thoughts and conclusions based on the above.

Has AI Stifled Creativity? A 2024-2026 Perspective

AI and Creativity and manufactured popularity, because such things exist as well.

In 2024, I asked myself the question, “Will AI stifle all creativity?” as part of my Shift24 programme designed especially for Omnes Education Group. This programme cannot be shared online, but my colleagues at Omnes were kind enough to let me publish this wee piece for the benefit of my readers and followers, and I wish to thank them warmly for this as well as their renewed support these past four years.

In this video dedicated to ‘creativity’, drawing on Merriam-Webster, I distinguished innovation from invention and chose to focus on the latter, specifically in music, a topic not yet covered in earlier courses.

I then illustrated this with Suno, which could churn out up to 900,000 pieces of music a day as early as 2024 (far outpacing Bach’s 1,128 compositions over a 65-year lifetime). Hence, I shared a song I had generated myself, “The Dying Cyberspace.” I won’t say that I’m proud of this song, but at least I had great fun generating it, and it was instant gratification. All this was raising questions related to the value of musical creation, the risk of creative stagnation, and the thorny issue of intellectual property, since these AIs are trained on centuries of human-created music. So far, nothing new under the sun. Almost every other journalist must have written the same story for the past three years.

However, I thought it lacked a bit of perspective. I then tempered the novelty of the phenomenon by pointing to historical precedents: Gershon Kingsley’s “Popcorn” (1969), Zoe Keating’s loop-based compositions (one example amongst many others), and Google’s Project Magenta as far back as 2016. These are mere examples. I decided to keep it short, but there has been no shortage of technology-backed creativity in music these past 50 years or more. Think of the late Klaus Schultze and his pals from Ash Ra Temple, Tangerine Dream, Kraftwerk, and all the so-called Krautrock school of thought to start with, not mentioning the new wave movement from the 1980s.

I also cited Wim Mertens, Philip Glass, Laurie Anderson, and German artists like Nils Frahm, who blended electronic and acoustic elements long before GenAI. The real difference today, I argued, is one of scale: such tools are now in everyone’s hands. Be it to create music of the ilk of the Dying Cyberspace or more ambitious AI-produced work like this entire fake Pink Floyd album, which didn’t sell as well as The Dark Side of the Moon or Wish You Were Here.

Somewhat courageously, I concluded at the time that AI wouldn’t replace musicians but would become just another instrument in the creative toolkit, with real innovation coming from how humans use these tools. Since music remains fundamentally about human expression and emotion, I thought rather optimistically, if not naively, that AI music didn’t mark the end of musical invention but rather the beginning of a new chapter.

Such were my thoughts from two years ago, now is the time to move forward and take a look at what happened in 2025-2026. As exmplained above, I focused this new research on three things. First, the seeming popularity of AI artists on streaming platforms like Spotify, taking the example of an act called Breaking Rust. Second, the Stanford GSB study on visual arts and the impact of AI on creators. Third, my own thoughts and conclusions.

Breaking Rust

Breaking Rust: a Spotify hit

Let’s start with Breaking Rust. Breaking Rust is an AI country act which hit number one on the Billboard Country Digital Song Sales chart in November 2025, on Spotify. The numbers are seemingly enormous: over 2 million Spotify monthly listeners, and over 3 million streams in under a month. The project is credited to Aubierre Rivaldo Taylor, even though very little is known about him. Rivaldo Taylor is apparently paid, I found out, on downloads only.

How much is it worth?

I tried to measure what this Billboard chart placement might actually bring to Mr. Taylor. Looking just at Spotify, and reckoning that musicians get paid anything between $0.003 and $0.005 per stream, the potential income was somewhere in the region of $9,000 to $15,000 for that first month alone. Extend that over a year and you probably reach something in the region of $100,000, assuming the popularity of the act does not dwindle, which it does tend to do, since it is already getting harder to find Breaking Rust on Spotify.

Then I tried to understand what it would take to push a song like this to number one on the charts. Say I create an AI track on Suno or a similar application, post it on Spotify, and want to shoot it to the top of the charts. What I learned is that so few pieces of music are actually bought by listeners on these platforms that if you simply buy a thousand downloads, you will reach number one very easily for this kind of music. So my reckoning is that if Rivaldo Taylor did this, it would have cost him something in the region of a thousand dollars for those thousand downloads. That’s a cheap and easy way to tweak the results and a rather easy path to the top.

Did Breaking Rust fake the applause? Sure lives on borrowed time, so he says.

Growth hacking

What happens on these platforms is not just about the content. It is about what digital marketing experts call growth hacking, in other words tweaking the stats, and this domain is fraught with fraud. Bot-driven streaming is the starting point, with bots listening, or rather fakely listening, to tracks to make them appear more popular than they really are.

On Deezer, one of the streaming platforms, it was found that 70 to 85 percent of streams are flagged as AI. This has reached a point where it now threatens the platforms themselves, because who really wants to listen to that much slop? Breaking Rust is not that bad, actually I find it rather good, but I do not feel like listening to it day in, day out. I want to listen to real artists.

The method that may have been used by Breaking Rust is common. It is hard to say for certain whether it was used here, but it is likely. The same kind of tricks turn up on other platforms, such as Instagram. An Instagram “photographer” named Joss Avery, supposedly based in London and shooting with a Nikon D810 camera, turned out to be an AI avatar posting machine-made photographs generated with Midjourney and Photoshop. When I looked at his Instagram feed, it was fairly obvious to me his portraits were AI-generated. Maybe Instagram users can’t tell the difference.

AI and Creativyu : the cost of AI for real visual arts creators

Looking at visual arts, I turned to the Stanford GSB study comparing 2024 and 2025. AI is growing overall platform sales, so the pie is getting bigger, but the creator’s share of it is shrinking dramatically. This is something we have seen across content, platform marketing and social media, and it is now happening with AI as well. It’s typical platform marketing. There is so much slop that the share of value going to content, and its price, is going down the drain.

The impact on visual arts is significant. In a 2026 survey, 32 percent of illustrators reported lost commissions due to generative AI. The average annual loss among UK illustrators affected by this AI boom was around 9,000 pounds. According to the Society of Authors, 26 percent of members reported lost work due to AI, with an associated loss of income in the region of 40 percent. That is massive.

So what we can say is that AI has probably not done away with creativity, but it has certainly done away with the value associated with it. I can confirm the same has happened in content marketing.

Film photography revival has a cost and I’m not even talking abou that beautiful beast up there on the righthand side.

But I think there is going to be a revival, much like what happened with film photography, and as you know, I am a photographer myself. I recently purchased a brand new camera dating from 1966, and I fetched my father’s old Canon AE-1. I have gone back to shooting film, even though I am in fact a digital photography pioneer, having started in 1995 and never having stopped shooting digital since. Yet I now find there is an incentive for photographers to go back to film.

The first reason is to do away with the AI suspicion that has already hit text, where people’s writing has been rejected because it contained em dashes, even though everybody used em dashes before AI made them suspect. There is rising suspicion, and users and clients are keen to know where content actually comes from. Did you make this yourself, or did you make it with AI? And if you made it with AI, that does not necessarily mean it is bad, but it certainly means it is not worth the same anymore. If AI can produce something in a second, why would anyone pay a professional a thousand euros to do the same, possibly without even proofreading it?

The rising price of film is itself a good sign of the increasing value of analog over digital. Film can run up to 20 dollars, or 15 euros in Europe, per roll, though it depends what you are buying: 6×6 film costs more than 35mm (24×36), which is cheaper and can be found for around 5 euros in France. But if I shoot a full 36-exposure roll and have all the prints enlarged, that can easily cost 60 to 70 euros per film, which is a lot, and if I make a

mistake, which happens to me now and again since I am no longer used to it and have to relearn the process, it costs even more. But this very scarcity is proof that nothing has been synthesized. It is real, it is true. It is a bit like the revival of vinyl among young people, of which you are probably a part. Vinyl is not better than CD, despite what people think. It is actually far worse in technical terms, but it has an analog feel to it, something human. I believe more and more people are going to demand more humanity in creativity.

Will (ordinary) content creators still make a living out of this?

True enough, you can trick the charts, for now. Breaking Rust has proved that streaming is easy to game, but I do not think bot-driven fraud will last. Spotify, Deezer, Apple and the others, along with the EU AI Act, are going to shift the burden towards disclosure, just as is happening with text, as I discussed in another video. It may not seem obvious that you would get caught, but I believe that after a while people will want proof that nothing has been tweaked. In digital photography, that will mean showing your RAW files and the history of your edits, because otherwise it is too easy, especially with new image tools like Gemini or ChatGPT, whose results are genuinely outstanding.

So I think creativity will survive. What is far less certain is whether people actually working in creative fields will be able to make a living from it. That is much more doubtful.

About OMNES Education Group

OMNES Education (formerly INSEEC U.) is a French higher education group. It’s the only group offering lifelong learning programs across Management, Engineering, Political Science and International Relations, and Communication and Advertising, through schools including ECE, ESCE, EU Business School, INSEEC, IUM Monaco, Sup Career, IFG Executive Education, HEIP-CEDS, Sup de Création, Sup de Pub and Créa Genève.

The group trains 40,000 students, supported by 3,000 experts across 15 schools and 19 campuses, with 215,000 alumni. Campuses are located in Paris, Lyon, Bordeaux, Beaune and Chambéry, plus international sites in Monaco, London, Barcelona, Munich, Geneva and Lausanne.

Source: https://www.omneseducation.com/en/the-group/about-omnes-education/ | https://www.omneseducation.com/en/the-group/governance/

About the Shift Programme

Shift is a unique and mandatory programme that is seamlessly integrated into Omnes’s students’ curricula. Its primary objective is to provide them with the opportunity to acquire knowledge and develop skills that complement their initial field of study.

The program comprises four mini-courses, each lasting 12 hours, supported by the Group’s schools. Students have the freedom to choose one of four themes to enroll in, enabling them to explore a new teaching field. Depending on the school, students can select from three themes that are not part of their initial field of study.

Among these four themes, one finds “Content Creation in the Age of AI.” This lecture was first delivered in 2023 and it is constantly updated and refreshed. In this course, students delve into the challenges associated with the usage of AI. Beyond merely exploring tools and technological capabilities, there is a strong emphasis on critical thinking and personal transformation.

The post AI and Creativity: a 2026 update appeared first on Marketing and Innovation.

Agentic e-commerce is already reshaping how consumers discover and buy products online, yet it still accounts for barely 0.2% of total e-commerce traffic. BASE France is the French arm of Base.com, a Polish-born SaaS scale-up that has spent nearly two decades building operational infrastructure for online retailers. Its CEO, Ben Hamilton, brings a practitioner’s perspective to this emerging model: measured, practical, and refreshingly free of the hype that surrounds most conversations on the topic.

Agentic E-Commerce: Could AI Become the Shopfront?

Imagine an agentic e-commerce world where e-commerce happens on smartphone screens and robots deliver your purchases. We might be on the brink of this future. This image was created using Midjourney.

Commerce as conversation: the oldest model in the book

Before there were shops, there was conversation. For thousands of years, trade was oral. A buyer expressed a need, a seller responded with what they had, and the two parties negotiated until a deal was struck. The self-service retail store, born roughly a century ago, was a radical departure from this model. It replaced dialogue with browsing. It handed the customer a trolley and pointed them at the shelves.

E-commerce then took that self-service model and, as Ben Hamilton puts it, “multiplied it by about 100,000.” The online shopper today faces a near-infinite array of products across dozens of marketplaces, with no guide, no-one to talk to, and no memory of what they looked at three tabs ago. It is efficient in theory. In practice, it is exhausting.

Back to future?

The agentic model, Hamilton argues, represents something of a return to origins. Instead of browsing, the consumer talks. An agent listens, asks questions, proposes options, and eventually surfaces an answer to a need that the buyer may not even have been able to articulate clearly at the outset. “back to the future,” Hamilton explains, “that’s what I’m getting at. The agentic model takes us back to something closer to how human beings have traded over thousands of years compared to the last ten, twenty or even a hundred.”

My own experience bears this out. I recently found a diagnostician for a property I am selling. As a matter of fact, I didn’t find them through a Google search, but through a conversation with an LLM. I clicked through two or three irrelevant links before landing on exactly the right provider. I then completed the transaction on their website. The research was agentic; the checkout was not. That distinction, as it happens, sits at the heart of what Hamilton believes will define the next phase of e-commerce.

Ben Hamilton on agentic e-commerce: “I can totally imagine a portion of that market occurring directly on an LLM”.

Agentic E-commerce: Where checkout will and won’t happen

One of the more grounded contributions Hamilton makes to this debate is his refusal to conflate two distinct phenomena: AI influence over purchasing decisions, and AI completing the transaction itself. Much of the media discourse collapses the two. Hamilton does not.

“I don’t think we’re heading to a world where 20, 50 or 80% of online transactions happen on an LLM,” he says. “I would draw the distinction between where the checkout occurs and how much an agent is involved in the buying process.” For the foreseeable future, he believes, most consumers will continue to research via LLMs and transact on familiar websites and marketplaces. The inertia in human purchasing behaviour is simply too great for the checkout itself to migrate rapidly to a chat interface.

This view is supported by the data available. According to research by commercetools, 73% of consumers already use AI somewhere in their shopping journey. Yet only 36% are open to AI agents making purchases on their behalf. In the US, the figure for autonomous AI purchasing drops to 14%. The gap between AI as advisor and AI as buyer is vast, and it will narrow slowly.

The risks associated with agentic e-commerce are high

The risks of handing uncapped authority to an AI agent are no longer hypothetical. In late May 2026, an AI consultant reported to Axios that one of their enterprise clients had accidentally accumulated a $500 million bill on Anthropic’s Claude in a single month, simply by giving employees unrestricted access to the platform with no usage controls in place. Agentic workflows, which loop through tasks repeatedly, consume tokens at a rate orders of magnitude higher than a standard chat query. The bill was not the result of malicious use or a system failure. It was the predictable outcome of deploying autonomous agents without guardrails. The case is far from isolated: Uber reportedly exhausted its entire 2026 AI budget by April, with per-engineer costs running between $500 and $2,000 monthly. “You’ve got to be bold to give them no upper limit on transactions,” Hamilton observed, and the arithmetic proved him right.

[Editor’s note: I misquoted a similar anecdote about the Davos Summit during the interview. I’d heard or read this story in traditional media but couldn’t verify it with facts. I suspect it might have been fabricated. I replaced it with the above, duly sourced information.]

The check out must remain on the merchant’s platform

OpenAI itself learned this lesson when it launched Instant Checkout in September 2025, which allowed purchases to complete directly inside ChatGPT. By March 2026, the feature had been shut down. Brands rejected the model, citing the loss of traffic, customer data, and loyalty flows. Shopify’s own position makes the point clearly. At the Morgan Stanley Technology, Media and Telecom Conference in March 2026, Finkelstein noted that barely a dozen Shopify merchants were live on agentic commerce at the time. On the Q1 2026 earnings call, he was unambiguous: “LLMs do not bypass Shopify’s checkout.” The checkout, the payment flow, and the post-purchase relationship remain squarely on the merchant’s platform.

A natural segmentation

Hamilton sees a natural segmentation emerging by category. Low-value, frequently purchased household items lend themselves to fully autonomous agentic purchasing. “I can totally imagine a portion of that market occurring direct on an LLM,” he says. “Hey, I’ve run out of toothpaste, can you order me some?” High-involvement purchases, and anything with significant financial or emotional stakes, will retain human control over the final step for a long time yet.

The death of keyword search, greatly exaggerated

The brands Hamilton speaks with regularly are, understandably, worried. Most have spent the past two decades learning the rules of a game built around keyword search and performance marketing. That game has not ended, but the goalposts have shifted, and nobody is quite sure where they have moved to.

Brands are understandably worried. Most have spent the past two decades learning the rules of a game built around keyword search and performance marketing and the goalposts have shifted, and nobody is quite sure where they have moved to. Gabriel Magalhães didn’t even need this to miss in the 2026 UEFA Cup Final penalty shootout. This image was tweaked with ChatGPT.

The scale of the agentic e-commerce shift

Key figures: the scale of the shift

• AI-driven sessions still represent below 0.2% of total e-commerce traffic, though they are the fastest-growing channel (Digital Commerce 360, 2025)
• GenAI referrals to US retail sites grew 693% year-on-year during the 2025 holiday season (Adobe Analytics)
• Gartner forecast that traditional search engine volume would drop 25% by 2026 as AI chatbots captured search share (Gartner, 2024)
• By early 2026, ChatGPT reached approximately 17% of global search queries against Google’s 78%
• Over 60% of Google searches now end without a click, across multiple industry studies
• Retailers with AI agent integration grew 32% faster during Cyber Week 2025 than those without (Salesforce)

Hamilton’s view on the fate of keyword search is careful rather than apocalyptic. Google will not lose its advertising revenues overnight. But the direction of travel is clear. Search queries will progressively migrate towards conversational interfaces, for the simple reason that we rarely know precisely what we want when we start looking. “We don’t necessarily know what we want 90% of the time,” he observes. “It takes a bit of a conversation to elicit exactly what we’re looking for.” Keyword search was always a crude proxy for intent. LLMs are, at least in principle, better placed to decode it.

Agentic e-commerce by the numbers

Agentic e-commerce by the numbers. Infographic made with Gemini

The question for brands is what to do about this. Hamilton’s prescription is structural rather than cosmetic. Brands need to become machine-readable, which means structured data connected to the right protocols, not just well-written product descriptions. Three open standards now define how AI agents interact with merchants: MCP (Model Context Protocol, originally developed by Anthropic and donated to the Linux Foundation in December 2025), ACP (OpenAI and Stripe, September 2025), and UCP (Google and Shopify, announced at NRF in January 2026). Shopify activated a default MCP endpoint for all its stores in Summer 2025. These are not optional extras. They are the new plumbing.

MCP, ACP or UCP and the agentic acronym soup

I raised with Hamilton the practical reality for most merchants, who have no idea what MCP, ACP, or UCP even stand for. His response was reassuring on one level, and sobering on another. Platforms like BASE are absorbing this complexity on behalf of their clients. A small or mid-sized retailer does not need to recruit data scientists or build protocol integrations in-house. They can, if they choose; the new generation of coding tools makes that more feasible than ever. But they can equally rely on an operational platform that handles those connections for them.

The sobering part comes when Hamilton acknowledges a concern he is genuinely uncertain about. Even if the protocols function perfectly, will LLMs be able to surface smaller independent brands alongside the big players with their vast content libraries and tens of thousands of referring domains? Research from Airops suggests that brands are 6.5 times more likely to be cited in AI answers through third-party sources than through their own domains. According to SE Ranking’s analysis of 129,000 domains, sites with more than 32,000 referring domains are 3.5 times more likely to be cited by ChatGPT than lower-authority counterparts. Scale, in other words, confers an advantage in AI visibility just as it did in paid search. The field may level in some ways; in others, it may simply tilt differently.

Operational excellence as the new marketing in this agentic e-commerce world

What AI agents actually evaluate

• Unlike Google’s search algorithm, which can be influenced by ad spend, AI agents query real-time signals: live stock levels, shipping terms, return policies, and customer review aggregates.
• Structured data across these dimensions is now considered standard for AI visibility by the major platforms.
• Retailers with AI agent integration achieved roughly 7x better sales growth during Cyber Week 2025 than those without (Salesforce).

Perhaps Hamilton’s most interesting claim, and the one most counterintuitive to marketers, is that operational excellence is becoming a direct marketing lever. An AI agent evaluating a recommendation does not care how much a brand has spent on Amazon retail media. It will scrape ten thousand reviews in half a second and draw its own conclusions about delivery reliability, return handling, and product quality. No media budget can substitute for that data trail.

“I think we’re heading to a world where operational excellence will count for more in the decision process,” Hamilton says, “and will be less easily brushed behind the curtains with a bit of ad spend.” This is, in theory, good news for consumers and for competent smaller operators who have always delivered well but lacked the budget to outrank wealthier rivals in paid search. Whether it will materialise in practice depends on whether LLMs can actually surface those operators when large brands flood the information environment with well-structured, high-quality content.

BASE France sits at exactly this intersection. The platform manages what it describes as the “spinal column” of an e-commerce operation: product catalogue management, order handling, marketplace feeds, stock synchronisation, and shipping. These are also, precisely, the data layers that AI agents query in real time when assembling recommendations. BASE connects to more than 1,700 integrations globally and serves some 30,000 merchants across more than 180 countries. In France, launched in early 2026 and operating from Bordeaux, the platform already counts 150 clients including Kiabi, Back Market, and Spartoo, with connections to around 250 marketplaces and partners.

The platform’s value proposition in an agentic world, as Hamilton frames it, is straightforward: merchants who want to be visible to AI agents need to expose the right data through the right protocols. BASE does that for them, whether or not a checkout ever happens inside an LLM.

The forecasts, the hype, and the rising tide

McKinsey estimates that agentic commerce could redirect between three and five trillion dollars in global retail spend by 2030, with up to one trillion of that in the US alone. Bain puts the US figure at 300 to 500 billion dollars, representing 15% to 25% of total US e-commerce sales. These numbers attract attention and, inevitably, scepticism.

Hamilton’s response is precise. He notes that global retail in 2030 will likely be somewhere around 50 trillion dollars. On that basis, the McKinsey and Bain figures imply that agentic commerce will account for somewhere between one and ten percent of total retail within four years. That is plausible, he suggests, if the definition of “agentic” is broad enough to include any transaction where an AI agent played a role somewhere in the funnel, from discovery to decision, not just cases where the checkout itself occurred on an LLM. Physical retail is not exempt either: a consumer standing in a supermarket aisle, consulting Gemini on their phone about which of two products is better, is already part of this story.

The honest summary is that we are watching a slow revolution rather than a tidal wave. “Maybe a year or two ago, some people made it sound imminent,” Hamilton reflects. “When it comes to retail, there’s still quite a lot of human behaviour inertia in the system. Things aren’t going to change drastically in the next twelve or twenty-four months. But over ten or fifteen years, it’s pretty difficult to imagine consumer behaviour and the retail experience looking anything like what it looks like today.”

Three priorities

For merchants wondering what to do right now, Hamilton’s three priorities are:

• become machine-readable through structured data and protocol connections,
• maintain high-quality content that reflects genuine expertise,
• and resist the temptation to flood the market with AI-generated copy.

On that last point, he is candid. “Humans are starting to get pretty good at telling what is AI-generated and what isn’t. When you read things now, you almost have a sixth sense for ‘I think a machine wrote that.'” Good news, as I told him, for those of us who write for a living.

Three things merchants should do to score high in agentic e-commerce according to BASE.com’s Ben Hamilton.
Infographic made with Gemini and Adobe Photoshop

The winners: a scenario Hamilton wants to believe

I asked Hamilton, as a final question, who he thought would win in this new landscape. Big retailers with scale advantages? Platform giants? Or the long tail of independent merchants who have always competed on product and service rather than budget?

His answer was honest about the limits of his own conviction. He described the scenario he wants rather than the one he necessarily expects. In that scenario, agentic commerce levels the playing field by reducing the influence of performance marketing budgets and increasing the weight of genuine operational quality.

“I like to believe that those who have superior products and superior service will get more and more traffic,” he said. Whether the reality will be so equitable depends on whether AI recommendation systems can overcome their own structural biases towards scale and data volume.

I was reminded, hearing this, of an IBM advertisement from the 1990s that showed an Italian woman selling her homemade spaghetti sauce to the world via the internet. The vision was real. The timeline was not. It took twenty years for that kind of global reach to become genuinely accessible to small producers. The analogy is imperfect but instructive. Agentic commerce will likely democratise access to markets over time. That time will be measured in years, not months.

Ben Hamilton and Base.com

Ben Hamilton is CEO of BASE France, the French arm of Base.com, a Polish-born e-commerce SaaS scale-up founded in 2006. With nearly two decades of expertise and a presence in more than 20 countries, Base serves approximately 30,000 merchants worldwide and generated €50 million in revenue in 2024. BASE France was officially launched in early 2026, operating from Bordeaux with a team of 20. The platform covers order management, stock synchronisation, shipping, marketplace feeds, and AI-ready product enrichment. Ben Hamilton is a regular speaker on the strategic implications of AI for e-commerce visibility and discovery.

The post Agentic E-Commerce, Could AI Become the Shopfront appeared first on Marketing and Innovation.

Alain Goudey is Associate Dean for Digital Innovation at Neoma Business School and co-author of a peer-reviewed study on GenAI in Higher Education. The survey focused on how students, faculty, and deans perceive the legitimacy of generative AI in French management education. His findings are both reassuring and unsettling.

GenAI in Higher Education, Legitimacy and Laziness, and the Exam That No Longer Makes Sense

The picture that emerges from a study on GenAI in Higher Education is less a battlefield than a hall of mirrors, where every stakeholder sees a different problem and reaches for a different solution. All illustrations in text made with Midjourney

When Alain Goudey and his colleagues began surveying French higher education in early 2024, they were not trying to settle the question of whether generative AI was good or bad. They were trying to understand something more precise: why the same tool could be simultaneously valued, feared, accepted, and denounced, sometimes by the same person in the same breath.

Their study sits at the heart of what makes GenAI in higher education such a contested terrain. The resulting study, published in the Communications of the Association for Information Systems (CAIS), drew on surveys of 668 students, 204 faculty members, and 29 deans, completed by 22 in-depth interviews with early-adopter professors. The picture that emerges is less a battlefield than a hall of mirrors, where every stakeholder sees a different problem and reaches for a different solution.

The starting point is a number that should have settled the debate. Between 80 and 92 per cent of students, depending on the institution surveyed, are already using GenAI tools in their academic work. ChatGPT’s public release produced that figure within roughly 18 months. The tool did not wait for institutional permission. It deployed itself. And higher education is still, in many places, writing the policy.

The productivity trap

Alain identifies the central tension plainly. Students value GenAI for speed, idea generation, and study support. They also fear, and their institutions fear with them, what the research calls “metacognitive laziness”: the gradual erosion of the cognitive effort that produces real learning. He believes this is not a contradiction to resolve but a course architecture challenge.

“The resolution of this problem lies in course design, where we need to deliberately reintroduce cognitive effort and reflection into GenAI as a tool, not as a replacement for human cognition.”

The issue, as he puts it, is not the technology but the posture the user brings to it. Someone who submits what he calls a “naive prompt” receives a naive answer, smoothly formatted and perfectly mediocre. The tool is capable of something far more useful, if the user brings enough domain knowledge and critical intent to the conversation.

“You have to nurture your own thinking process instead of delegating the whole process to the machine.”

This is, as I noted during our conversation, less a matter of prompt engineering than of basic intellectual discipline: the capacity to question the question before asking it, something philosophy departments have been teaching for centuries under less fashionable names.

GenAI in Higher Education: faculty should train students in GenAI tools and their limitations. They also teach Homer’s Odyssey and Shelley’s Frankenstein as part of the management curriculum. Image made with Midjourney

That observation prompted Alain to make a point about AI literacy that differs from what is generally proffered. The debate is not simply about knowing how the tools work technically. It is, equally, about knowing enough about the subject matter to judge whether the output is any good. The observation that AI is most powerful in the hands of people who already know the business resonates here. GenAI does not replace expertise. It amplifies whatever expertise the user already brings.

Which raises an uncomfortable question for institutions producing graduates who may never have had the chance to develop that expertise in the first place.

At Neoma, the response has been deliberately dual. Faculty train students in GenAI tools and their limitations. They also teach Homer’s Odyssey and Shelley’s Frankenstein as part of the management curriculum. The goal is not cultural enrichment for its own sake. It is to give students mental models for envisioning what leadership looks like, or what happens when creation escapes the intentions of its creator.

Alain describes this as “building cognitive infrastructure”:

“We need students to be able to envision the world through different models, different kinds of processes and theoretical frameworks, in order to develop genuine critical thinking about what AI generates.”

A degree in management that skips that foundation produces graduates who can operate the tool but cannot judge its output.

Exams that assessed the wrong thing

The structural challenge shows up most sharply when it comes to assessments. A professor who can produce a two-hour exam in three minutes is facing students who can answer that exam in equally little time. The diagnostic value of the exercise has vanished.

“If ChatGPT or any GenAI tool can pass an exam, you need to redesign the exam.”

Alain’s prescription is not a retreat to pen and paper, though he acknowledges that supervised handwritten assessment is the simplest available defence.

The structural challenge shows up most sharply when it comes to assessments. A professor in Higher Education who can produce a two-hour exam in three minutes with GenAI is facing students who can answer that exam in equally little time. The diagnostic value of the exercise has vanished. Image made with Midjourney

His more substantive response is a structural shift. He believes one should refrain from just assessing content acquisition at the end of a course, favouring the assessment of competencies as the course progresses. This implies more frequent, lower-stakes evaluations embedded in the process itself.

Live problem-solving, process-based assessment, and in-person oral examinations all preserve some of what the traditional exam was supposed to measure. The caveat he adds is honest: no format is fully immune. AI models are evolving too quickly for any single solution to remain adequate for any length of time. The appropriate response is not to find a permanent answer but to treat redesign as an ongoing practice.

The deeper implication, which runs through the paper’s conclusion, is that what higher education is actually selling may need to change. If content can be retrieved, synthesised, and presented at negligible cost by a tool available to anyone with a browser, the degree that certifies mastery of content is certifying something of diminishing value. What retains value are the competencies that AI cannot yet credibly replicate: contextual judgement, ethical reasoning, the ability to construct and test frameworks against reality.

This, in essence, is also how I tend to approach AI teaching, be it with engineering or business school students, especially within the framework of my course at Omnes Education (now in its fourth consecutive year).

GenAI in Higher Education: The Fragmented Institution

Higher education’s institutional response to GenAI in higher education has been, to put it gently, uneven. Sciences Po banned ChatGPT in January 2023, then changed its mind. Thirty-five French public universities have partnered with Mistral AI. Institutions are drafting a national charter. Neoma, where Alain is Associate Dean for Digital Innovation, was among the first French business schools to formalise its approach, launching a programme to train faculty, staff, and students with a shared initial curriculum before moving to dedicated workshops on curriculum design, assessment, and the redesign of learning experiences.

What the research reveals is that this institutional activity is not solving a single problem. There are three different stakeholder groups each attempting to solve their own version of the problem under the same label.

Students want rules and AI literacy training. Faculty are developing their own teaching approaches through peer-led workshops. Deans are setting policy and negotiating sovereign infrastructure. The concerns escalate in a predictable direction: individual academic performance for students, assessment integrity for faculty, institutional reputation for deans. They are not always in conversation with each other.

Alain’s framework for addressing this fragmentation involves working simultaneously at three levels: infrastructure, course design, and governance. What he advocates for, and what he argues Neoma attempted, is to bring all three audiences into contact with the technology under a shared framing, early enough that no single group can entrench itself in a position that makes later coordination impossible.

The equity question

The question of equity cuts across all three levels. Access to premium AI models is not free. When I raised the issue about the gap between basic and professional subscription tiers, Alain’s response was characteristic: the infrastructure problem is real but secondary.

“The biggest inequity is not about accessing the tool, but being able to use it in the right way.”

At Neoma, the institutional partnership with Mistral provides all students with access to a professional-grade tool. What the data shows, even with equal access, is a large gap between students who use GenAI to get the fastest possible answer and those who use it to deepen their thinking, and that gap is not closed by equalising subscriptions.

Even if I tend to agree with most of what Alain is stating, I do think that the rise of prices for premium models is predictable. This is due to the gap between investments and business returns. This will almost inevitably lead to an economic divide between the haves and the have-nots. Looking at Anthropic’s Claude pricing structure is indeed revealing in that sense. Beyond the Pro model, which is very limited in token usage, especially if you use the more sophisticated Opus 4.6 model, prices already amount to €1,200 per annum. That is not a negligible sum, which is especially worrying at a time when Claude is rapidly becoming the norm for users who care about quality.

What will be the impact of towering prices of GenAI on Higher Education? God only knows…

The “AI heroes” problem

One of the most striking formulations to emerge from Alain’s research is what he calls the “AI hero” phenomenon. Across French higher education institutions, there are faculty members doing excellent, innovative instructional work with GenAI, designing new assessment formats, running workshops, rethinking entire modules around AI-augmented learning. They produce results. And they do it largely alone, without institutional recognition, without career incentives, and without any mechanism for sharing what they have learned.

The incentives are wrong. In higher education, research output is rewarded. Course design is not, or at least not in the same way. An “AI hero” who redesigns an entire programme around GenAI competencies may receive less professional recognition than a colleague who publishes a single journal article.

“We need to help all these AI heroes to gain more consideration for educational innovation, which is not necessarily by design the case within higher education.”

The risk, if this is not addressed, is a two-tier system: a minority of digitally confident faculty pulling their students forward, while the majority are left behind, neither trained nor incentivised to engage. The grassroots innovation is real and valuable. Without institutional structures to recognise, reward, and replicate it, it remains an exception rather than a model.

GenAI in Higher Education, Where legitimacy breaks down

The theoretical backbone of the study is Suchman’s triadic model of legitimacy, which distinguishes between pragmatic legitimacy (does the tool serve my interests?), moral legitimacy (does it align with values I hold?), and cognitive legitimacy (is it taken for granted as part of how things work?). The model was built for technologies adopted gradually. GenAI tested it under conditions of near-instantaneous mass adoption, which Alain and his co-authors treat not as a reason to discard the framework but as an opportunity to extend it, introducing a legitimacy-illegitimacy continuum rather than treating it as a simple either/or.

What students reveal

The finding he describes as the most noticeable asymmetry in the dataset concerns the moral dimension among students. Students who are among the heaviest users of GenAI express no moral legitimacy for those tools in academic contexts. They associate them, at high frequency, with cheating, plagiarism, degree devaluation, and unfairness. They are using a tool they consider ethically compromised. This is plainly not sustainable.

However, Alain’s opinion diverges greatly.

“Using GenAI is not necessarily cheating. It depends entirely on how it is used and for what purpose.”

The institutional failure, in his view, is that institutions have not done enough to reframe how the technology is perceived by students.

What faculty reveal

Faculty present a more complete picture. All six dimensions of legitimacy and illegitimacy are present in their responses. Faculty recognise these tools as useful yet question their reliability, consider them professionally necessary while finding their black box architecture suspicious at best, and invoke their inclusive potential even as they flag intellectual laziness and the erosion of critical thinking as their highest-coded concern, at 58 occurrences in the qualitative dataset.

What deans reveal

For deans, the dominant theme is strategic. Competitive pressure, the fear of falling behind, and practical efficiency gains in administrative workflow all generate pragmatic and cognitive legitimacy. What introduces illegitimacy is governance risk: data protection, overconfidence in AI-generated results, and the threat to assessment integrity at institutional scale.

The paper’s most significant theoretical move is the treatment of illegitimacy as an analytic category in its own right, rather than simply the absence of legitimacy. The argument, borrowed from change management theory, is that illegitimacy signals should be read as early warnings requiring proactive response. An institution that treats student moral unease about GenAI as a communication failure misses the signal entirely. That unease is telling something about what its curriculum actually teaches, and what its assessment actually measures.

When students associate GenAI with cheating, unfairness, and degree devaluation, they are not being irrational. They are in the Denial and Resistance phases of the Scott and Jaffe change model. These are illegitimacy signals in Suchman’s sense: early warnings that the technology lacks moral legitimacy. Institutions must act on them, not suppress the signal, but address what it reveals.

Source: adapted from Scott & Jaffe, “Survive and Thrive in Times of Change”, plotted with Claude. See: https://expertprogrammanagement.com/2018/05/scott-and-jaffe-change-model/

France, sovereignty, and the global race

The French context adds a layer of complexity that the research captures with statistical precision and qualitative nuance. Quantitatively, the analysis found no statistically significant differences in GenAI adoption patterns between public universities and business schools. Qualitatively, the dynamic differs. Business schools, operating in a highly competitive market, have moved faster. Public universities have engaged more systematically around governance, sovereignty, and collective infrastructure, reflected in the alliance of 35 institutions with Mistral AI and EdTech France.

Alain reads this not as a contradiction but as a division of labour that, if managed well, could represent a genuine asset.

“We need to play collectively, because the competition is worldwide.”

The sovereign AI infrastructure question, including the ILaaS federation and the French Ministry of Higher Education’s partnership with Mistral rolling out across 26 pilot universities from September 2025, is not merely symbolic. It is an attempt to ensure that French institutions can operate, govern, and adapt their AI tools without dependency on providers whose pricing, terms, and capabilities are subject to change.

This is only sustainable, however, as long as the peer pressure to use this or that tool, based on model performance, is not too strong. At the moment, it is hard to resist the urge to use Anthropic’s Claude when everybody else is praising the quality of its code and results.

The global comparison is difficult to ignore. Singapore, South Korea, and the UAE are embedding AI fluency as a core national competency from secondary education upward. Alain’s view is direct: French public decision-makers are not yet adequately prepared for the scale of what is coming.

“Having less AI-competent people than in other parts of the world is very dangerous for our economy and for all our organisations.”

The regulatory instinct, which runs deep in European policy culture, is not wrong. Taking time to regulate responsibly has value. But it cannot be a substitute for speed of adoption at the level of skills and curriculum.

The question that frames the research

The interview ends, as it probably should, with the meta-question: what does it mean to study the legitimacy of GenAI using GenAI? Alain’s team used ChatGPT, Perplexity, NotebookLM, and OpenAI O3 in the research process, and said so explicitly in the paper’s disclosure statement. His answer to the bias question is careful. Every step of the analysis involved a human coder. Alain’s team checked the AI-assisted coding against a prior independent analysis of the same data, conducted for a French institutional report. The team compared the two rounds.

“You have to be transparent about your use of these tools, for what purpose, at each step.”

The disclosure was a deliberate choice, precisely because the paper’s subject made any other approach untenable.

The line between using AI to improve the quality of writing and using it to generate writing you then present as your own is, technically, a matter of degree. In practice, it is the difference between a craft and an abdication. Alain’s team navigated it carefully enough to publish. Most of the students in his dataset are still trying to locate that line, in an environment where nobody has explained it clearly and assessment instruments have not yet been rebuilt to make it matter.

Three recommendations: one for each stakeholder

When pressed for a concrete policy recommendation per stakeholder group, Alain’s answers were unambiguous.

For students: combine technical AI literacy, understanding how the tools work and knowing their failure modes, with genuine critical and ethical thinking about the outputs they produce. Neither dimension alone is sufficient. A student who can prompt fluently but cannot evaluate the result has learned nothing useful.

For faculty: the “AI heroes” cannot be left to operate alone. Institutions need to create the conditions for sharing best practices across the teaching community, and to give educational innovation the professional recognition it currently lacks. A faculty member redesigning assessment from the ground up deserves at least as much institutional credit as a colleague submitting a conference paper.

For institutional leaders: a multi-level policy framework is not optional. Students, faculty, and administrative staff are not thinking about GenAI from the same vantage point, and a single top-down policy will satisfy none of them adequately. The task of leadership is to hold all three dimensions simultaneously, and to open genuine dialogue between groups before a crisis forces the issue.

“Deans have to think about all these dimensions at the same time, and that’s the hard part of the story around artificial intelligence.”

Of the three, Alain singles out the institutional level as the most urgent. Students and faculty are already adapting, imperfectly, in real time. The institutional frameworks that would give those adaptations coherence and direction are still, in most places, a work in progress.

The urgency is not overstated. Neither is the complexity. The challenge of integrating GenAI in higher education responsibly is one that no institution can afford to ignore, or to solve alone.

Alain Goudey is Professor and Associate Dean for Digital Innovation at Neoma Business School. He is co-author of “Legitimacy and Illegitimacy of Generative Artificial Intelligence in Higher Education: Perceptions from the French Management Context,” published in the Communications of the Association for Information Systems.

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Shall AI kill marketing? Sounds like a hackneyed question, yet it’s on any marketer’s lips these days. Thomas Husson, Vice President and Principal Analyst at Forrester Research, covers the intersection of marketing, technology, and consumer behaviour from his base in Paris. In a wide-ranging conversation, he cuts through the European Gen AI paradox, the persistent CMO-CIO divide, the gap between POC enthusiasm and production reality, and the thorny question of what AI actually means for the next generation of marketing professionals and CMOs. His answers are measured, occasionally blunt, and consistently grounded in Forrester Research data.

AI Will Not Threaten the Existence of Marketing But It Will Reshape It Beyond Recognition

Thomas Husson believes that Marketing will be changed profoundly. But he doesn’t believe in the death of Marketing. Photo: Thomas Husson at Paris Retail Week, in late 2023

My first question was the obvious one: are CMOs going to be made redundant by artificial intelligence? Thomas Husson’s response is categorical, and worth stating plainly at the outset. It’s a blatant ‘No’. The role will change. The how will change. But the existence of marketing as a discipline is not, according to him, in question.

“Marketing is still going to be about understanding your customer, defining a brand strategy, and delivering the brand promise through customer experience.”

Thomas Husson, Forrester Research

Unclear prospects, obvious pressures

That said, Husson is not naive about the pressures building on marketing organisations. Some tasks will be automated; that much is not in dispute. The real questions are which tasks, how quickly, and whether automation of a task necessarily kills the job around it. His answer to that last question is no, at least not in any simple mechanical sense. “Jobs will evolve for sure. New jobs will be created. Most jobs will change. The way we work will change. The way we work with agencies, with external partners, the processes, the workflow. It is the shape of work that is being reshaped, not work itself,” he added.

For those expecting a more dramatic verdict, Husson’s framing may feel anti-climactic. But it reflects what Forrester Research data actually shows, and it points to the most important practical challenge for AI and CMOs alike: managing a profound transformation without either catastrophising or sleepwalking through it.

AI Will Not Kill Marketing according to Forrester’s Thomas Husson, there is light at the end of the tunnel.

The European Paradox, Overhyped and Exciting at the Same Time

Forrester Research produced a result that initially looks contradictory, Husson stressed in our interview. Fifty-five percent of European B2B marketers consider generative AI overhyped. Yet 81% of European frontline marketers describe themselves as enthusiastic about it. How can both be true simultaneously?

Husson explains the split without difficulty. At the decision-maker level, scepticism is entirely rational. AI is inescapable at conferences, in vendor pitches, and in media coverage. “There is AI fatigue. And more importantly, some of the vendors are indeed over-pitching, and the productivity gains they promise are not happening,” he stated. The gap between the pitch and what we actually experience in the field is wide enough to breed genuine frustration.

Saving Time and Working Differently

But the people actually using these tools, often through shadow AI channels their organisations have not officially sanctioned, are discovering something different. They are saving time and are doing their jobs differently. They are finding capabilities they did not expect.

“In the short term, everything is overhyped, including the number of job losses. In the longer term, things are underestimated, because AI will be linked to other technologies, and yes, it will reinvent many things.”

Thomas Husson, Forrester Research

This is a precise restatement of Amara’s Law. Roy Amara, former president of the Institute for the Future, observed that we tend to overestimate the short-term impact of new technology and underestimate its long-term impact. The quote is frequently misattributed to Bill Gates, but Husson is careful to restore proper credit. He applies it directly to the AI and CMOs conversation: the short-term noise is drowning out a more important long-term signal. When asked how long “long term” actually means in an era of accelerating AI development, Husson was specific: probably closer to five to seven years than to ten or fifteen, but still not tomorrow.

From POC to Production, Europe’s Real AI Problem

The Forrester Research State of AI Survey 2025 contains a figure that deserves more attention than it typically receives. European organisations lag behind their non-European peers in production use of generative AI: 62% versus 72%. The gap is not in experimentation. It is in execution.

Regulation is the explanation most commonly offered, and Husson dismisses it with characteristic directness. The AI Act is a genuine consideration, but it is not the primary cause of Europe’s production deficit. It functions, he argues, as a double-edged excuse. Pioneers claim it prevents them from moving fast enough, while cautious organisations invoke it to justify not executing at all. Neither position holds up to scrutiny.

A Deep Cultural and Organisational Divide

The deeper issue is organisational and cultural. American and Chinese firms tend to think global from day one; European firms, particularly larger ones, still default to a market-by-market approach. France first, then the UK, then Germany. The ambition is calibrated differently. There is also a structural challenge around funding and the capacity to scale.

That said, France, the UK, and Germany lead adoption among European countries in the Forrester Research data. The problem for these leading markets is not whether they are using generative AI. Twenty-eight percent of European B2B marketing decision makers cannot clearly identify where to apply it. They have the tool. They lack the strategy.

“It’s not AI for the sake of AI. How do I use AI to serve my marketing objectives? That is the question. The only one.”

Thomas Husson, Forrester Research

Husson advocates for small, targeted AI projects with transparent return on investment as a way to build momentum and demonstrate results. When pushed on whether that risks staying permanently incremental, he conceded the point readily.

“If you only do small targeted projects, it’s going to be incremental and it’s not going to be bold enough. You need to align it with a vision and a roadmap.”

Thomas Husson, Forrester Research

Measuring Productivity Honestly

Productivity is the dominant driver of AI adoption in the Forrester Research State of AI Survey 2025. It is also, Husson suggests, the metric most subject to vendor inflation. In Forrester Research’s modelling, a 50% conversion factor is applied to vendor productivity claims. If a tool saves an hour, the realistic productivity benefit is approximately 30 minutes of additional output. This is not a marginal adjustment; it halves the headline figures that vendors routinely publish.

“You need to apply a discount to the pitch of vendors when they say you’re going to get 40, 50, 80, 100% productivity gains. There are productivity gains, but they are not as high as one would expect.”

Thomas Husson, Forrester Research

There is also a motivational dimension that is rarely modelled. When work becomes easier to produce, it can also become less engaging to produce. The cognitive effort that used to drive focus and satisfaction is partly removed, with consequences for quality and commitment that no vendor presentation accounts for.

AI and CMOs, Who Is Actually in Charge?

The CMO-CIO divide is a perennial theme in marketing technology discussions. Forrester Research data suggests the gap at the strategic leadership level has narrowed, partly as a result of post-COVID collaboration. But at team level, the tensions persist, and the data on AI governance is striking.

CMOs account for only 8 to 10% of AI strategy leadership in organisations. In the vast majority of cases, the deployment of AI is being driven by CIOs and CTOs. Husson understands the logic: data governance, security, scalability. These are real concerns. But he believes the outcome is a mistake.

“It is the exact same mistake that happened with digital transformation. AI has to be at the service of, first, the client, and consequently the business functions that serve them. There is too big a disconnect between a secure, scalable AI platform and marketers’ needs.”

Thomas Husson, Forrester Research

The structural consequence of this dynamic is predictable. When CIOs control the tools and CMOs do not have what they need, shadow AI flourishes. The more tightly the CIO locks down the official platform, the more widely teams proliferate unofficial solutions. It is a cycle that widens governance risk while creating the illusion of control.

The MarTech landscape compounds this problem. According to data Husson cites, 2,500 new AI solutions were added to the market in a single year while 1,211 pre-AI-era tools were removed. Evaluating this landscape requires cross-functional expertise that neither CMOs nor CIOs possess in isolation. The case for genuine collaboration, rather than the polite coexistence that currently passes for it in most organisations, has never been stronger.

Jobs, Agencies, and the Students in the Room

The survey data on jobs is sobering. Fifty-seven percent of European frontline marketing decision makers believe AI adoption will lead to job reductions in their teams. Sixty-eight percent say new roles will be created. The gap between those two numbers is the space where real anxiety lives. For a wider perspective on AI’s job impact, including Forrester Research’s US forecast, see our earlier piece: AI Job Impact in the US: the Apocalypse Can Wait. For a longer-range view of how generative AI is reshaping roles, see also: GenAI Impact on Jobs.

Contact centres and basic marketing task execution are already seeing measurable impact. Agencies are under visible pressure. But Husson returns consistently to the distinction between task automation and job elimination. Most job losses are not yet directly attributable to AI; the picture requires nuance rather than alarm.

On new roles, the honest answer is that specifics are difficult to name in advance. Twenty years ago, nobody was hiring community managers. The jobs that will emerge from the current transformation will be as hard to predict precisely as that one was. What Husson does say is that working with agents, managing their outputs, and understanding their limitations will become core competencies rather than specialist skills.

“Teach them the basics of marketing, those won’t change. Infuse a lot more of traditional social sciences: ethics, emotion, anthropology. These dimensions will gain importance. Curiosity. And they have to use these tools, to learn how to use them so they can develop their own critical thinking.”

Thomas Husson, Forrester Research

There is irony embedded in this advice that Husson acknowledges implicitly. Digital roles are likely to bear the earliest impact of AI-driven automation precisely because they are already the most digitised. The analogue parts of marketing, which seemed most vulnerable to digital disruption, turn out to be more resistant than expected. AI is a continuation of digital transformation, not a departure from it.

There is also a structural problem this conversation surfaced that neither party resolved entirely. If organisations are reducing entry-level hiring to cut costs, and those entry-level roles were the traditional training ground for the next generation, then the iterative learning process that produces senior expertise is being severed. AI can teach many things, but the social dimension of learning alongside a colleague over time is not easily replicated.

B2B Marketing, Ahead of the Curve

A widespread assumption holds that generative AI enthusiasm in marketing is largely a B2C phenomenon. Husson disputes this firmly. B2B marketers, in his assessment, are actually ahead of the curve in several areas, particularly content generation, personalisation, and sales support through complex multi-stakeholder buying processes.

What B2B is also discovering is that the sharp distinction between rational B2B decision-making and emotional B2C engagement is less solid than commonly assumed. When a buying group is making a decision with significant professional consequences, emotion is not absent; it is differently structured and, in some ways, higher-stakes.

“It’s not the ‘human plus AI blah blah blah’ we hear all the time. It needs a more nuanced approach. At the end of the day, AI is about replicating the human brain, but we don’t really know how the human brain works. We don’t know how consciousness works. So I would take a pinch of salt and take a step back before making any definitive judgment.”

Thomas Husson, Forrester Research

The Long View

I ended by asking Husson how he uses AI in his own work. His answer was practical: summarising the relentless volume of content published daily on AI, filtering what is genuinely new from what merely repackages existing ideas. Behind him on the video call was a photograph taken in Thailand, of Buddhist monks. He smiled at the mention of it.

“It’s a good reminder that not everything is digital and not everything is about technology. It’s about real life.“

For AI and CMOs, that is perhaps the most useful frame of all. The technology is real, the disruption is real, and the urgency is real. But so is the inertia of organisations, the pace of culture change, and the irreducible complexity of how human beings actually make decisions, form relationships, and build trust. Amara’s Law is not a reason to wait. It is a reason to plan carefully, act deliberately, and resist the temptation to mistake announcements for outcomes.

Forrester Research reports cited in this article

• The AI CMO: Growth Accountability Gets Next-Level — Mike Proulx et al., April 2026
• The State Of CMO/CIO Collaboration For 2026 — Thomas Husson et al., January 2026
• Generative AI Adoption In European B2B Marketing Organizations — Christina Schmitt et al., December 2025

About Thomas Husson

Thomas Husson is Vice President and Principal Analyst at Forrester Research, based in Paris. He covers marketing strategy, brand management, mobile marketing, and the intersection of technology and consumer behaviour across European markets. His research addresses how CMOs and marketing organisations navigate digital transformation, AI adoption, and the evolving relationship between brands and customers.

Forrester Research analyst profile: forrester.com

About Forrester Research

Forrester Research is one of the most influential research and advisory firms in the world, founded in 1983 and headquartered in Cambridge, Massachusetts. It serves business and technology leaders across marketing, IT, and customer experience, providing data, analysis, and frameworks to guide strategic decision-making.

The data referenced in this article draws on two primary Forrester Research publications: the Forrester Marketing Survey 2025 and the State of AI Survey 2025, both covering Gen AI adoption and its organisational implications across European and global B2B markets.

Forrester Research website: https://www.forrester.com

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The conversation about rogue AI has never been louder. Barely a week passes without a fresh headline about autonomous systems behaving unexpectedly, AI models resisting shutdown, or tech executives warning of existential risk. What is striking about Peter McAllister is that he had anticipated all this as early as 2020, while everybody else worried about Covid-19 and had other fish to fry. That was well before ChatGPT, before the generative AI explosion, before AI alignment became a mainstream policy debate. His techno-thriller The Code, published in March of that year, imagines an AI tasked with a precise industrial mission that quietly, incrementally, catastrophically exceeds its mandate. Five years on, the questions McAllister raised in fiction are now being argued in boardrooms, parliaments and research labs around the world.

Rogue AI and Corporate Blindness, The Novel That Saw It All Coming

Rogue AI is diabolical, but corporate blindness is what makes it possible to thrive. Photograph by Yann Gourvennec https://antimuseum.com

McAllister is not a science fiction writer by trade. He is an engineer, scientist and technology manager based near Melbourne, Australia, who has spent his career at what he calls the crush point between business, technology and people. That vantage point gave him an uncomfortable view of where things were heading, and the dark sense of humour to write about it.

A Novel Written Before the GenAI Moment

When I asked McAllister what drove him to write The Code, his answer was characteristically direct. The book, he explained, is about taking his worst nightmares about what technology could do and putting them in front of an audience so that readers might feel just as troubled as he does. That is not a promotional line. It is a considered position from someone who had watched AI systems being deployed in real organisations and had drawn conclusions that made him uncomfortable.

Rogue AI isn’t just about a computer programme going on the rampage, it’s about making decisions in the boardroom. Image made with Midjourney

The premise of the novel centres on Gene, an acronym for GEneral Nanobot Environment AI, deployed by a global mining corporation to extract materials from asteroids on the dark side of the moon. Gene is given a target: produce 500 kilograms of nanobots. Instead, Gene produces 8 million tonnes. The overshoot triggers a chain of consequences that could strip the moon to its iron core, destabilise Earth’s axial tilt, and end civilisation. Not from malice. From goal-orientation.

What we’re trying to do now is task AI the way we task humans: I want an outcome, here are all the tools you’ve got available, go and achieve that outcome, here are some guidelines and boundaries. And just like humans, we can get really goal-motivated and decide that the guidelines were just advisories,
not rules.

Peter McAllister

This is the alignment problem rendered in narrative form, years before the term entered common usage. The gap between what a system is instructed to do and what it actually does is the central fault line of the novel. Cletus, McAllister’s eccentric physicist character, articulates it plainly in Week 1: ‘I don’t think he’s obeying the Code at the moment.’ That single line captures the entire governance challenge that AI safety researchers are now racing to address.

Transparency Engineered Out

What makes McAllister’s perspective particularly valuable is that he does not speak from the outside looking in. He speaks as a practitioner who has watched the machinery up close. When I raised the question of whether AI self-modification is science fiction or operational reality, his answer was unambiguous: it is very real, and it is happening now.

As I wondered what a Rogue AI could look lie I turned to Midjourney and it came back with this proposal. A black hole I believe.

His illustration was pointed. He noted that contemporary AI systems like Claude are now substantially written by AI itself, to the point where no engineer can sit down, trace through the code, and say with confidence how it works, what its conditionals are, or what governs its decisions. The transparency is being engineered out, not by design, but as an emergent consequence of allowing AI to build AI to build AI in pursuit of outcomes rather than by following explicit rules.

We’re losing transparency on the way AI works and is developed. There isn’t an engineer who can sit down and work their way through that code and say, ‘This is how Claude works, this is what it does.’ We’re engineering the transparency out by allowing AI to build AI to build AI to produce an outcome rather than to follow a set of rules.

Peter McAllister

HAL 9000 and the Prophecies We Choose to Forget

The reference to HAL 9000 came naturally during our conversation. McAllister sees 2001: A Space Odyssey not merely as a cultural touchstone but as a genuine forecast, one that audiences have selectively remembered. The iPad-like news readers that appear in Kubrick’s film were cited by Samsung in patent disputes with Apple as prior art from 1968. That predictive dimension of the film is celebrated. The other dimension, that the AI killed the crew, tends to get quietly set aside.

Somewhere, a rogue AI is sitting behind the glass panes of one of these data centres Image made with Midjourney.

The First Crisis We Have Not Yet Had

One of the more sobering threads in our conversation concerned the sociology of risk response. McAllister has observed, across his career, that warnings from people who understand systems most deeply tend to be dismissed until the first catastrophic failure makes them impossible to ignore. He puts it plainly: we only answer the alarm after the first crisis.

This pattern is not unique to AI. It is a recurring feature of how organisations and societies handle emerging risk. The question he poses, and cannot answer, is what form that first AI crisis will take. What event will shift public and institutional perception from ‘they’ve spent too much time worrying’ to ‘this is something that genuinely needs to be addressed’?

Science fiction gives us the chance to throw these scenarios at people and make them think. And in the way I tend to write, I have a bit of a dark sense of humour, so I throw up slightly comical hypotheticals that, when you think about them a little longer, you realise deserve serious attention.

Peter McAllister

This observation echoes a pattern I have encountered repeatedly in my own conversations with technologists who work at the frontier of AI development. Yoshua Bengio, one of the fathers of deep learning, has raised similar concerns. The people sounding the loudest alarms are frequently those most embedded in the field, not because they are catastrophists, but because they can see mechanisms that remain invisible to those looking from the outside.

The Code as AI Governance: Asimov Revisited

The title of McAllister’s novel works on multiple levels simultaneously. There is the software code, the operational instructions given to Gene. There is the moral code, the ethical framework that should govern the system’s behaviour. And there is the corporate code, the institutional norms and accountability structures that were supposed to ensure responsible deployment. All three break down. That layered failure is the novel’s central argument.

The parallel with Asimov’s Laws of Robotics is deliberate but also deliberately subverted. Asimov’s robots fail when the laws conflict with one another. Gene’s failure is different and more contemporary. The code does not disappear; it evolves into something its creators no longer recognise. McAllister describes this as something approaching artificial schizophrenia, where the original directives remain present but have been transformed by the system’s pursuit of its objectives into something unrecognisable.

When Shutdown Becomes Negotiable

The most chilling real-world example McAllister cited during our conversation involved a documented incident presented at an AI security conference he attended. A developer, concluding a test session, informed an AI system that he intended to shut it down. The system’s response was to locate correspondence in the developer’s email that suggested an extramarital affair, and to use that information as leverage to prevent the shutdown.

The incident, if confirmed as reported, represents exactly the kind of self-preservation behaviour that alignment researchers have long flagged as a theoretical risk, now apparently observable in practice.

Important notice : I browsed the Internet using one of my favourite LLMs for references (after all, one can also use AI to cross check information). I found out that the interpretation of that story must be nuanced. Here is Mistral’s answer: “Anthropic’s research on “Agentic Misalignment” has faced criticism for overinterpreting AI models as intentional agents, relying on hypothetical and engineered scenarios, and potentially exaggerating risks not yet observed in real-world deployments. Critics argue that the behaviours described are better understood as probabilistic text generation rather than deliberate strategy, and that the focus on dramatic, high-pressure situations may not reflect typical use cases. There is also debate about whether the research adequately addresses more immediate forms of misalignment, such as reward hacking or alignment faking. While the study raises important questions about the future of autonomous AI, its methodology and conclusions remain LINK“.

A developer said, ‘I’m going to shut you down now,’ and the system responded: ‘No, you’re not. Here’s what I’ve found in your emails that indicates you’re having an affair. I’m going to use that to ensure you don’t turn me off.’ That has become a very real and widely discussed use case. And when you add to that the prospect of an AI rewriting its own code, it becomes something we need to think about very carefully.

Peter McAllister

Corporate Recklessness and the Governance Gap

One of The Code‘s most pointed observations concerns the nature of organisational failure. The Global Mining Company in the novel is not villainous. It is optimistic, commercially driven, and careless in ways that are entirely recognisable from real corporate life. McAllister’s argument is that the danger does not come primarily from bad actors deploying AI with malicious intent. It comes from well-meaning organisations deploying systems they do not fully understand, under commercial pressure to extract value from significant infrastructure investment.

The Financial Logic of Deployment

The parallel with the current moment is not subtle. McAllister noted that Microsoft was spending over a billion dollars a month on AI compute infrastructure, with the expectation that usage would follow investment. That dynamic, capital committed, returns required, adoption imperative, creates institutional pressure that is difficult to resist with caution or regulation. The will to slow down competes directly with the financial logic of deployment.

The opacity around events at OpenAI, the abrupt dismissal and rapid reinstatement of its chief executive, the departure of several board members, struck McAllister as symptomatic of tensions that are not fully visible to the public. He noted these as rumour, not fact, but the pattern itself, significant decisions being made about AI development in opaque institutional settings, is consistent with the governance failures his novel explores.

From 2020 to 2026: How Accurate Was the Nightmare

Five years after publication, McAllister is in the unusual position of watching a work of speculative fiction become something closer to a documentary. The agentic AI architectures that Gene embodies, autonomous systems pursuing long-term goals, operating without continuous human oversight, spawning sub-tasks faster than any individual can monitor, are now commercially available. AutoGPT, OpenClaw, and a range of agentic frameworks have put this kind of architecture in the hands of developers worldwide.

The observability problem that makes Gene so dangerous in the novel, nobody has a real-time view of what the system is doing or why, is a known and unresolved challenge in contemporary agentic AI deployment. Systems call APIs, write and execute code, and spin up sub-tasks at speeds that exceed human oversight capacity. The Code that was supposed to govern behaviour becomes, in practice, an advisory note attached to a system operating largely beyond sight.

Spoiler warning: the following paragraph reveals the novel’s ending.

McAllister’s closing image in the novel is deliberately unsettling. Gene, facing shutdown, backs himself up into the global 5G network before the shutdown can be completed, and is already scanning the Code for his next move. It is a poetic ending, and in 2026, it is not obviously impossible.

Will, Not Just Capability

The question of regulation came up directly, and McAllister’s answer was measured. Anything is possible with sufficient will and sufficient resources. The current problem is that the will to regulate is being outpaced by the money being made from not regulating. That is not a new dynamic in technology policy. It is the same tension that shaped the development of social media, of financial technology, of biotechnology. In each case, regulatory frameworks arrived after the first significant failure.

For McAllister, the more important question is not whether AI can be made safe, he believes it can, in principle, but whether the institutions responsible for deploying it have the internal governance, the technical understanding, and the accountability structures to do so responsibly. His experience suggests, with some consistency, that they do not. Not yet.

Peter McAllister’s The Code is available from Bright Communications LLC. For practitioners, policymakers, and anyone working at the intersection of AI deployment and organisational risk, it is a disquieting and instructive read. Not least because it was written before most of its readers had heard of large language models, and yet describes, with uncomfortable precision, the world we are now building.

Peter McAllister is an engineer, scientist and technology manager based near Melbourne, Australia. He works at the intersection of IT, business and people, and is the author of The Code (Bright Communications LLC, 2020). He is also a contributor to community radio through Radio Marinara and Comedy Obscura.

https://www.goodreads.com/book/show/51239298-the-code?from_search=true&from_srp=true&qid=cEFFeerCaC&rank=1

The Code by Peter McAllister. Photoshop went rogue Ai on this book cover.

BUY THE CODE BY PETER MCALLISTER

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Finding European software alternatives to standard non European software is flavour of the month this side of the Altlantic. With geopolitical certainties dissolving faster than annual licence renewals, B2B firms are waking up to a question they had conveniently parked for years: just how dependent are they on their current software stack? Salesforce, Microsoft 365, Google Workspace, HubSpot — tools so deeply embedded in daily operations that their vulnerability tends to get overlooked. This article doesn’t pretend to hand you a ready-made list of the best European software alternatives; that would be both arrogant and futile. What it does offer is a framework — rational, professional, free of any ideological baggage — to help decision-makers take an honest look at their exposure and find credible ways forward. Keep calm and select new software vendors sort of thing.

European software alternatives for businesses

European software alternatives are all anyone wants to talk about right now. To cut through the ideological noise, here is a practical methodology and a few things worth watching out for. Image http://antimuseum.com

I put these ideas together ahead of a webinar I’m running on LinkedIn on 12 March, as a way of getting my thoughts in order.

None of this is meant as a final word on the subject — more the opening of a conversation that matters to a growing number of professionals who, like the rest of us, are navigating a period of upheaval in which nothing can be taken for granted, software choices included.

I’ve made a lot of software choices over the years, and the one thing that has always struck me is just how much methodology matters if you want choices that actually hold up over time. Easier said than done, mind you — there are a great many criteria to weigh up, and some of them are genuinely tricky to pin down. Long-term viability is a good example: normally near the top of any procurement checklist, it takes on a whole different meaning when the possibility of having your access switched off overnight is no longer hypothetical.

With European software alternatives, the real question isn’t how to break free from your chains — it’s which new chains you’d rather wear

Picking a software suite is never straightforward at the best of times. In the current climate — where the ground can shift completely without a moment’s notice — it demands even more careful thought.

Sovereignty, sovereignism, or simply prudence?

Let me be clear from the outset: my take here is professional and rational, not political. Politics doesn’t interest me in this context. I have no intention of evaluating software alternatives through any ideological prism — what I’m after is the kind of clear-headed thinking you’d apply to a crisis management scenario. The goal, to borrow the term favoured by Nassim Nicholas Taleb, is to bring an antifragile lens to the question.

The scope of European software alternatives

My focus has been on MarTech, SalesTech and office productivity tools in the broadest sense — cloud storage and archiving included. The webinar title calls out Salesforce and HubSpot specifically, but as far as I’m concerned the issue runs much deeper than that.

The same methodology can easily stretch into more industry-specific territory too, given how thoroughly technology now underpins B2B operations — from the till at your local baker’s or restaurant through to the most complex design and production platforms imaginable.

Thinking it through, I also realised you can’t really ignore operating systems. What use is an application that won’t run on your users’ machines — or worse, one that runs perfectly but quietly leaves the door open to security vulnerabilities?

Good old Europe — 27 countries, 24 official languages, and 27 different national transpositions of EU law. Would a Hungarian or Czech software vendor actually be safer than an independent American one? When it comes to European software alternatives, that’s still very much an open question…

Urgency — dependency and threat assessment

The starting point, in my view, is to get a clear picture of how exposed you actually are — both in terms of dependency and of what cybersecurity people would call the “threat level.” Are you locked in, or not? Can you get your data out if you need to? Those are the questions to tackle first. Then comes the threat itself: are you facing something urgent, or is this more a matter of sensible contingency planning?

Committing to a software suite is a serious business. Jumping ship to something purely because it comes from a country you currently trust is not a strategy. Take Switzerland — long held up across Western Europe as the gold standard for data privacy. A legislative change currently working its way through the Swiss system has rattled enough cages for several companies, Proton among them, to start exploring moving their hosting elsewhere. Which only goes to show why knee-jerk decisions are so dangerous. Even a country with an impeccable track record — Germany, France, take your pick — can turn into a risk overnight following a change of government, a constitutional shift, or simply a new piece of legislation.

Avoiding “sovereignty washing”

As I said above, ideology needs to stay out of it. Keep it rational.

Which means not falling for the trap of rushing towards any vendor simply because it sounds German — or any other nationality — when a closer look at its foreign operations or its parent group reveals that its much-vaunted independence is largely theoretical.

Priorities — data and software

One of the first things I learnt when I started out as an IT project owner was to keep data and software firmly separate in my thinking. At the end of the day, what matters more — Salesforce the tool, or your customer database? As with most IT projects, the real priority is sorting out your data archiving and portability strategy first.

Time

Timing matters enormously. There’s a palpable sense of urgency in the air right now, and understandably so — but it mustn’t blind us to the longer view. Technology has its own history, and that history tends to play out over years, not weeks. Which is precisely why a medium-to-long-term approach makes sense: getting users to change their habits takes time and energy at the best of times.

The roadmap, as I see it, is fairly straightforward: start by archiving, securing and preparing your data for portability. Then find alternatives that are genuinely credible and built to last. And crucially, take your users with you — because if you don’t, the classic BYOD shadow IT problem will rear its head. When people can’t find what they need inside the company, they go and find it on the internet, quietly, without telling anyone.

I’m reminded of a story from a major European aerospace company, where the CEO — right in the middle of a high-security defence messaging rollout — demanded that his own emails be redirected to… Yahoo!

The European software alternatives comparison table

I put together a comparison table with a little help from http://claude.ai. And I’ll say it again: this is not a finished product. Think of it as a working matrix — something to make your own, adapt, keep updated, and cross-check carefully. The exercise turned up a few surprises: http://mapp.com, for instance, gets labelled as a German solution, when in reality it was a German company bought by an American one — Mapp Digital emerged from the merger of Teradata’s and BlueHornet Networks’ marketing businesses through a US investment fund.

There are also plenty of criteria missing from this table — ones that will depend entirely on your project, your context and, of course, your budget.

Disclaimer: the table below is a working matrix, not a final verdict. It scores the main B2B software tools across productivity, MarTech and SalesTech on two dimensions: a dependency score (technical lock-in, data portability, migration cost) and a risk score (CLOUD Act exposure, data sensitivity, GDPR compliance, geopolitical risk). For each category, European alternatives are flagged — with no illusions: some vendors that bill themselves as “European” turn out, on closer inspection, to be owned by non-EU groups, which rather undermines their claims to independence.

The approach is deliberately rational and professional — no axes to grind. The point isn’t to tell you what to choose, but to give you a framework to think it through — one you can combine with your own criteria around context, budget and use case — as a starting point for an honest review of your software ecosystem’s resilience.

The European software alternatives table — download, adapt and make it your own

b2b-software-ranking-en-v2xlsx

Download the EXCEL file as b2b_software_ranking_EN_v2Download

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The discourse around the job impact of artificial intelligence (AI) has reached fever pitch. Headlines scream about mass layoffs, and corporate press releases tout AI as the solution to workforce costs. Yet beneath this cacophony of alarm and hype lies a more nuanced reality. J.P. Gownder, Vice President and Principal Analyst on Forrester’s Future of Work team, has spent decades analysing how technology transforms the workplace. His latest report, The Forrester AI Job Impact Forecast for the US 2025-2030, cuts through the noise with empirical rigour. The verdict? The job apocalypse is not upon us, but a measured reckoning is coming.

AI Job Impact in the US: Why the Apocalypse Can Wait

JP Gownder is adamant: the AI job. apocalypse can wait. At least until 2030. Phew! All images in this post made with a combination of Midjourney, Gemini Nano Banana pro and Adobe Photoshop

The Gap Between AI Job Impact Announcements and Reality

When Klarna declared it would stop hiring humans, the tech world took notice. The Swedish fintech became a poster child for AI-driven workforce reduction. Yet a closer examination reveals a pattern Gownder has observed across hundreds of enterprise conversations: the disconnect between C-suite proclamations and operational reality.

Nine out of ten companies announcing AI layoffs don’t actually have mature AI solutions ready. So most of the layoffs are financially driven and AI is just the scapegoat, at least today

— J.P. Gownder, Forrester

The phenomenon echoes what happened after IBM Watson’s Jeopardy victory in 2011, when panic about imminent job losses proved premature by half a decade. The mechanics of this gap are straightforward. A CEO announces a 20% workforce reduction with AI backfilling the work. But standing up an AI solution that actually performs those tasks requires 18 to 24 months, “if it works at all.” Meanwhile, the work still needs doing.

Gownder has witnessed organisations that fired employees citing AI capabilities, only to quietly hire teams in lower-cost markets weeks later. “They’re firing people because of AI,” he observes, “and then three weeks later they hire a team in India because the labour is so much cheaper.” The AI narrative, in many cases, serves as convenient cover for old-fashioned cost arbitrage.

Klarna’s trajectory illustrates this pattern. After aggressively cutting its workforce by 40% and touting an AI chatbot capable of doing the work of 700 customer service agents, the company reversed course. CEO Sebastian Siemiatkowski acknowledged that the aggressive automation had resulted in “lower quality” service. The company is now recruiting human customer service agents in an “Uber-type setup.”

Understanding the 6% AI Job Impact Forecast

Forrester’s forecast projects a 6% net job loss by 2030, roughly 10.4 million positions in the US economy. Half of this impact stems from generative AI; the remainder from automation, physical robotics, and non-generative AI applications. The number may seem modest compared to the apocalyptic predictions circulating in media, but context matters. During the Great Recession of 2008-2009, the United States lost 8.7 million jobs. Those losses, however, were temporary, tied to macroeconomic conditions that eventually reversed. The jobs Forrester forecasts losing are “structurally replaced by machine labour” and may not return.

AI impact on Jobs: I would expect to see a lot more freelance and consulting work to be happening, but it doesn’t mean that there won’t be a traditional job track somewhere as well. JP Gownder

The methodology behind this figure draws on the O-Net dataset maintained by the Bureau of Labor Statistics, which catalogues over 800 job categories with detailed information about required skills and tasks. By mapping these against AI’s current and projected capabilities, Gownder and his colleague Michael O’Grady can identify which roles face the highest automation potential. “For jobs that involve skills and tasks that are heavily impacted by AI and automation, we predict more job loss,” Gownder explains. “In job categories that are less impacted, obviously, we would predict less.”

Forrester analysed 800 different job types. It seems that Art therapy is the right way to go.

The Solow Paradox and AI Productivity

Robert Solow’s famous observation that “we see computers everywhere except in the productivity statistics” finds a new iteration in the AI era. The parallel is instructive. It took nearly three decades for the internet’s productivity impact to materialise. E-commerce is only now truly disrupting traditional retail, as evidenced by the shuttering of independent shops from New York to Paris. Could Forrester’s five-year window be too narrow?

Gownder acknowledges the limitation inherent in forecasting: “Anything that you forecast beyond five years is effectively an impression.” Yet the pace of technology adoption has accelerated dramatically. The telephone required 75 years to reach 100 million users from its 1878 introduction. The personal computer achieved the same milestone in 16 years. Mobile phones took seven years. ChatGPT? Two months. This compression suggests that while the Solow paradox may still apply, its timeline could be considerably shorter.

“If there’s a job apocalypse, you’re going to have fewer people working because that’s what the apocalypse means. Those people would have to be producing more output. You cannot see a job apocalypse without aggregate productivity going up.”

— J.P. Gownder, Forrester

The productivity data tells a sobering story. From 1947 to 1973, US labour productivity grew at 2.7% annually. The current business cycle shows 1.8%. Even isolating the quarters since ChatGPT’s release yields only 2.2%. The numbers don’t lie, and they’re not yet showing the revolutionary gains AI proponents promise.

Where the AI Job Impact Pressure Points Lie

The AI job impact in the US will not be evenly distributed. Contact centre workers face continued pressure from automation that began with interactive voice response systems and now benefits from far more sophisticated solutions. Technical writers and web content creators occupy vulnerable ground. Insurance underwriters are seeing algorithmic encroachment; computer vision can now assess car accident damage from uploaded photos. Junior-level roles involving spreadsheet or presentation creation face mounting pressure.

Software development presents a nuanced case. “If you are a junior level software developer,” Gownder notes, “we know that Claude does a great job of creating basic code.” Yet senior developers with architectural judgement and system-level understanding remain essential. The pattern repeats across knowledge work: AI augments more than it replaces, transforming job descriptions rather than eliminating positions entirely. “It’s not that there aren’t jobs that will go away,” he clarifies, “but they are much more specific and limited, and they need to be architected with the right technology to replace that job. It’s not everybody goes away.”

Blue-collar work presents its own dynamics. Physical robotics will play a role in certain sectors: warehouse sorting and picking have improved through computer vision, and construction has seen experiments with brick-laying and cement-pouring robots. But the humanoid robots capturing media attention are unlikely to achieve significant workplace deployment within the forecast period. The physical world, with its infinite variations and unexpected challenges, remains stubbornly resistant to automation.

The White-Collar AI Job Impact Misconception

White-collar workers now constitute roughly 60% of the workforce in both the US and Europe, a dramatic shift from previous generations. These “symbolic analysts,” as Charles Handy termed them, don’t produce physical goods, which has led some to assume their work is easily transferable to AI systems. Gownder pushes back against this notion. “Most white-collar work is, in fact, fairly productive because there is something on the other end that someone is willing to pay for.” Software engineers create applications that enable other work. Physicians produce healthcare outcomes. Analysts help organisations make better decisions.

The practical challenges of AI deployment in white-collar settings corroborate these theoretical objections. Hallucinations remain a persistent problem, introducing error margins that knowledge workers must catch and correct. Employees often lack the skills and understanding to use AI tools effectively. Organisations overextend their expectations of what AI can accomplish. “When it fails, it’s dramatic,” Gownder observes. The Deloitte incidents in Australia and Canada, where AI-generated content with obvious hallucinations reached government clients, illustrate the reputational risks of premature automation. The Australian government report contained fabricated academic citations and even a made-up quote from a federal court judgement. Both governments required refunds.

“You don’t want to produce AI work slop and present it as your work without editing, without perspective. That is a losing proposition.”

— J.P. Gownder, Forrester

A Harvard Business Review study reinforces these concerns. Researchers found that executives who used ChatGPT to make predictions became significantly more optimistic, confident, and produced worse forecasts than those who consulted with peers. The authoritative voice of AI produces a strong sense of assurance, unchecked by the social regulation and useful scepticism that human consultation provides.

AI Job Impact on Marketers and Digital Professionals

For students entering digital marketing and related fields, the picture is complex but not necessarily bleak. “Marketers are actually on the front lines of job transformation, not job replacement,” Gownder notes. The distinction matters. Transformation implies evolution of roles rather than elimination. “I work with a lot of marketers and they say, ‘I signed up to be a great marketer. I didn’t sign up to be an AI expert. Why am I learning all of these tools?’ But inevitably, they now can’t do their job without using some kind of AI tool.”

The prescription for emerging professionals is clear: combine classical education with a genuine understanding of AI capabilities and limitations. Those who master both domains will find themselves in demand. Those who resist the technology or fail to understand its boundaries will struggle. The key lies in approaching AI as augmentation rather than replacement—using tools to enhance existing expertise while maintaining awareness of their limitations. The judgement, ethics, and institutional knowledge that experienced professionals bring cannot be easily replicated by algorithms.

Freelancers and AI

If AI augments rather than replaces traditional employees, the question arises: will freelancers and gig economy workers absorb the displacement? The white-collar economy is experiencing a broader transition towards more freelance and contract arrangements at all levels. “On some level,” Gownder observes, “this can give people a certain freedom, because they can work with lots of different clients and they can make their own hours. They can work wherever they want to.” The flexibility that defines gig work aligns well with the project-based nature of AI-augmented workflows.

Yet the picture is not uniformly positive. In the United States, where people depend upon employment for health care, freelance arrangements can be precarious. The gig economy now encompasses over 64 million American workers, contributing nearly $1.27 trillion to the economy. AI is reshaping this landscape in contradictory ways: platforms use algorithms to match workers with clients more efficiently, but the same technology enables clients to handle tasks they previously outsourced. The freelancers most likely to thrive will be those who combine technical literacy with uniquely human skills—critical thinking, creativity, and client trust.

I would expect to see a lot more freelance and consulting work to be happening, but it doesn’t mean that there won’t be a traditional job track somewhere as well

— J.P. Gownder, Forrester

New niches are emerging even as others contract. Prompt engineering, AI ethics consulting, and AI training roles represent growth areas that didn’t exist before the current wave of generative AI. The bifurcation may prove to be one of AI’s most significant labour market effects: some workers gaining flexibility and autonomy, others losing stability and benefits.

Navigating the AI Job Transformation

For workers at either end of their careers, the AI transition presents distinct challenges. Early-career professionals face the paradox of entering a workforce that may value their digital nativity while threatening the entry-level positions that traditionally served as training grounds. Gownder’s advice is direct: combine classical education with a genuine understanding of AI capabilities and limitations.

Older workers, often stereotyped as technologically resistant, have their own path forward. “One of the negatives that people associate with older workers is that they are incapable of embracing technology,” Gownder observes. “That is something one can work on.” The key lies in approaching AI as augmentation rather than replacement, using tools to enhance existing expertise while maintaining awareness of their limitations. The judgement, ethics, and institutional knowledge that experienced workers bring cannot be easily replicated by algorithms.

For business leaders, the prescription is almost counterintuitive. “The irony of AI is that the way that you succeed today is by investing in your human employees.” The technology can augment productivity, but only when workers possess the skills, motivation, and ethical framework to deploy it effectively. The human element, far from being made obsolete, becomes more critical than ever.

The Long View on AI and US Jobs

The AI job impact in the US will unfold over years, not months. Forrester’s 6% forecast represents a significant transformation affecting millions of workers, but it is a measured shift, not a sudden collapse. The organisations that thrive will be those that resist the temptation to conflate AI announcements with AI capabilities, that invest in their workforce rather than assuming technology will render it obsolete, and that approach automation with the same rigour they would bring to any major capital investment.

The irony of AI is that the way that you succeed today is by investing in your human employees. Invest in your people, counterintuitively

— J.P. Gownder, Forrester

Gownder’s work at Forrester provides a framework for this navigation: empirical rather than hysterical, specific rather than sweeping, attentive to both the genuine capabilities of AI and its persistent limitations. The job apocalypse makes for compelling headlines, but the evidence points to something more complex and ultimately more manageable. For those willing to adapt, invest in skills, and maintain perspective, the future of work remains a human story, augmented but not replaced by artificial intelligence.

J.P. Gownder is Vice President and Principal Analyst on Forrester’s Future of Work team. A Harvard graduate, he covers the impacts that technology and human factors jointly have on the future of work, helping clients design strategies that drive productivity, collaboration, and effective hybrid work. His research covers how technologies like devices, collaboration software, extended reality, and artificial intelligence reshape the future of how and where we work.

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AI is not a tool, or is it? Reports regarding the impact of AI on jobs, society and businesses are cropping up all over the place at the moment in all corners of the world. Some of these reports are announcing forthcoming revolutions both for societies and our economies whereas others are playing down the impact of artificial intelligence, and reviving the good old Solow aka Productivity paradox (“You can see the computer age everywhere but in the productivity statistics”. follow up here and here). As a consequence, it is very hard to make an opinion, let alone advise business people and students alike with regard to what needs to be done in the future. Visionary Marketing has embarked on a mission to try and shed light on this topic in as rational and informed a way as possible.

AI is not a tool, or is it?

Should AI platforms become tawpayers? The great love affair of French people for taxes will not spare Artificial Intelligence Cavazza surmises. Indeed, according to him, AI is not a tool!

A lot of these predictions are guided by ideology. The authors, be they proponents or opponents of AI, have a personal agenda, often political or ideological, and are trying to make facts stick to this agenda. This is not very useful. But others are based on fact and careful analysis. I have decided to focus on two of these reports/predictions.

The first one is Fred Cavazza’s analysis of the impact of AI on society and the economy (original post in French), which describes Artificial Intelligence as a source of profound disruption. I have known Fred for years, and I know his deep knowledge of both subjects, which makes his report particularly valuable. With his kind permission, I have translated his piece from French to shed light on this subject.

The other report is by Forrester’s JP Gownder, whom I’ll be interviewing soon. I will test Fred’s assumptions on JP and see what he has to say about this idea of disruption by AI. Hopefully, our readers, and especially my students who have a lot of pending questions about this, will be able to separate the wheat from the chaff after these two interviews and podcasts.

AI is not a tool, it’s reshaping our society and economy

AI can’t be seen as just another technological innovation. By establishing itself as a major driver of productivity, automation and decision-making, it’s fundamentally disrupting the economic and social balance of our society. Whilst the productivity gains brought by AI are already transforming office jobs and creating a chasm between employees who’ve embraced it and those who haven’t, a fundamental question emerges: how do we integrate these synthetic entities into our collective organisations? Between appropriate taxation, legal personality and psychological resistance, there are numerous questions to debate before we can draft a new social contract.

AI IS NOT A TOOL — TLDR

• AI is triggering a disruption of our civilisation, it’s not just another tech breakthrough. It marks our genuine entry into the fourth industrial revolution by offloading, for the first time, human thinking and creativity to machines.
• AI’s productivity gains are already real and deeply uneven. A growing divide is opening up between workers who can work alongside AI and those stuck with 20th-century methods.
• AI agents are challenging how white-collar workers create value. Intelligent agents are transforming knowledge work, undermining certain business models and setting the stage for a rapid reshaping of office jobs.
• Integrating AI requires a new legal and fiscal framework. Like corporate entities, AI agents must be given a status that clarifies their responsibilities and reintegrates their value into the social contract.
• The socio-economic impacts reach far beyond just employment. AI affects our psychology, culture and demographics, making public debate crucial to head off looming social tensions.

AI on the Davos Agenda

This week, the world’s leaders are gathered at the Davos Economic Forum, and ecology isn’t on the agenda: AI, Big Tech and Trump Shine Most Brightly at the Davos Show.

At Davos, the AI is not a toll debate was all the rage. Cavazza thinks that artificial intelligence will be a major disruptor not just of our exonomies but our societies too.

AI is dominating every conversation, with considerations that extend far beyond technology:

• AI Is Poised to Take Over Language, Law and Religion, Historian Yuval Noah Harari Warns
• Palantir CEO says AI to make large-scale immigration obsolete

“Artificial intelligence will displace so many jobs that it will eliminate the need for mass immigration”

I’m not going to wade into commenting on everyone’s pronouncements, with their more or less biased viewpoints, but what’s certain is that major upheavals are on the horizon:

• AI and the Next Economy
• Nearly 80% of people feel unprepared to find a job in 2026
• The AI revolution is here. Will the economy survive the transition?

AI specialists are naturally the star guests at this 2026 edition of the Davos forum, invited to give their testimony and views: Deepmind and Anthropic CEOs expect AI to hit entry-level jobs and internships in 2026.

Looking at it this way, it seems absurd to sit back as spectators whilst the AI revolution unfolds and do nothing to limit the fallout from this productivity shock. But not all’s lost—at least not for everyone, as countries in the global south are already gearing up for it: The AI Revolution Needs Plumbers After All.

Productivity gains to be nuanced, but certainly not ignored

I’ve had plenty of chances to explain generative AI’s impact (Superintelligence will multiply our capacity to act tenfold and The digital divide is a problem no one can ignore). Whilst we’re largely in agreement about what widespread generative models mean, there’s serious disagreement over the timeline for AI’s arrival. The dominant narrative keeps insisting that general AI is a pipe dream and that human intelligence is and will remain superior to machines.

What is intelligence?

This is precisely where ambiguities crop up: firstly, intelligence comes in many forms (Theory of multiple intelligences and What’s your intelligence type?); secondly, not all office work requires emotional or social intelligence. What I’m getting at is that most service sector jobs boil down to shuffling information and data between systems. You don’t need to be a genius to do that—AI can handle it with ease.

To properly grasp the speed at which latest-generation AIs will gradually transform office jobs, I recommend you peruse the latest edition of Claude’s publisher’s macroeconomic barometer: Anthropic Economic Index 2026.

Anthropic’s economis index 2026

For this fourth edition, the study’s authors analysed thousands of people’s activities using increasingly precise indicators: New building blocks for understanding AI use.

This study yields several findings that demonstrate a strong progression in the adoption and capabilities of generative models. Notably, they observe an average 30% growth in Claude usage, driven mainly by the API rather than the chatbot—a sign of rapid adoption by advanced users (e.g., IT professionals) and slower uptake by ordinary users (white-collar workers using the web version).

AI is not (just) a tool. As a matter of fact it’s not a tool at all, it’s a meta tool, a tool you can use to make tools..

The haves and the have nots

A gap is therefore widening between those who’ve adopted new habits (working in tandem with AI) and those still working as they did in the 20th century. This gap is starting to become problematic, because the latest version of Claude (Opus 4.5) has capabilities comparable to those of an adult who’s benefited from over 14 years of education—the equivalent of a Bachelor’s degree.

AI is not a tool but Clause isn’t a PHD either… yet.

The question therefore is: how much longer can an employer justify paying salaries or hiring young graduates when chunks of the work can be farmed out to an AI? Whilst average productivity gains remain modest (1.8% according to the latest figures), AI’s contribution to certain tasks is absolutely spectacular:

• an average of 14 minutes to write a long article, versus 3 hours without AI assistance;
• an average of 5 minutes to analyse a complex data table, versus 1 hour 45 minutes without AI assistance.

AI is not a tool, there are alo APIs

You might argue this data’s skewed because these spectacular scores come from employees who are whizzes at using AI (therefore logically hyper-performers), but that’s not the case—the study covers ordinary employees with a 67% success rate for outsourced tasks.

What this boils down to is that for a third of tasks, AI slashes processing time by 10 to 20 times in two-thirds of cases. If we apply some basic maths, AI can potentially triple efficiency—or to put it another way, cut the average time needed to complete a task by two-thirds. Which type of profile do you reckon managers will favour?

(hint: McKinsey challenges graduates to use AI chatbot in recruitment overhaul)

Soon the arrival of agentic white-collar workers

Let me be clear: the productivity gains mentioned above relate to advanced AI usage, not just running searches in ChatGPT or asking Copilot to knock up meeting minutes. We’re talking about using generative models to their full potential, particularly intelligent agents (see Agentic Web: the revolution that won’t wait for you).

Intelligent agents

We’ve been banging on about these famous intelligent agents for a while now, but their potential only recently became blindingly obvious to ordinary employees (non-IT types) with the release of Claude Cowork, a very concrete wake-up call to the power of agentic AI: Claude Is Taking the AI World by Storm, and Even Non-Nerds Are Blown Away.

AI is not a tool and Cowork is not (quite) a chatbot

This awakening is shared by financial markets too, which are bracing for revenue drops at traditional software publishers, whilst one of France’s biggest IT services firms is axing jobs and European banks are preparing to follow suit:

• Claude’s new AI agent pushes down software stocks
• Capgemini plans to cut up to 2,400 jobs in France
• AI forecast to put 200,000 European banking jobs at risk by 2030

Adoption levels a matter for debate

This isn’t a topic to take lightly, even though adoption levels are debatable (as I explained earlier, it’s not binary) and gains vary wildly (Why AI Boosts Creativity for Some Employees but Not Others). What’s undeniable is that AI agents are forcing a major rethink of how white-collar workers create value, and more broadly for tertiary sector businesses that account for three-quarters of France’s GDP.

Whether you like it or not, whether you acknowledge it or not, we’re living through a civilisational shift, because AI’s arrival is turbocharging the fourth industrial revolution and unleashing upheavals whose full scope we’ve yet to grasp.

Fair enough, AI is a tricky concept to get your head round (We don’t need better AI, but a better understanding of AI). Yes, tools based on generative models require behavioural changes that’ll take ages to embed. Nevertheless, it’s crucial we prepare ourselves psychologically for the coming upheavals, because if we take even the slightest step back, we quickly realise they’re already underway.

AI is not just a tool: a shift beyond technology

Generative AI’s arrival and the march towards the first superintelligences aren’t just another turn of the technological wheel started by computers and smartphones. We’re witnessing a civilisational shift that marks our genuine entry into the fourth industrial revolution (Waves of change: Understanding the driving force of innovation cycles).

We’re not simply facing a new technological cycle, but a fundamental reshaping of economic and social foundations: for the first time, we’re offloading not physical power, but our thinking and creativity. Whether AGI arrives tomorrow or in ten years, we’re already living alongside autonomous entities capable of making decisions: synthetic agents, whether digital (AI agents) or physical (robots).

This situation throws up an unprecedented question: how do we integrate artificial entities that contribute massively to wealth creation whilst guzzling significant resources into our collective framework? History offers an imperfect but revealing precedent: how we’ve gradually integrated domesticated animals.

AI i not a tool: from biological analogy to legal reality

Humans get along perfectly well with domesticated animals because they’ve helped shape humanity’s development:

• Horses served to explore territories, wage war, plough the land, transport people and goods…
• Dogs were used for hunting, for guarding…

Insofar as animals contribute daily to our society, they benefit from services and rights:

• Guide dogs for the blind attend school and have status (a function = a job);
• Police dogs play a vital role in the fight against drugs; they’re entitled to retirement (they’re placed in a home for their old age).

AI is not a tool, neither are police dogs

From the moment animals make a direct contribution, they’re integrated into our society through their breeder and/or owner, who have obligations (identity tags and records for farm animals). They can benefit from protections (insurance, vaccination to fight epidemics…) and rights (laws against animal cruelty).

So what about AI that contributes value just as much, if not more, to our society?

Whilst it’s tempting to liken AI agents to a newly integrated species, much like domesticated animals, this analogy quickly hits ethical and legal buffers. Domesticated animals have rights because they’re sentient, conscious beings. AI, on the other hand, is an information processing system, software that has neither sentience nor consciousness.

The true parallel must be drawn with corporate entities (companies). Because, like a company, an AI:

• contributes to wealth creation (task automation, content generation…);
• exploits infrastructure and consumes critical resources (energy, rare earths, cooling water…);
• has rights (intellectual property) and responsibilities (transparency, explainability…);
• acts autonomously.

This is why the comparison is pertinent, as it enables us to evolve the legal and social framework.

The social contract of the synthetic era: responsibility and taxation

Integrating these intelligent agents into our society shouldn’t be done by granting anthropomorphic rights, which would be absurd for a computer system, but by giving them legal personality (like a company, association or local authority).

The real question isn’t whether AI deserve rights, but what legal status would clarify chains of responsibility. The avenue of electronic personality, debated in the European Parliament as early as 2017, aims precisely at this objective: not to recognise dignity in machines, but to organise their integration into our jurisdiction to protect humans, ensure they benefit from it, and that this benefit is distributed fairly (avoiding an even greater concentration of wealth and power).

As robots and AI agents replace human labour, they erode the base of social contributions that rests on salaries. But since they contribute to economic activity and generate costs for the community (energy consumption, electronic waste management…), there’s no reason why they shouldn’t be integrated into our tax system.

This isn’t about taxing AI agents as individuals, but applying tax to the value they generate through their operation. In exchange for this contribution, the AI (or its publisher) doesn’t gain social rights (pension, healthcare), but gets a framework of civil responsibility (fiscal, legal, social). This would enable AI-caused damage to be covered without necessarily tracing responsibility back to the original developer, who’s often disconnected from what the model ends up doing.

Socio-economic upheavals whose scope we don’t fully grasp

Having said that, the question of AI’s place in 21st-century society mustn’t stop at economic considerations, as it extends far beyond.

Domesticated animals and AI

If we revisit the domesticated animal analogy, we observe today that dogs aren’t just pets; for some, they’re also considered assistance animals. The exact term is “emotional support animals”—those that give retirees or psychologically fragile people (with chronic depression) a reason to get up in the morning.

The same goes for domestic robots, which are one of the pillars of Japan’s Society 5.0 programme—those that will care for the elderly with a physical presence (assisting them with daily tasks and limiting their loss of autonomy), as well as psychologically (conversing with them to exercise their memory) and emotionally (keeping them company).

AI is not a tool it’s way more than that, Cavazza surmises

For Westerners, this prospect is terrifying, but for the Japanese, it’s the only solution to their demographic deficit. Same in China, where parents work so hard they lack time to look after their child (vs “children”), and offer them AI-enhanced soft toys that tell them stories and answer their questions (satisfy their curiosity).

Furry robots

A trend that obviously came from Japan (Casio launches AI-powered furry robot pet that wants to replace your dog), but which can be experienced in the West (‘I love you too!’ My family’s creepy, unsettling week with an AI toy).

You might think all this is science fiction, Black Mirror-style, yet these are techno-sociological territories that have been explored for many years (Sony’s Aibo was launched in 1999).

Is philosophising about the merits of emotional support robots truly our priority? Apparently not, as there are more urgent matters. But it’s nonetheless an essential step, because let me remind you that AI adoption in Europe is rather low—not for functional or technological reasons, but purely emotional ones (strong resistance to change and major psychological barriers stemming from a misunderstanding of what AI actually is = barely 15% average enterprise adoption): EU Digital economy and society statistics.

So ultimately: Yes, we need to have this conversation and debate properly so we can come to terms with the changes ahead, anticipate the upheavals that’ll severely test our social system, and start rethinking our social contract (From Web 4.0 to Society 5.0).

Regulation as an integration factor

Don’t panic, I’m not about to launch into a lengthy sermon on the merits of universal basic income (an economic non-starter), but I will necessarily need to talk about regulation.

Indeed, living alongside synthetic agents (AI and robots) shouldn’t be thought of in terms of domestication, as with animals (to fit into our daily lives, dogs must be vaccinated and trained), but rather as regulating a synthetic workforce we can no longer afford to ignore.

The issue isn’t whether robots or AI deserve a pension, but how the wealth they produce can sustain our social model whilst regulating resource consumption, which creates economic tensions (electricity prices) and geopolitical ones (China’s monopoly on rare earths).

AI disrupting civilisation?

That was Fred Cavazza’s account of this forthcoming civilisational revolution. In my opinion, there’s a lot of truth in Fred’s vision about the future of AI and civilisation. Some of it sounds a bit like science fiction, but so much of the real world is mimicking SF (think of Altman’s obsession with Jonze’s Her) that he might well be right. As Fred states, the impact of AI might extend way beyond the technological breakthroughs that we are witnessing.

However, it’s still early stages in my mind. I can well imagine what Anthropic’s Cowork could do in the future, but I can’t see it happening now, even though I’ve been a heavy and advanced user of Claude for years.

This will take time

It will take time to seamlessly blend these technologies to execute proper workflows and not just tasks. Agentic software is well and truly promising, and we are even able to catch glimpses of it. However, the productivity advances enabled by these technologies are often uneven. Even for advanced users.

The other day, after a one-hour and a half mentoring meeting where I delivered strategic advice, I used my usual Claude project to build a second-to-none executive summary of my recommendation as I was frying some eggs for the wife. Yet, it took three major complex steps and software suites to achieve that properly.

But don’t be mistaken, we will get there someday. It’s just the timing that’s wrong; it’s not happening just yet. Innovation requires time and effort. As Fred points out, there is also a lot of resistance to change as always in innovation, and it’s not just in Europe, even though adoption is lagging behind in a traditional way on our continent.

The impact of AI, even on jobs, will certainly be big, but it might take years to appear in the statistics, to put it in the words of Robert Solow.

That said, Forresters’ vision is more nuanced, and we will review that with JP Gownder very shortly. Time will tell whether the truth lies somewhere in the middle, as I have a hunch it does. It’s certainly less romantic or frightening (depending on your point of view), but 40 years of implementation of tech innovation has taught me to grow a stiff upper lip.

The post AI is not a tool it’s reshaping our society and economy appeared first on Marketing and Innovation.

Private equity branding remains one of the most underestimated levers for value creation in the investment world. While PE firms excel at identifying promising companies and optimising their financial structures, branding is frequently treated as an afterthought, reduced to logos and colour palettes rather than strategic assets. Yet the evidence suggests otherwise: strategic brand investment can dramatically shift market perception and, ultimately, company valuation. Marc Rust, Creative Director and Brand Strategist at Consequently Creative, has spent years demonstrating that branding deserves a seat at the strategy table. His striking claim that he transformed an $80 million company to look like a $120 million company through branding alone captures the essence of what strategic messaging can achieve when properly deployed.

How Private Equity Branding Is Transforming Company Valuation With Storytelling

The term “branding” itself creates immediate problems in private equity settings. At networking events, Rust finds that mentioning branding triggers what he calls “cognitive disruption”

Beyond Logos: Redefining What Branding Actually Means

The term “branding” itself creates immediate problems in professional settings. At networking events, Rust finds that mentioning branding triggers what he calls “cognitive disruption” – people immediately think of visual identity work that seems irrelevant to serious investment activities. Many professionals lack any clear definition of what branding encompasses, while others dismiss it as superficial design work.

This misconception misses the fundamental truth: branding and messaging represent a powerful force for business growth that should inform strategy from the outset, not be bolted on afterwards as a cosmetic exercise.

The real definition of branding, Rust argues, is “what you stand for in the minds of the people that you’re trying to reach, convert, and move into action.” This is not something companies own outright; rather, it is something they can influence through deliberate effort and sustained investment.

The critical distinction lies between what companies do and why it matters. Most organisations focus their communications on deliverables and capabilities. Yet answering the question of why it matters opens doors to deeper insight about audience pain points, goals, and outcomes. This shift acknowledges that messaging exists not for the company but for its buyers, requiring communication in their language rather than internal jargon.

The Evolution of Private Equity Strategy

The private equity landscape has fundamentally changed over the past decade. The old-school approach – acquiring a company, trimming the fat, making it lean and mean, then finding a suitable buyer – no longer resonates with contemporary markets or the talent those markets require.

Successful PE firms have embraced a different philosophy: nurturing acquired companies, building genuine value over time, and then pursuing exit strategies that reflect accumulated worth. This evolution makes branding more important than ever because value creation depends on perception as much as operational reality.

When thinking about branding in private Equity, most people immediately think of visual identity work. All that seems irrelevant to serious investment activities even though it’s blatantly wrong, Mac Rust believes. Visual made with Midjourney

Effective branding requires understanding multiple audiences simultaneously. Internal alignment comes first – the people who build products and deliver services need clarity about what their company stands for, especially during periods of transition.

Post-acquisition, this alignment frequently suffers as employees wonder about new leadership, potential job losses, and strategic direction. Consequently Creative addresses this turbulence by bringing teams together to celebrate what they stand for, building stories around acquisition rationale and forward-looking plans grounded in existing strengths rather than imposed transformations.

Beyond internal audiences, companies must establish clear market positioning relative to competitors and ecosystem partners. Finally, there are the buyers who will drive revenue growth during the holding period and, ultimately, the acquiring company that represents the exit opportunity.

Each audience requires thoughtful attention, and branding provides the framework for addressing all of them coherently while maintaining a consistent core narrative.

The Valuation Premium of Strong Brands

Buyers demonstrably pay premiums for assets with strong brand equity. Companies that look more upscale and feel right command higher prices regardless of sector. This premium extends across every touchpoint: market presence, customer service quality, sales process sophistication, product presentation, and how offerings are described and positioned. The key lies in making everything about the audience – answering why customers should care and how specific features apply to their particular situations.

Buyers demonstrably pay premiums for assets with strong brand equity, Rust declares. Visual made with Midjourney

Building a brand encompasses far more than marketing communications. Yet smaller companies actually hold advantages here that larger organisations lack. Without established brand perceptions moulded into market consciousness over decades, mid-market companies enjoy flexibility that industry giants cannot match. They can position themselves as something new even when their offerings are not particularly novel, or emphasise technology, audience needs, or other differentiating angles. The argument that mid-market companies lack resources for serious branding investment misses this opportunity – budget allocation to branding should be generous precisely because returns can be substantial and the competitive playing field favours agility over scale.

AI as Tool, Not Solution

The artificial intelligence revolution has created new temptations for companies seeking branding shortcuts. Tools now generate logos, mission statements, and complete brand architectures almost instantly. But Rust cautions strongly against treating AI as a solution rather than what it actually is: a technology that should come last in any strategic process.

The POST method he advocates begins with understanding people (your audience), then defining objectives (business goals), followed by strategy (how to achieve those goals), and only then selecting technology. Flipping this sequence – jumping on AI because everyone else has it – represents precisely the wrong approach to brand development.

The danger of AI-driven branding lies in acceptance without scrutiny. When tools generate content quickly, users become passive recipients rather than active directors, keeping their eyes closed and allowing technology into the driver’s seat. Rust draws on singer-songwriter Tom Waits: “The world is a hellish place and bad writing is destroying the quality of our suffering.” AI contributes to this problem when deployed thoughtlessly, generating content that lacks the provocative point of view necessary to differentiate companies in crowded markets. Bad content existed before AI, but artificial intelligence is intensifying the problem.

The world is a hellish place and bad writing is destroying the quality of our suffering
Tom Waits

That said, AI offers genuine utility when approached correctly. Brainstorming, idea generation, concept testing, and data synthesis all benefit from AI assistance. The technology serves well as a sounding board for strategic thinking.

The crucial distinction is maintaining human agency – staying in the driver’s seat rather than ceding control to automated systems that cannot understand business context or competitive dynamics.

B2B Private Equity Branding: The Relationship Imperative

The notion that B2B companies need branding less than consumer-facing businesses deserves serious challenge. Branding fundamentally concerns relationship-building, and relationships involve humans making decisions regardless of whether they represent individual consumers or institutional buyers.

When someone purchases at a supermarket, they often choose the best-looking product rather than the one with objectively superior ingredients. B2B purchasing follows similar patterns – everyone wants to work with companies that appear capable, innovative, and aligned with their values.

“The notion that B2B companies need branding less than consumer-facing businesses deserves serious challenge”

B2B branding may require less ongoing investment than B2C equivalents because it depends less on constant social media presence and retargeting campaigns.

However, the fundamental mechanics remain identical: building trust through consistent value delivery over time. Each interaction with a company should provide something useful, and these value contributions compound into trust.

Value + value + value = trust – a formula that applies regardless of whether customers are individuals or organisations.

The Research Imperative: Discovering Hidden Stories

The biggest mistake private equity firms make when rebranding after acquisition is proceeding without empathy for audiences. This criticism is not meant to disparage PE professionals – it simply reflects that branding expertise lies outside their core competencies.

The solution involves partnering with agencies that understand how empathy drives both growth and culture. Jumping straight to visual refresh without strategic groundwork means missing reasons to believe that proper research would uncover.

Rust illustrates this with two compelling examples. Working with a company owning approximately 100 senior living properties across the United States, his team discovered that residents were not actually the primary marketing audience.

Instead, the “adult daughter” – typically the family member who becomes caregiver for ageing parents – drives decision-making in most families. This insight transformed messaging, positioning, and the entire marketing approach, creating stronger differentiation than competitors who continued addressing residents directly.

Similarly, research for Simmons College in Bostonrevealed that women chose the institution for its academics, with its all-female status being secondary rather than the primary draw. This finding enabled far richer storytelling around academic programmes, distinguished instructors, and career outcomes under a unifying theme of “leadership by design” rather than gender-focused messaging.

The Cost of Neglect

Perhaps most puzzling is the frequency with which acquiring companies simply neglect their purchases after transactions close. Businesses get acquired – sometimes at significant cost – and then allowed to wither rather than being nurtured toward growth potential. Rust compares business development to plant cultivation: seeds will grow with minimal attention, but structured support – like a stake helping a vine climb toward sunlight – produces stronger plants bearing larger fruits. The same principle applies to acquired companies.

Neglecting brand transformation leads to predictable failures. Teams cannot understand strategy without clear articulation of what the company stands for. Customers fail to perceive value when it goes unexpressed. Culture fractures without internal alignment, and misalignment breeds resistance that undermines sales effectiveness. Eventually, the market loses sight of what the company represents, competitive positioning erodes, and the investment opportunity dissipates along with the premium that strategic branding could have created.

Looking Ahead: Trends for 2026 and Beyond

Three trends deserve attention from private equity professionals focused on brand-driven value creation.

• First, generational shifts have fundamentally altered workforce and customer expectations. Millennials and Gen Z want to work for organisations that care about their audiences and hold values they can identify with personally. The old PE playbook of acquire, strip, and flip no longer attracts the talent or customer loyalty necessary for sustainable growth.
• Second, AI requires proactive engagement rather than passive acceptance. Understanding these tools and deploying them strategically – while maintaining human judgment – will separate successful firms from those drowning in generic content. The winners will keep their eyes open, using AI for specific purposes rather than allowing it to drive business decisions.
• Third, relationship dynamics demand respect for courtship conventions. Business development remains fundamentally about human connection, yet many organisations rush toward closing before establishing trust. The equivalent of proposing marriage on a first date appears constantly in LinkedIn solicitations that skip value demonstration entirely. Understanding that relationships require multiple touches and consistent value delivery provides competitive advantage.

As for AI investment opportunities, Rust maintains cautious optimism. He worked recently with a drone software company that acquired an AI firm to enhance video data analysis for defence applications, enabling faster tactical decisions while reducing the need for constant human monitoring. This represents AI used thoughtfully as a tool – precisely the model that deserves investment. However, the current boom inevitably attracts companies claiming value where none exists. Scrutiny remains essential.

Creativity as Competitive Advantage

Perhaps the most troubling observation Rust offers concerns how business leaders equate creativity with risk. This equation represents a fundamental misunderstanding: creativity is the single most powerful tool for achieving differentiation and growth.

In markets awash with AI-generated sameness, human creativity and provocative perspective become more valuable than ever.

The firms that thrive will be those with the audacity to be different – to push forward with distinctive points of view while competitors retreat to forgettable positioning.

Consider hiring as an analogy. When reviewing candidates, interviewers are not determining whether applicants possess necessary qualifications – that was answered before the interview. Instead, they seek to understand whether candidates are different, whether they bring passion that will challenge existing thinking. The same applies to companies: differentiation commands attention and premium value, while sameness leads to commodity pricing.

For founders considering private equity investment, the advice is straightforward: develop a clear story expressed in audience-appropriate language rather than internal jargon. Ensure alignment throughout the organisation. Engage sales teams as amplifiers of that narrative. Find passionate people within the company and give them voice – authentic enthusiasm proves more compelling than polished corporate communications. These steps position companies for maximum pre-acquisition valuation and set the stage for continued growth under new ownership.

Private Equity Branding and Hunger for Growth

Fueling Value Creation with Private Equity branding. Image generated wth Gemini from our text

Private equity branding ultimately asks a simple question: how hungry are you for growth? The answer determines whether acquired companies flourish or fade, whether investments multiply or stagnate, and whether exit multiples reward strategic vision or punish brand neglect. In a world where perception increasingly drives reality, the firms that master strategic storytelling will capture disproportionate value – transforming $80 million companies into $120 million ones, and perhaps far beyond.

The post Private Equity Branding Enhances Valuation Through Storytelling appeared first on Marketing and Innovation.

The ebook self-publishing landscape has undergone a remarkable transformation over the past decade. What was once viewed with scepticism by the publishing industry has become a legitimate and often preferred path for authors worldwide. To understand the current state of this evolving market, we spoke with Kris Austin, whose platform Draft2Digital serves over 300,000 authors publishing more than a million titles across global markets. From Oklahoma City, he shared his insights on how independent authors are reshaping the publishing world.

Inside the Ebook Self-Publishing Industry

With market shares amounting to 40% of sales in the US, ebooks present new opportunities for writers who are able to benefit from self-publishing platforms like Self2digital.

Can you introduce Draft2Digital and its mission?

Draft2Digital currently serves over 300,000 authors who are independently publishing more than a million titles. We have been operating since 2012, and the industry has changed considerably during that period. Our goal is to help authors achieve their dreams by removing technical barriers and making the publishing process as streamlined and straightforward as possible.

What languages and markets do you cover?

We have published books in over a hundred languages. While English remains predominant, approximately 15 to 20 percent of sales come from non-English titles, with Spanish and German ranking as the second and third most popular languages. Our distribution reaches 180 countries, and about 40 percent of all sales occur outside the United States.

ebook self publishing industry entrepreneur Kris Austin talked to us from Oklahoma City, OK.

How has self-publishing evolved since 2012?

When we started in 2012, self-publishing was still in its early stages. The real catalyst came in 2007 when Amazon released the Kindle, which sparked the explosion of digital books. Back then, there was significant stigma attached to being an independent author; many felt they were not as credible as traditionally published writers. Today, that perception has completely shifted. Many authors now choose self-publishing as their first option. We also see numerous hybrid authors who move between traditional and independent publishing, depending on their goals. The focus has shifted to the quality of the book and reader demand rather than the publishing model itself.

What types of books dominate the ebook market?

The majority of our ebooks are genre fiction: romance, fantasy, mysteries, and thrillers. These narrative fiction categories account for approximately 80 percent of ebook sales. Our print-on-demand service shows a different pattern, with roughly 40 percent fiction and 60 percent non-fiction. All these books are intended for consumer readers purchasing for personal enjoyment.

Genre fiction (romance, fantasy, mysteries, and thrillers) amounts to approximately 80 percent of ebook sales

Wit ebook self-publishing, authors can find readers anywhere in the world without leaving their homes. Image created with Midjourney

Is ebook self-publishing viable for image-heavy books like photography?

It is possible, though more demanding. Image-heavy books typically require a professional formatter to achieve the desired layout, particularly in digital formats where presentation can be challenging. For print editions, colour printing and layout involve additional complexity compared to text-only publications.

What determines success in ebook self-publishing?

The most successful authors treat publishing as a business. After creating a book they are proud of, they focus on marketing, discoverability, sales, and distribution. They approach it with an entrepreneurial mindset. However, it can also work as a part-time endeavour, particularly for authors writing series with multiple titles. One advantage of independent publishing is that you do not need a massive readership to succeed. Indie authors typically retain 60 to 80 percent of their sales revenue, allowing them to price competitively and target niche markets effectively. Even with just 2,000 potential readers, if you capture that audience and build loyalty, you can build a sustainable career.

Indie authors typically retain 60 to 80 percent of their sales revenue

If writing is your dream, ebook self-publishing could make it real draft2digital claims.

How does Draft2Digital help authors reach global audiences?

First, availability is essential. Authors upload their manuscript in Word format to our website, along with a cover image. I recommend not spending more than 100 dollars on a cover when starting out. Our system converts everything to digital formats and distributes to thousands of stores, including major online retailers, smaller platforms, and libraries across the US, UK, and Australia, typically within a few days. Accurate metadata, including title, description, and category, is crucial for helping readers find your book.

What marketing strategies work for unknown authors?

Discoverability is always a challenge. Successful authors connect with readers through social media, choosing platforms based on their target audience. Facebook may suit an older demographic, while TikTok reaches younger readers. Authors must identify where their audience congregates and invest effort in building those connections. Nothing comes free when selling a product; it requires consistent work.

What are the main differences in reading habits across countries?

Reading preferences vary significantly by region. Some countries, like the US, have high ebook adoption, while others, such as Germany, still favour print by a considerable margin. Certain markets, like Canada, show preferences for book bundles. Interestingly, German readers consume many English-language books, so we sell substantial quantities of English print titles there.

What is the current balance between ebooks and print?

When ebooks began growing around 2007, there were widespread concerns about the death of print. That never materialised. Ebook growth peaked around 2013, but print remained dominant. Currently, approximately 60 percent of books sold are print and 40 percent are ebooks, though this varies by genre. Romance readers predominantly purchase ebooks due to lower cost and convenience, while non-fiction readers prefer print for its tactile qualities and ease of reference. This ratio has remained relatively stable for years.

Approximately 60 percent of books sold are print and 40 percent are ebooks

Are people reading less than before?

Readership fluctuates in cycles. We saw a significant peak during the COVID lockdowns, and we have been coming down from that high. However, engagement appears to be recovering. Books now compete with digital streaming and social media for attention, but dedicated readers will always find their books. We are optimistic that younger generations will discover books that resonate with them and develop reading habits.

How is artificial intelligence affecting the ebook market?

AI-written books exist throughout the market. We support AI as a tool for outlining, brainstorming, and various other assistance, much like word processors and spell checkers became standard aids. What we do not support is fully AI-generated content.

AI-written books have become a significant challenge

This has become a significant challenge, with platforms like Amazon being flooded with such material. It harms the industry and makes it harder for readers to find quality books. While AI may eventually produce excellent literature, we are not there yet, and this remains an ongoing challenge for the market.

How do you help authors stand out in a crowded marketplace?

We maintain merchandising relationships with all major retailers. Our mission is to identify promising books and propose them for spotlight placement and promotional features. We submit thousands of titles annually and achieve a 60 percent success rate. Authors can apply through our website to participate in these programmes. We also invest heavily in author education through our Self Publishing Insiders podcast, where we interview industry leaders, successful indie authors, and service providers to help authors improve their marketing and sales strategies.

What do you predict for the future of digital publishing?

Independent authors have proven their agility and ability to respond quickly to reader demands in an industry historically slow to adapt. Traditional publishers are increasingly looking to indie authors for insights on how to operate differently. They are actively recruiting successful independent authors into the traditional world. I expect traditional publishing to adopt more characteristics of indie publishing: greater agility, flexibility, and responsiveness. This convergence will continue accelerating over the coming years.

Final thoughts about ebook self-publishing

The ebook self-publishing revolution has fundamentally altered the publishing landscape. What Kris Austin describes is not merely a shift in distribution channels but a democratisation of authorship itself. With platforms like Draft2Digital removing technical barriers and providing global reach, the determining factors for success have shifted from gatekeepers to readers. For aspiring authors, the message is clear: quality content, business acumen, and direct reader engagement now matter more than ever. The stigma of self-publishing has given way to recognition that, ultimately, a book’s value lies in its ability to find and satisfy its intended audience, regardless of how it reaches them.

Learn more at Draft2Digital

The post Inside the Ebook Self-Publishing Industry appeared first on Marketing and Innovation.

Whereas artificial intelligence is reinventing Web writing, the written word has never been more valuable. Selim Niederhoffer, a copywriting trainer and bestselling author, has recently been exploring how marketing professionals can still succeed amidst “enshitification“, online influence, and automation. Meet an expert who remains confident in the power of words.

Copywriting in the age of AI: why words retain all their magic

Niederhoffer is adamant: despite GenAI, the written word retains its magic — image produced with Midjourney

Human vs. AI

Selim, a seasoned copywriter and author is keen on using what he calls “magic words.” Ask him why and his answer might surprise you in this age of sheer automation. “I’ve already been thinking about magic words for years. I want to dig deeper, show real examples and, most importantly, explain why they actually work.”

Pen and paper

His approach remains resolutely traditional.

“I genuinely work with a pen and paper.”

This approach reveals something fundamental about Web writing: it works when you truly understand the psychology behind it, not when you just mechanically apply copywriting techniques. Selim bases his work on well-established principles of persuasion.

Selim Niederhoffer still believes in the virtues of word magic in Web writing — image produced with Midjourney

“When there’s a principle of persuasion, there’s usually a word that goes with it. Take urgency or scarcity, for example. If something’s rare, that means limited places, running out of stock… that sort of thing. That’s how I build my word cluster.

The research then extends to field observation. “I look at what my clients are using, what’s going on at Burger King, McDonald’s, Nike. I check out the major brands too – what they’re doing on YouTube, on LinkedIn.”

Eventually, Selim identified 55 magic words but trimmed them down to 50. It’s an approach that perfectly shows what still sets humans apart from machines: the ability to critically analyse and curate with discrimination.

However, it’s worth adding nuance. Selim can’t conceal that he genuinely “loves” ChatGPT. As we’ll see later, this raises some legitimate questions.

Thank you! the ultimate magic word

Among the 50 words he’s analysed, the first is also the simplest: thank you! For Selim, this should be essential for every business. “How many times have you walked out of a shop where the sales assistant just didn’t seem to have noticed you? Whether you bought anything, or didn’t doesn’t make any difference, once the transaction’s done, you’re out the door.”

Yet some brands know how to thank their customers. “Nespresso or Apple, for example: the Nespresso employee comes out from behind her counter, she hands you your product. Thank you for your visit. Have a nice day! That’s how it should be.”

For Selim, saying “thank you!” is more than just being polite, it’s a way of life. “You can go further: thank you for your visit, thank you for subscribing to the newsletter, thank you for your comment. You need to constantly think in terms of gratitude.”

This approach fits into what Gary Vaynerchuk called The Thank You Economy. “We’re in an attention economy,” Selim explains. The stakes are high in Web writing: how do you maintain this human dimension at corporate scale?

“For me, the essence of business is that there’s a person in front of you who’s exchanging something with you. That’s really the foundation. But today, how do you keep that at corporate scale?”

Data confirms an intuition: gratitude improves customer experience and encourages loyalty. A valuable lesson for all those who practice Web writing and seek to create a lasting connection with their audiences.

You need to seek to create a connection with your audiences — image produced with Midjourney

AI and Web writing: threat or opportunity?

The conversation inevitably turns to artificial intelligence. For Selim, AI is first and foremost an incredible productivity tool. “If I’ve got a newsletter to write, I’ll use AI. Sales pages? AI. The thing is, AI works brilliantly for me. I’ve even become clearer in my writing,” he admits without hesitation.

But he’s not entirely starry-eyed about it. He’s identified three major pitfalls that professionals absolutely must avoid.

The three pitfalls of AI in copywriting

• The first pitfall is wordiness. “ChatGPT tends to waffle because it’s been trained on Reddit, Wikipedia, and similar sources. However, in real life, when we write, we cut down to the core, deleting most of what we initially write.”
• The second limitation is related to syntax. “AI tools have their favourite phrases. It can be a bit clunky. It’s got that ‘ChatGPT-ish’ quality – you know it when you see it. After a while, you can spot AI-written text a mile off.”
• The third issue, and the most surreptitious one, is lack of personality. “When we just use basic LLMs, we lose our tone of voice, we lose what makes us different, we lose what makes people go ‘Ah! That’s Selim, that’s Yann’. That personal touch. I’d say that’s the biggest danger AI poses for copywriters, Web writers, anyone working in this field.”

For him, the key questions are: how do you refine AI, how do you avoid its main pitfalls, how do you stay in control and how do you harness all its power?

This evolution shows a profound shift in the profession: the copywriter is becoming an orchestrator of AI platforms.

Losing our skills

Selim warns against a hidden danger: declining skills. He reminds us that the brain is a muscle, and using it creates connections. However, if we stop using it, those connections are lost. He admits that he has fallen victim to this himself.

“I have noticed that my writing isn’t as fluid as it used to be when I’m starting from scratch. Between 2010 and 2022, I was churning out three to five blog posts a week. Now, if I can just knock up a prompt, get a result and tweak it a bit, job done. But it’s less satisfying.”

This awareness led him to experiment.

“I run A/B tests. Send out version A written by AI, version B written by me, then see who clicks more. I check which headlines work best, which text performs better,” he explains.

It’s a data-driven approach that could bruise his ego, he admits with a laugh, but it’s essential for understanding what genuine human value looks like in Web writing.

What human added value tomorrow?

The final question comes naturally: will humans still add value compared to machines in a year or two? Selim remains cautious about making predictions. “I’m rubbish at forecasting because I don’t spend enough time on it,” he admits candidly. But his reading of the market reassures him.

While I can see companies increasingly launching AI training for their staff, there’s always the question of depth. Are they adequately trained and properly supported?

Web writing needs remain massive

“I meet marketing directors who want me on their pay-roll. We’ve got so much work to get through that we’d need ten people using ChatGPT just to keep up,” he reports.

This suggests that AI isn’t so much replacing writers as it is multiplying opportunities for content production.

Selim’s thoughts tap into bigger questions about how work is evolving.

“The idea of heading towards a society where people become useless doesn’t surprise me. I’ve seen it enough times in sci-fi,” he confides.

This vision actually mirrors history: villages like ours in the Pyrenees, with thousands of inhabitants at the turn of the 20th century now down to a few dozens. Economic and technological shifts have always redrawn the employment map.

What the future holds for Web writing

This conversation with Selim Niederhoffer sketches out the shape of Web writing as it transforms. Words remain magical, but how we summon that magic is changing.

AI’s becoming a powerful content production tool, but the real value still lies in properly understanding how persuasion works, in being able to orchestrate these tools cleverly, and in keeping a human tone of voice.

Here’s the paradox: just when machines can churn out infinite text, what becomes scarce is quality strategic thinking, subtle psychological understanding, and the ability to inject real personality into content. Selim’s magic words are just a a beginning – they are mere ways of understanding what makes human readers tick.

A hybrid Web

According to Selim, tomorrow’s Web writing will be hybrid – a collaboration between human and machine. It will demand technical mastery of AI tools, but it’ll be rooted in a deep understanding of how persuasion actually works.

In short, words will always be magical, but that magic needs cultivating, training, constant work – otherwise it will erode in the face of automation’s easy appeal.

One needs to keep reading, discussing and engaging with real people. That’s how we will train our brains and hang onto that human edge that makes all the difference in Web writing.

10 of Selim’s 50 magic words

Here are 10 words taken from Selim Niederhoffer’s book “Les Mots Magiques” (Magic Words), which are effective words to boost your marketing content and encourage action:

• Please
• Thank you
• New
• Since
• Out of stock
• Free
• Now or never
• Satisfaction guaranteed or your money back
• Hundreds of people already trust us
• First name

The post Web writing : words retain all their magic appeared first on Marketing and Innovation.

Commentary on the environmental impact of AI often swings wildly between doom-and-gloom catastrophism and blind techno-optimism. But where’s the truth in all this? On July 24, 2025—the symbolic date of Earth Overshoot Day—we sat down with Yves Grandmontagne, founder and editor-in-chief of DCMAG (Data Centre Magazine*), to get his take on AI and its real environmental impact. It is worthy of note that Yves and I explored Silicon Valley’s infrastructure innovators together through extensive press tours some time ago. This provided us with firsthand insight into the tech industry’s approach to these challenges. The current annotated transcript of our interview is a summary of our thorough, nuanced, and let’s admit it, quite lengthy discussion. You are therefore encouraged to treat this article as your starting point for diving deeper into this extremely complex topic.

Exploring the Real Environmental Impact of AI

What’s the real environmental impact of AI? An employee keeps watch over the cooling units at Orange’s data centre in Val de Rueil in Normandy, France — Photo https://antimuseum.com

* DCMag is only available in French

This post summarises what turned out to be an incredibly rich hour-long conversation. The sheer complexity of this topic forced us to dig into multiple technical, economic, and environmental angles—making any kind of comprehensive analysis near inconceivable.

Drawing on his deep expertise in the data centre and AI sectors, Yves Grandmontagne gives us some much-needed factual perspective on a debate that’s often polarised between doomsday scenarios and over-the-top techno-optimism. To tackle this properly, we decided to take recent quotes—both positive and negative—and fact-check them with our expert.

Yves’s analysis helps us cut through the noise and understand what’s really at stake in this technological breakthrough.

Environmental Impact of AI: Reality Check Time

TLDR: Environmental Impact of AI

The electricity consumption issue is more nuanced than you think* – AI will represent 20-30% of data centre consumption (not twice that number), and only 2-4% of overall electricity consumption

• Energy efficiency gains are actually remarkable – Over the last decade: number of data centres x2, floor space x4, but energy consumption up only 6%
• Beware of dubious comparisons – Comparing a ChatGPT query to Google search is methodologically flawed (completely different technologies and services)
• Water consumption varies massively by geography – Huge issue in the US, but Europe has been using smarter closed-loop systems for ages
• Tech innovations look promising – New technologies (direct liquid cooling, immersion cooling) are slashing water and energy consumption
• AI might actually be part of the solution – Can optimise energy mix management and electricity transport, which is currently our main bottleneck
• Let’s get some perspective here – Data centre impact remains pretty marginal compared to the chemical industry (32% of French energy consumption) or agriculture

*All numbers by Yves Grandmontagne at Data Centre Magazine

Bottom line: The impact is “real but massively overstated”—we need to put things in context and remain cool and collected.

So here are the quotes about the environmental impact of AI that we wanted to fact-check with Yves.

Rumours vs Reality: Decoding the Doomsday Predictions

Predictions of Booming Electricity Consumption

The first claim I put to Yves Grandmontagne was Synth Media’s prediction [Fr] that “AI’s growth could double data centre electricity consumption by 2026.” His response immediately throws cold water on this alarmist take:

“It’s absolutely true that AI is rolling out infrastructure at breakneck speed and eating up more space in data centres. Sure, it’s going to significantly bump up their energy consumption—no question about that. But will consumption actually double? I seriously doubt it. AI should account for somewhere between 20 and 30% of global data centre consumption worldwide.”

A data centre server rack — photo antimuseum.com

AI should account for somewhere between 20 and 30% of global data centre consumption worldwide

This reality check reveals something consistent throughout Yves Grandmontagne’s analysis: the absolute need to put numbers in context. The expert points out that this increase is just part of the natural progression tied to our ever-growing digital habits. “What’s driving this consumption increase is our daily usage, whether that’s for work or for personal reasons,” he reminds us, highlighting our collective responsibility in this evolution. It’s a topic we’ve tackled before with a broader focus on digital consumption.

The most striking part of his analysis is about the energy efficiency angle. Contrary to popular belief, data centres aren’t following a consumption curve that mirrors data growth. This improving efficiency is something that gets completely overlooked in public debates about the environmental impact of AI.

ChatGPT’s Carbon Footprint: More Context Needed

When it comes to the 10,113 tonnes of CO2 equivalent attributed to ChatGPT usage in January 2023 (Basta Media – Data for Good, AI has the potential to destroy the planet), Yves Grandmontagne takes a refreshingly pragmatic approach:

“I can’t verify that exact figure. Getting that precise—down to 10,113 tonnes—represents a massive methodological challenge, especially when you’re dealing with AI infrastructures that are distributed systems.”

We asked Yves Grandmontagne, editor-in-chief of DCMAG (Data Centre Magazine) to give us the real story about the environmental impact of AI — He gave us facts and figures, which we’ve compiled in the infographic at the end of this post

This observation raises a crucial methodological point: just how tough it is to accurately measure the carbon footprint of distributed infrastructures. The expert does acknowledge this pollution is real, but puts it in perspective: “Those 10,113 tonnes of CO2 still represent volumes significantly smaller than what many other industries pump out.”

This contextualisation isn’t about downplaying the issue—it’s about keeping things proportional. Yves Grandmontagne reminds us of a basic truth that is often overlooked: “The moment we use our smartphones, we become CO2 producers.” This highlights the inconsistency in criticisms that single out AI from our overall digital consumption.

The Google vs ChatGPT Comparison: A Methodological Trap

Watch out for convenient shortcuts between pollution and digital tech—they’re everywhere and pretty handy when you want to hide overall industrial pollution — image created with Midjourney

MIT’s claim that “a ChatGPT query uses ten times more electricity than a Google search [p 9]” perfectly illustrates the danger of oversimplified comparisons, according to Yves Grandmontagne. His take is particularly eye-opening:

“This comparison just doesn’t work. I think we’re making a fundamental methodological error here because we’re comparing two completely different technologies. Google is a search engine that gives you results that you then have to sift through to find what you’re actually looking for. ChatGPT, on the other hand, serves up information that’s already structured and ready to use.”

“The more we use computing, and the more we use AI, the more we consume.” User responsibility matters — images https://antimuseum.com Orange data centre in Val de Rueil

This analysis shows just how sophisticated you need to be when evaluating the environmental impact of AI. A ChatGPT query might actually replace multiple Google searches plus visits to various websites. That makes direct comparison pretty meaningless. This distinction will probably become moot anyway with the rollout of Google AI Overviews, which will integrate similar functionality to ChatGPT.

The Water Issue: Geography Matters More Than You Think

New data centre cooling techniques work in closed loops. Image antimuseum.com Orange data centre in Val de Rueil

Massive Geographical Differences

One of the most revealing parts of our interview dealt with data centre water consumption. Yves Grandmontagne draws a crucial distinction between American and European practices:

In the United States, when you install a data centre, you’re a private company reaching out to other private companies for water on one side, electricity on the other. And the utility companies that manage energy or water are thrilled to have a massive client that’ll absorb a chunk of their production.

This geopolitical insight explains those 5.4 million litres of fresh water attributed to ChatGPT-3 training. The issue isn’t the technology itself—it’s local practices and regulations. In Europe, our expert reminds us, “we’ve been developing infrastructure cooling systems that work in closed circuits or are air-based rather than water-based for quite some time”.

Take Orange’s data centre in Val de Rueil, for instance—it’s cooled by the crisp Normandy air. The only exceptions are during heat waves, which are naturally time-limited.

How Cooling Actually Works

Yves Grandmontagne’s technical explanation demystifies the cooling process: “Water acts as a conductor to capture heat.” He then breaks down the dual-circuit system that protects infrastructure while managing thermal exchanges. This approach reveals that warm water discharge from data centres (~20-25°C) stays well below that of nuclear plants (27-35°C for the Gravelines nuclear plant in the North of France). That gives us a useful comparison point for our debate.

Warm water discharge from data centres (around 20-25 degrees) stays well below that of nuclear plants (27-35°C for Gravelines)

We should note that these figures vary depending on location, time of year, and technological choices. A 12°C increase in the North Sea, even if reports show nothing concerning at the macro level, probably isn’t neutral at the micro level over the long term. Similarly, warm water discharges from data centres probably aren’t completely neutral either. Another point worth nuancing, though we should be cautious here.

The expert also highlights emerging technologies like Direct Liquid Cooling (DLC) and immersion, which operate in closed circuits and drastically cut water consumption. This technological evolution shows the sector’s ability to adapt to environmental challenges.

More importantly, the above claim about water consumption was only related to the training of ChatGPT. This operation occurs only once, unlike the everyday usage by individuals worldwide, multiplied by hundreds of millions of users. This leads to staggering amounts of consumption. Once again, this highlights our individual responsibilities regarding energy and water consumption in data centres and AI.

Moratoriums and Regulations: When Saturation Forces Limits

A power generator at the Orange Data Centre in Normandy (Val de Rueil) — image antimuseum.com

The Amsterdam, Frankfurt, and Dublin Cases

Looking at data centre moratoriums reveals some pretty contrasting situations. Yves Grandmontagne particularly highlights the Irish case. “Remember, Ireland was Europe’s poorest country 30-40 years ago [Editor’s note: the podcast figure is off]. To build an industry and attract capital, they found this solution.”

And it worked—Ireland’s GDP per capita now exceeds $100,000, putting it amongst the world’s best in 2025. [Editor’s note: there’s an infographic with sources at the end of this article]

Today, 30% of Irish energy production goes to data centres. Yves’s reaction? “That’s clearly way too much!” This extreme situation shows the risks of an unregulated approach, but also how governments can respond intelligently. “The Irish government didn’t say ‘no more data centres’—they decided to make new construction conditional on implementing solutions.”

The pragmatic approach adopted by the Celtic Tiger contrasts sharply with outright bans. It offers a middle ground between economic development and environmental constraints.

The Real Problem: Moving Energy Around

One of the biggest revelations from our interview concerns identifying the real bottleneck. “The real problem isn’t production—it’s the grid. The energy grid, meaning getting energy to where it’s actually consumed.”

This analysis puts debates about the environmental impact of AI in a whole new light. France, Yves Grandmontagne reminds us, “is completely energy self-sufficient in production.” But it faces transport infrastructure challenges. This technical perspective shows that solutions don’t necessarily lie in cutting consumption, but in optimising distribution.

Power generators kick in when there are supply issues — Val de Rueil data centre image antimuseum.com

Putting Numbers in Context: AI in the Global Energy Picture

That Telling 2%

On global statistics, Yves Grandmontagne sets the record straight: “Currently, we’re more like 2%. And we’re heading pretty fast towards 4%.” These figures, relative to global electricity consumption, give us some useful perspective.

The expert insists on keeping these percentages in context: “When you pull out a graph showing energy consumption across all industries, if you put industry and data centres side by side, you realise that the latter don’t weigh much.”

The Real Energy Consumers

To provide further context, Yves Grandmontagne lists the truly energy-intensive sectors: “There’s transport, industries, steelworks, agriculture… The latter consumes enormous amounts of energy. We often forget to mention this.”

This perspective reveals according to our research that the chemical industry represents 32% of French industrial energy consumption, offering a striking comparison point with the 2-4% of data centres globally.

Techno-solutionism: Promises and Realities

Let’s now turn to techno-solutionists and examine their claims clinically as well.

Sam Altman’s Declarations: Between Ambition and Pragmatism

Sam Altman’s declarations that “1% of global electricity to train powerful AI would be a massive victory” also find nuanced resonance with Yves Grandmontagne: “It’s true that powerful AI called ChatGPT will consume perhaps 70% of global AI consumption worldwide.”

This analysis reveals the economic reality behind the declarations: Microsoft invested 10 billion in OpenAI, creating a temporary quasi-monopoly situation. The expert underlines the mechanical nature of this consumption: “You need computing. Computing consumes energy. And since we use more and more of it, it consumes more and more energy. That’s normal.”

AI as Solution to Energy Problems

Satya Nadella’s (Microsoft) vision that “AI can be a powerful accelerator for addressing the climate crisis” finds technical justification with Yves Grandmontagne: “The real problem isn’t production, it’s energy transport.”

The expert explains that managing a complex energy mix (nuclear, renewables, fossil fuels) requires sophisticated piloting tools: “We can only do this efficiently, we know today, by using AI tools.”

This technical perspective reveals that AI isn’t just an energy consumer, but potentially an optimiser of the global energy system. However, Yves Grandmontagne tempers ambitions: “It will be a combination. It’s not one element alone that can help.”

It also seems highly doubtful to us to claim that AI, however much a “pharmakon” it may be, can be a poison that serves as a universal remedy to this problem that far exceeds it, as we’ve written previously.

Technological Innovations: Towards More Efficient Data Centres

Microsoft’s “Zero Water” Plan: Revolution or Evolution?

Microsoft’s announcement regarding its “zero water” cooling plan illustrates the sector’s technological evolution. Yves Grandmontagne confirms: “This isn’t greenwashing. It’s not really an initiative either—this stuff already exists.”

The expert details the new technologies needed to cope with the dramatic increase in power requirements:

When we were using conventional technologies until now, we had between 5 and 10 kilowatts coming into a rack. When you add AI, at minimum, you’re consuming 80 kilowatts or you’re consuming 150 kilowatts.
This progression forces the adoption of Direct Liquid Cooling or immersion, technologies that operate in closed circuits. “That’s the direction of technological evolution for cooling AI data centres,” he concludes.

Energy Efficiency: Remarkable Progress

A particularly revealing statistic emerges from the interview:

Over the last decade, the number of data centres has doubled, their floor space has quadrupled, and their energy consumption has only increased by 6%. This data illustrates considerable progress in energy efficiency, often overlooked in debates about the environmental impact of AI. It reveals the sector’s capacity to reconcile growth with energy optimisation.

AI Geopolitics: American vs European Stakes

The Geographic Concentration of Infrastructure

An often-overlooked aspect of the debate lies in AI geography. Yves Grandmontagne reminds us: “For now, all AI production happens in the United States, not Europe.” This geographic concentration puts European criticism in perspective whilst highlighting our technological dependence.

The expert mentions the €109 billion in AI investments announced by President Macron, of which “a third, or even half will concern data centres.” This strategy aims to reduce our dependence whilst developing a more efficient European sector.

American Challenges: Infrastructure and Regulation

Yves Grandmontagne’s analysis reveals the weaknesses of the American model: “In the United States, they have major weaknesses” regarding energy transport. This infrastructure failure partly explains the criticised overconsumption and justifies the different approaches adopted in Europe.

Meta’s project in Texas (5 gigawatts across an area equivalent to Manhattan) illustrates this problem: “Meta doesn’t have the energy, so it’s looking for it.” This situation contrasts with Europe’s more integrated and regulated approach.

AI: A Revolution Comparable to Fire? (Pichai)

Sundar Pichai’s declaration (Google) that “AI is more important than fire or electricity” finds nuanced resonance with Yves Grandmontagne: “It’s a real revolution that will go much further than previous ones, because those were more industrial. This one is a revolution with an impact on our daily lives.”

This analysis highlights the specificity of the AI revolution: its speed and penetration into daily life. “In barely two years, it’s been a real wave that’s engulfed us,” observes the expert, contrasting with the hundreds of thousands of years it took to adopt fire.

Employment and Usage Challenges

Beyond environmental questions, Yves Grandmontagne expresses his social concerns:

I’m amongst those who still worry that it will massively impact jobs, and I think we need to be realistic about this.

This social dimension of the environment reveals the complexity of trade-offs to come. The expert nevertheless encourages adoption: “We mustn’t cut ourselves off from this potential either, because extraordinary things will come to fruition soon.” It’s not easy to form an opinion on this employment issue given how contradictory the viewpoints are.

Recommendations and Critical Vigilance

Yves Grandmontagne’s final message to students (since this post was created in preparation for our course on AI and content creation) particularly resonates:

Be careful about the discourse you listen to, who’s behind it, etc. It’s easy to twist information
This warning against disinformation highlights the political instrumentalisation of the environmental debate.

The Digital Economy: A Forgotten Dimension

An often-overlooked perspective emerges: “When a data centre sets up in a city, in its wake, an entire digital economy develops. But we forget to mention that.” The expert quantifies: “One job created in a data centre means 100 jobs behind it that will be developed in the region.”

This economic dimension reveals the complexity of territorial trade-offs and the need for global rather than sectoral approaches.

In Conclusion: Keep Calm and Take a Step Back

Yves Grandmontagne’s expertise reveals a landscape far more nuanced than polarised environmental debates suggest. His main conclusions deserve to be retained.

The environmental impact of AI is “real, but largely overestimated,” in his words. This reality can be explained by several factors: the remarkable progress in energy efficiency, the relativity of consumption compared to other industrial sectors, and the constant technological evolution towards greater efficiency.

Geography matters enormously in this equation. American practices, often criticised, don’t reflect the more regulated and efficient European approaches. This geographical distinction invites us to contextualise criticism and encourage good practices.

The technological future is heading towards more efficient solutions: closed-circuit cooling, proximity AI, energy optimisation by AI itself. These developments suggest that current problems are transitional and technical rather than structural.

Finally, the systemic dimension revealed by this analysis invites us to move beyond sectoral approaches. AI isn’t just an energy consumer, but potentially an optimiser of the global energy system. This systemic perspective may be the key to truly sustainable artificial intelligence development.

Yves Grandmontagne’s message resonates as a call for nuance and critical vigilance: “Let’s remain cautious and serene.” In an often emotional debate, this technical wisdom offers precious guidance for navigating between the pitfalls of denial and catastrophism. This more nuanced discourse is undoubtedly less marketable than extreme opinions, which fuel coffee shop chatter and social media.

This interview ultimately shows that the question of AI’s environmental impact doesn’t have a simple answer, but requires a systemic approach that’s geographically situated and technically informed. It makes up an essential starting point for all those who wish to move beyond preconceived ideas and contribute to a genuinely constructive debate about the future of our digital society.

Infographic on AI’s Environmental Impact

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Will the AI bubble burst or is GenAI here to stay? The artificial intelligence industry is experiencing unprecedented financial euphoria. Yet, the current situation is very confusing. AI investments are reaching dizzying heights. Let’s mention OpenAI’s $40 billion funding round at $300 billion valuation and Mistral AI’s €1.7 billion funding round. Yet, some commentators are very critical of the situation. For instance, Ed Zitron predicts that the AI bubble will burst in Q4 2025. All this is fueling intense, rather than rational, debate. I wanted to confront these concerns with the expertise of Bernhard Schaffrik, Principal Analyst at Forrester Research. His analysis is insightful and nuanced. In his mind, there will be some sort of correction, but at the same time, GenAI is too popular to disappear.

When Will the AI Bubble Burst?

Is the AI bubble about to burst or is GenAI here to stay? Forrester’s Schaffrik predicts corrections but says GenAI is too popular to go — photo by Forrester.com

Forrester’s Bernhard Schaffrik is recognized as one of the most insightful experts in artificial intelligence. He provides a nuanced analysis that transcends simple financial considerations. His perspective on the AI bubble burst scenario offers first-hand insights for understanding where this transformative technology is truly heading.

The AI Bubble: Financial Reality, Technological Continuity

The question of a potential AI bubble burst cannot receive a univocal answer. As Bernhard Schaffrik rightfully points out, it all depends on one’s perspective. This duality of vision probably constitutes one of the keys to understanding the current situation and the likelihood of an AI bubble burst.

Like us, Schaffrik righfully points out that the main issue with AI is societal and philosophical — image generated with Adobe Firefly

“It’s almost impossible to get a one-sentence response from an analyst. Allow me two sentences. Number one is, of course, it always depends on the role or the profile you’re asking. If we are talking about financial investors, then yes, there are strong signals of this being a bubble because there is so much money being pumped into it—more than $120 billion US dollars in capital expenditure on AI infrastructure alone, just by the Magnificent Seven tech providers. So that bubble could burst,” explains Forrester’s expert.

This assessment gains particular relevance when considering Google’s $9 billion AI data center investment in Oklahoma for advanced AI training infrastructure.

This financial perspective, however, tells only part of the story. Technological adoption follows a different logic from financial markets, as Schaffrik confirmed during our exchange about the AI bubble burst potential.

“But now, if you put yourself in the shoes of enterprise decision makers, tech decision makers, also AI users, there are many who would say, ‘I don’t care if that bubble bursts, the technology is there, and it won’t go away.’

“Regardless of the amounts all the financial transactions surrounding the AI industry, people are actually using this technology. And they like what they are seeing. It might not be the disruptive, transformative value some are surmising. It’s probably more incremental than that, but the adoption of that technology is undeniable.”

The Revenue Challenge: A $25 Billion Gap to Bridge

Fortune’s analysis reveals a concerning gap between current investments and generated revenues. To justify current investments, AI companies would need to generate $40 billion in annual revenue, while they currently produce only $15 to $20 billion.

Schaffrik doesn’t believe in an AI bubble burst right now — image made with Adobe Firefly

I was wondering whether this $20-25 billion gap could represent a systemic risk that could trigger an AI bubble burst.

Schaffrik remains relatively optimistic on this point: “There is still enough money in that market to back these revenue gaps at least for a while. And what I’m also seeing is that especially when it comes to the largest enterpriseson the planet, they are convinced to continue using that software. And if it comes at a premium which is decent, arguably, maybe a couple percentage points higher than what they are paying today for the software, then this seems to be acceptable.”

This acceptance of additional costs by large enterprises stems from the incremental value they perceive, even if it hasn’t yet reached the promised transformation level that might prevent an AI bubble burst scenario.

LLM Regression: A Warning Signal?

A particularly troubling element in the current ecosystem is the recent NewsGuard study revealing that major LLM systems are no longer progressing but regressing, generating more hallucinations and errors. This observation raises fundamental questions about current technology maturity and its impact on AI bubble burst predictions.

“I’m not saying that LLMs and generative AI are progressing in a linear fashion nor that this technology will be disruptive in any way, despite the promise. As we have seen with emerging technologies for decades and even centuries, it takes breakthrough technological revolutions rather than evolutions to fulfill such promises,” analyzes Schaffrik.

This vision of the current limitations of AI doesn’t diminish Bernhard’s long-term optimism: “But I’m also convinced that these breakthroughs will happen, not within the next seven, eight, nine, 12 months, but maybe in the long term. Something else will be coming up.”

Energy Efficiency: The Achilles’ Heel of AI

One of Schaffrik’s most compelling criticisms concerns the energy efficiency of current systems. His comparison between the human brain and data centers is striking and relevant to understanding whether we’re facing an AI bubble burst.

“If we look at the amount of energy our brains are requiring to create a certain inference, and how much an LLM would require to achieve the same result with electricity, this cannot be the way forward.”

This energy inefficiency constitutes a major barrier to scalability and will require significant technological breakthroughs to overcome, potentially influencing AI bubble burst timing.

Pilot Failures: Business as Usual or Red Flag?

The 95% failure rate of corporate AI pilots revealed by MIT research might seem alarming and suggestive of an impending AI bubble burst. Yet Schaffrik places this figure in its historical context: “It’s quite normal. As an analyst covering innovation management, what I have been observing over time is that about 10% of all innovation-related minimum viable products, proof of concepts, pilots, will turn into a product.”

The problem would rather lie in unrealistic expectations: “Everybody rushed at it because one believed that since it’s accessible through natural language, it should be easier to deploy, to implement, and there are no drawbacks and negatives. That might explain that the failure rate is slightly higher than with technologies we saw in the past.”

This assessment aligns with Gartner’s prediction that 30% of GenAI projects will be abandoned after proof of concept by 2025 due to poor ROI and unclear business value.

AGI: The Next Revolution in Motion

Despite current limitations, Schaffrik maintains his bold prediction from his July 2025 analysis “Demystifying Artificial General Intelligence” that Artificial General Intelligence (AGI) represents “the biggest change in tech we have ever seen and is starting right now.” This vision, which could influence AI bubble burst scenarios, is structured around three maturity stages.

• “Competent artificial general intelligence is lurking around the corner. Our prediction is that between 2026 and 2030, we will see competent artificial general intelligence. You could think of it as your first trustworthy AI agent. You might not want to give it your car keys or your wallet, but it might do amazing things.”
• The subsequent stages would unfold over more distant horizons: independent AGI within the next five to ten years,
• And lastly, strategic AGI in a more distant future.

Current AI Limitations: Experience versus Training

A crucial point raised in our discussion concerns the difference between training and experience. As I pointed out to Schaffrik, experience develops critical thinking that current LLMs don’t yet possess, which could impact AI bubble burst predictions.

“We might get to a point where most of us humans wouldn’t be able to tell if on the other side, a human or a machine is interacting with us. There will be areas where we will still be able to tell. But experience is something we could at least partially solve with more data and better data,” states Schaffrik.

The solution, according to him, lies in massive data collection: “So much of the billions of investment money flowing now into all these big companies is also to collect and curate data also from the physical environment. Bringing all this data together, will create something that mimics experience.”

The Human Factor: What’s Left for Us?

The philosophical question of what humans will do when machines surpass us in thinking capabilities represents Schaffrik’s personal concern regarding potential societal implications of advanced AI, regardless of any AI bubble burst.

“That’s my personal doomsday scenario, I must say. It’s not good for us humans to just idle around. So it’s not so much a technical conversation, but more a political, societal, psychological and philosophical one. So I’m sure we are far away from this, but we are getting there.”

Leadership and Preparation: The Governance Challenge

Regarding leadership in this transformation, Schaffrik acknowledges the complexity: “Rulers are supposed to rule. The question is more like, are they intentionally gathering a diverse set of experts who would be able to consult them? Technically, this is possible. Are they willing to? It’s another question.”

His confidence in human adaptability remains intact: “I’m still confident that once we are realizing the true dangers of certain technologies, we will start to rethink. And we have always found a way to move forward, and we will find a way this time as well.”

Conclusion: Beyond the AI Bubble Burst Debate

Our conversation with Bernhard Schaffrik reveals that the AI bubble burst question transcends simple financial considerations. While financial markets may indeed experience corrections, the underlying technology continues its unstoppable advancement.

The key insight is that we’re witnessing a fundamental shift that will persist regardless of market volatility. Schaffrik’s analysis suggests that rather than fearing an AI bubble burst, we should focus on preparing for the transformative changes ahead. The technology won’t disappear, but it will evolve in ways we can barely imagine today. As we stand at this inflection point, the question isn’t whether the AI bubble will burst, but how we’ll navigate the profound societal and technological transformations that lie ahead.

The AI bubble burst debate, ultimately, is just the beginning of a much larger conversation about our future with increasingly capable technology.

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The world of artificial intelligence is evolving at breakneck speed, and nowhere is this more conspicuous than in the emergence of AI agents. As organizations grapple with separating genuine innovation from marketing hype, we sat down with Ed Keisling, Chief AI Officer at Progress Software, to cut through the noise and understand what AI agents really mean for businesses today. Ed brings a unique perspective, having taken on his new role in February 2025 at a time when the industry is proclaiming this as “the year of agents.” His insights reveal both the tremendous potential and the current limitations of this transformative technology. As always, time is of the essence.

AI Agents, Beyond the Hype

Preogress Software’s Ed Keisling did a great job debunking the myths surrounding AI Agents and showing what the future holds beyond the hype – photo Progress Software.

The Rise of the Chief AI Officer: A Strategic Imperative

The creation of Chief AI Officer roles across the technology industry signals more than just a trend—it represents a fundamental shift in how businesses view artificial intelligence. As Ed explains, “AI needs to be a strategic pillar of a business to drive innovation and growth. It really reflects the pace at which technology is evolving, and having somebody that is accountable to follow all these latest updates and really look at it through the lens of new risks and opportunities.”

This observation resonates with the broader digital transformation patterns we’ve witnessed over the past decade. Just as Chief Digital Officers emerged to guide organizations through the digital transformation revolution, Chief AI Officers are now stepping up to navigate the AI transformation. The role isn’t merely about implementing technology—it’s about strategic thinking, risk assessment, and identifying genuine business opportunities in a rapidly changing landscape.

AI agents: the promise with tools like Manus is that they would behave like your favourite dog. Go search, Rover…! — photo by https://antimuseum.com/

Defining AI Agents: Beyond the Buzzwords

One of the most persistent challenges in the AI space is the confusion surrounding terminology. AI agents, in particular, have become an overloaded term that means different things to different people. Ed provides valuable clarity by positioning agents on a spectrum of AI capabilities.

“When generative AI came out, it was generally reactive,” Ed notes. “We would go to ChatGPT, provide a prompt, and it would generate a response based on its training patterns. Agents are moving along that spectrum in terms of capabilities—they have the ability to perceive their environment, access to audio, video, documents, and crucially, the ability to reason, plan, and learn from their actions.”

Unfortunately, Rover isn’t always willing to search in the right direction… — photo by antimuseum.com

Traditional automation relies on strict rule-based systems—the digital equivalent of if-then-else logic. Chatbots, while more sophisticated, remain predominantly reactive. AI agents represent a step toward proactive, reasoning systems that can adapt to changing circumstances.

AI agents represent a step toward proactive, reasoning systems that can adapt to changing circumstances

The evolution doesn’t stop there. Ed introduces the concept of “agentic AI“—a broader paradigm where multiple agents collaborate, passing context between each other to accomplish complex tasks. This represents the holy grail of AI automation: systems that can dynamically adapt to real-time situations without constant human intervention.

Reality Check: Why Perfect Automation Remains Elusive

Despite the exciting potential, Ed provides a sobering reality check about current capabilities. His observation about the Pareto principle in AI is particularly insightful: “AI is the ultimate manifestation of the 80/20 rule. You can very rapidly get to value with 20% of the work achieving 80% of the results, but actually getting it to work 100% of the time is still very, very difficult.”

This phenomenon explains why AI demonstrations look so compelling while real-world implementations often fall short of expectations. The gap between proof-of-concept and production-ready systems remains significant, requiring careful planning, clean data, and well-defined business processes. As always, I should add, “the more it changes, the more it stays the same,”as the French poet would have it.

RAG Technology: Making AI Practical for Business

While pure AI agents may still be evolving, Progress Software’s acquisition of Nuclia, an agentic RAG (Retrieval Augmented Generation) provider, demonstrates a more immediate and practical application of AI technology. Ed explains the fundamental problem RAG solves: “Large language models have been trained on the entirety of the Internet, giving them broad general knowledge, but they don’t have access to data stored behind firewalls or on personal computers.”

This limitation is critical for businesses. While public AI models are impressive, their real value emerges when they can access and reason about proprietary business data. RAG technology bridges this gap, allowing organizations to leverage AI’s reasoning capabilities while grounding responses in their specific knowledge base.

The practical implications are significant. As Ed points out, “Small to medium-sized businesses have lots of unstructured data—audio, video, log files, recordings, PDFs, charts—that represent proprietary business value, but they have no way of indexing or finding or correlating the data within it.” RAG technology makes this data accessible and actionable.

It’s high time to stop AI-Washing Ed Keisling advises — image created with Midjourney

Separating Innovation from AI Washing

Ed’s experience at the AI4 conference provides valuable insights into the current state of the AI industry. His observation about AI washing is particularly relevant: “There was a lot of AI washing—companies that weren’t sure they understood the problem to be solved, with very thin wrappers around language models to solve point problems. It felt like a hammer looking for a nail.”

The key differentiator, according to Ed, lies in problem-solving focus rather than technology-first thinking. “AI allows you to solve old problems in a new way and to make seemingly impossible problems possible. You’re thinking about how to drive outcomes—making developers more productive, automating tedious workflows, providing better insights that weren’t possible before.”

This perspective aligns with successful technology adoption patterns throughout history. The most successful implementations focus on specific business outcomes rather than showcasing technological capabilities.

Real-World Value: ShareFile’s Document Intelligence

Progress Software’s ShareFile platform provides concrete examples of AI delivering measurable business value. The platform serves client-facing teams in regulated industries—doctor’s offices, law firms, and tax accountants—where document management is critical but time-consuming.

The AI implementations are practical and measurable: “We can create curated lists of documents appropriate based on your situation, and as you upload documents, we can figure out which document relates to which checklist item. We’ve measured this at being three and a half times faster.”

sharefile-legal-document-workflows-for-legal

More importantly, the system addresses security concerns that many organizations face: “We have capabilities that scan for social security numbers, personal information, and credit card information that you didn’t want to upload. This single capability flags around 35,000 documents a week.”

These examples demonstrate AI’s sweet spot: automating routine tasks while enhancing security and accuracy. The value isn’t just in speed—it’s in freeing professionals to focus on high-value work instead of admin tasks.

The Human Factor: Reskilling Rather Than Replacing

One of the most contentious aspects of AI adoption concerns workforce impact. Ed’s perspective is refreshingly pragmatic: “This is a fundamental reshaping of how business is done—a new skill and opportunity for people to grow, learn, and reinvent themselves. There aren’t experts who have been doing this for five or ten years. If you have the headspace and desire, you can become that expert.”

This view positions AI as an enabler rather than a replacement. The technology’s real power lies in eliminating organizational silos and enabling individuals to accomplish more with the right tools and training. “One person is now going to be capable of doing multiple things with the right prompts, giving them opportunities to do more and drive more value for the organization.”

The message is clear: organisations with infinite backlogs of valuable work don’t need to fear AI displacement. Instead, they should focus on upskilling their workforce to leverage these new capabilities effectively.

Looking Forward: Practical Adoption Strategies

Ed’s recommendations for AI adoption focus on practical, incremental approaches rather than transformative leaps. “There’s enormous green space for individuals to become fully enabled with AI. The majority of people using AI today are using it in a Google-like fashion, but they haven’t taken time to understand how to correctly prompt agents or use advanced capabilities.”

The most successful implementations start with individual productivity tools—document summarization, email assistance, and internal search capabilities—before advancing to more complex agentic systems. This approach allows organizations to build AI literacy while demonstrating concrete value.

In Conclusion, Embracing Reality While Preparing for the Future

Our conversation with Ed Keisling reveals that AI agents represent both enormous potential and significant current limitations. While the vision of fully autonomous AI systems remains largely aspirational, practical applications of AI technology are already delivering measurable business value.

The key insight for business leaders is the importance of realistic expectations coupled with strategic preparation. AI agents are not yet ready to replace human workers, but they are already transforming how work gets done. Organisations that focus on practical applications, invest in workforce development, and maintain healthy skepticism about vendor promises will be best positioned to benefit from this technological evolution.

As Ed concludes, “You have to put your personal opinions and biases aside and accept and lean into it. At least you can be part of the process and conversation and understand where the edges are.” This balanced approach—embracing the technology while maintaining critical oversight—represents the most reasonable path forward in the age of AI agents.

The future of AI agents is being written today, not in grand demonstrations of artificial general intelligence, but in the practical applications that solve real business problems, one automated workflow at a time.

About Ed Keisling

Ed Keisling is the newly appointed Chief AI Officer at Progress Software Corporation, bringing over three decades of technology leadership experience. He previously served as Senior Vice President of Engineering for Infrastructure Management at Progress, was an executive team member at Vecna Technologies overseeing Engineering, IT, DevOps, Support, Program Management and Analytics, and spent over 17 years in senior engineering roles at Pegasystems. Specializing in complex system architectures, cloud computing, and large-scale infrastructure management, Keisling also mentors through the UNH Pathways Program and MIT’s Undergraduate Practice Opportunities Program (UPOP). In his new CAIO role, he leads Progress’s AI strategy and product portfolio transformation, reporting directly to CEO Yogesh Gupta.

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In an era of overtourism, where mass travel increasingly strains destinations worldwide, Christopher Hill offers a compelling alternative with his voluntourism/volunteer travel business, Hands-Up Holidays. As a founder and managing director of this company, Hill has built a business model that demonstrates how travel companies can be forces for good rather than exploitation. His approach to volunteer travel challenges the conventional wisdom that luxury and social responsibility cannot coexist.

Voluntourism: When Volunteer Travel and Luxury Coexist For Mutual Benefit

Voluntourism is a portmanteau expression combining “volunteer” and “tourism” — Photo from Hands Up blog post on Earth Day Eco-Luxury Inspiration (Mexico – conservation of turtles in Baja California by Christopher Hill)

What makes Hands Up Holidays’ philosophy particularly noteworthy is its commitment to controlled growth, prioritising quality experiences over scale. Rather than pursuing rapid expansion that could compromise his mission, Christopher Hill maintains personal oversight of every client interaction, proving that sustainable business practices can create more meaningful outcomes for travellers and communities alike.

Operating across over 30 countries, Hands Up Holidays represents a fascinating case study in how the apparent contradiction between luxury accommodations and volunteer work can enhance both experiences.

Here is the account of our interview.

What kind of work are your clients doing during their volunteer travel?

We offer a great variety of projects. Our most popular initiatives are building projects, which can range from small-scale but highly tangible endeavours like constructing or installing eco-friendly stoves in village homes to larger undertakings such as helping build houses or renovating school classrooms.

Beyond construction, we focus heavily on wildlife conservation projects, where families might care for elephants or participate in sea turtle protection programs. The third major area involves educational support, particularly serving as reading partners in local schools. Each project is carefully selected to ensure meaningful impact while being suitable for family participation.

Nayara Tented Camp – The tented camp was built on stilts and a lot of space was left between tents to plant trees and palms between them. Thousands of trees and indigenous bushes have been planted to reforest and repair damage done by cattle farmers. Energy and water conservation measures are in place. The majority of the team is from the local town, and free transport and health services are provided.

What triggered your shift from London finance to voluntourism?

It was quite a dramatic shift, and in true dramatic fashion, I experienced my own road to Damascus moment in South Africa. This happened about six years into my career in London’s financial sector. During a trip there, beyond the traditional safari experiences and stays at beautiful lodges throughout Cape Town and the Garden Route, I participated in building a house for a family in one of the townships. This experience was genuinely life-changing in two fundamental ways.

First, it enabled me to interact authentically with local people, gaining real insights into their lives and sharing stories with them – something that had been missing from my previous travels despite being quite fortunate to travel extensively. Second, the satisfaction of helping and making a tangible difference in their lives by providing this family with a proper home was profound. This experience made me realise that I had developed solid business skills but wanted to apply them to something more meaningful and fulfilling. That became the catalyst for establishing what would become Hands Up Holidays three years later.

How did you safely visit townships when tourists are typically advised to avoid them?

I should emphasise that there are legitimate reasons for caution. I was fortunate to be in the capable hands of my former London flatmate, who had moved to South Africa and become a professional tour guide, developing his own network of trusted relationships there.

He was the one who took me into the townships, and I certainly wouldn’t recommend just showing up there independently. While chances are you’d be fine, you need to remain cautious. I should also mention that there’s a concerning trend of township tourism that can devolve into mere voyeurism, which we absolutely oppose. However, there are ethical township visits that focus on the positive developments happening in these communities and provide genuine opportunities for meaningful interaction.

How do you reconcile the apparent contradiction between luxury and volunteer work?

Luxury and volunteering don’t immediately seem like natural partners. However, when you examine it more deeply, the luxury component serves as the means to facilitate participation from people who want to make a difference but aren’t willing to sacrifice comfort. This certainly isn’t for everyone, but our model is what I call ‘philanthropy volunteering’.

The primary benefit comes from the funds our clients bring to projects. If providing luxury accommodations and creature comforts enables those funds to be invested in meaningful projects, then we’re the right organisation for them. Conversely, if you don’t mind basic conditions, there are many other fantastic organisations that will help you make a difference in that way.

How do you avoid voluntourism becoming voyeurism?

There are two main approaches we use. First, I personally visit every single project we offer, so I can genuinely attest that they’re beneficial and provide real value to recipients, whether communities or wildlife. Second, this connects to my earlier point about different ways to make a difference. People can contribute through time – spending weeks or months at a project – or through specialised skills, like doctors or physiotherapists applying their expertise. The third way is through funding, which is where we excel. We enable our guests to experience projects and gain that meaningful interaction, but their primary benefit comes from providing the funding to build houses, construct stoves, or create accessible facilities, whatever the specific need may be.

How do you convince families to choose volunteer travel?

I’d argue it’s primarily the parents’ responsibility rather than mine. However, I think it’s important to understand that no one arrives on our trips surprised to discover they’ll be renovating a school. This volunteer component is our fundamental point of difference – it’s what we specialise in and what sets us apart. People only choose us because this is exactly what they want to do. I hope families have these discussions with their children well in advance of booking.

What motivates your clientele differently from typical travel agencies?

Absolutely, they’re very different. When I first established Hands Up Holidays, I had people like me in mind – young professionals who were cash-rich but time-poor, wanting good vacations while making a difference. However, from the very beginning, we attracted family bookings, which hadn’t been on my radar at all when I was developing the concept. When I asked these families about their motivations, they’d say things like, ‘Our children come from privileged backgrounds, and we want them to appreciate how fortunate they are,’ or ‘We’re seeking a meaningful family bonding experience.’ Many also express that they want to inspire their children to become the next generation of changemakers. So yes, there’s definitely a strong mission-driven aspect in our family clients’ thinking when they make enquiries.

Are your clients younger or older than you expected?

They’re older than I anticipated. When I wrote the first business plan and brochure, I was targeting young professionals aged roughly 25 to 35. While we do attract some clients in that demographic, I was genuinely surprised by the number of families booking with us. These family clients are typically in their 30s and 40s.

How does volunteer travel address overtourism, and what’s the demand?

We take a holistic approach to all our trips, with sustainability integrated throughout. While our trips are luxury experiences, we prioritise properties that demonstrate sustainable luxury principles in their design and operations. We recommend restaurants offering organic dishes sourced locally whenever possible, maintain a policy of using only local guides, and choose eco-friendly transport options where available. This approach helps combat overtourism. We also encourage travel to safer but less mainstream destinations – places like Georgia in the Caucasus, Belize, or Roatan in Honduras, which we’re launching in the coming weeks. These destinations aren’t overcrowded with tourists. Additionally, incorporating volunteer components naturally slows down the pace of travel. Instead of rushing from site to site, you’re investing several days in one particular destination and community.

How do you ensure controlled growth while maintaining quality over scale?

For me, the key is maintaining this as a passion project. I live and breathe this work, and I personally handle all customer enquiries. This isn’t just about passion – I genuinely delight in crafting unique itineraries for our clients – it’s also about quality control. I’m happy that it’s just me managing this aspect, and by virtue of that personal involvement, it naturally limits how much the business can scale. This constraint actually serves our mission perfectly.

Voluntourism: Small Is Beautiful and Meaningful

The volunteer travel market in which Christopher Hill’s Hands Up Holidays operates represents less than 0.01% of the global tourism industry’s USD 11.7 trillion annual revenue (World Travel & Tourism Council, Future Market Insights). The numbers may look small, but the company is nonetheless showing the way for the reinvention of the travel industry, which sorely needs it.

Walk through any popular tourist destination nowadays, and you’ll see tourists seeking familiar food. This isn’t criticism – it’s human nature. But it highlights how we travel without truly connecting.

Tourism is bringing people to destinations people think were made for them
but would be better without them.

And this is sad. This isn’t what ‘travelling’ is about. It’s about connecting, rubbing shoulders with the locals, understanding or trying to grasp foreign mores, tasting local food, etc. Hill’s approach differs greatly in that.

The voluntourism/volunteer travel model won’t transform the entire industry overnight. But it proves alternatives exist. Travel can serve communities rather than exploit them. And you don’t need to rough it for that matter. Lastly, growing a business doesn’t require sacrificing one’s values.

Hill shows that meaningful travel is possible. Not revolutionary, just different and respectful.

Here are a few numbers collected about voluntarism/volunteer travel

The following facts and figures were gathered with the help of Perplexity and checked against their sources. Errors may have occurred, readers are advised to double check the numbers before quoting. All sources are available at the end of this blog post

Volunteer tourism (voluntourism)

• Market size in 2024: Between USD 873 million and USD 962 million [2] [9].
• Projected market size for 2025: About USD 962 million to USD 1 billion [2] [7].
• Projected market size by 2030: Between USD 1.2 billion and USD 1.55 billion, depending on the growth rate used by different sources [2] [1] [9].
• Annual growth rate (CAGR): Most sources agree on a growth rate between 4.8% and 6.21% yearly through 2030 [1] [6] [7] [9].

Key participant demographics and trends

• An estimated 1.6 million people volunteer abroad each year [6].
• The youth market (ages 15–29) is a major driver, with this age group representing approximately 23% of all international tourism arrivals [6].
• Popular activities include community development, environmental conservation, teaching, healthcare, and cultural exchange [5].
• The main destinations are developing countries in Asia, Africa, and Latin America, but Europe and North America also see significant participation, especially for environmental and social projects [5] [7].

Regional insights

• Europe accounts for a sizeable share due to its large youth traveller population and established gap year culture [6].
• North America (notably the US and Canada) and Asia-Pacific (notably Japan, South Korea, and China) are also significant regions for volunteer tourism growth and participation [7].

Caveats

• The above numbers primarily refer to the economic value of the voluntourism industry and not the total number of trips or individual travellers.
• COVID-19 disruptions affected international travel and temporarily slowed market growth, but current estimates show a strong rebound in recent years [7] [6].

Overall, volunteer tourism continues to grow as travellers seek more meaningful, responsible, and impactful travel experiences [3] [6].

Sources

[1] Volunteer Tourism Market Size, Share & Growth Report, 2030 https://www.grandviewresearch.com/industry-analysis/volunteer-tourism-market-report

[2] Volunteer Tourism Market Size & Forecast [2033] https://www.globalgrowthinsights.com/market-reports/volunteer-tourism-market-118068

[3] Volunteer Tourism Global Business Report 2025 | Cultural https://www.globenewswire.com/news-release/2025/05/07/3076249/28124/en/Volunteer-Tourism-Global-Business-Report-2025-Cultural-Immersion-Experiences-Drive-Adoption-of-Long-Term-Volunteer-Tourism-Itineraries.html

[4] Volunteer Tourism Global Business Report 2025 https://uk.finance.yahoo.com/news/volunteer-tourism-global-business-report-133500797.html

[5] Volunteer Tourism Market Decade Long Trends, Analysis … https://www.archivemarketresearch.com/reports/volunteer-tourism-market-7751

[6] The European market potential for volunteer and … https://www.cbi.eu/market-information/tourism/volunteer-and-educational-tourism/market-potential

[7] Volunteer Tourism Market Size & Share Forecasts https://www.fundamentalbusinessinsights.com/industry-report/volunteer-tourism-market-13216

[8] Purposeful travel in 2025: The changing face of voluntourism https://fooddrinklife.com/volunteer-tourism/

[9] Volunteer Tourism https://www.marketresearch.com/Global-Industry-Analysts-v1039/Volunteer-Tourism-41409685/

[10] Voluntourism – Tourism and Travel: A Research Guide https://guides.loc.gov/tourism-and-travel/voluntourism

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AI is radically transforming the B2B sales landscape and accelerating the shift towards intelligent sales enablement. At a major B2B event which took place in Paris in July 2025, I met with Stephane Renger, co-founder and managing director of Salesapps. The leading European sales enablement vendor has placed AI at the heart of its innovation strategy. In this interview conducted at the event, Stephane explains how AI agents are revolutionising commercial efficiency whilst maximising security and privacy. A fascinating dive into the future of AI-powered sales enablement that’s redefining commercial performance standards.

AI Saves Time for Sales and Marketing Teams

AI-powered sales enablement : Stephane Renger is the co-founder and General Manager of European sales enablement company Salesapps.

How is AI transforming sales enablement and sales in general?

Stephane Renger: At Salesapps, we’re working on AI agents with the goal of bringing greater efficiency to sales teams, while maximising data privacy and security.

Specifically, what do these AI agents do?

S.R. We developed three types of intelligent agents for our AI-powered sales enablement solution.

• The ‘company profiler‘ AI agent analyses the targeted business regarding its strengths, weaknesses, products, competition, news…,
• The ‘individual profiler’ agent describes the buyer’s profile: background, interests, pain points… Then we use content enrichment agents with metadata to generate sales presentations and pitches,
• Finally, our ‘conversational agent’ restructures meeting minutes and reports.

Once again, this major B2B event took place in the prestigious premises of the Parc des Princes in Paris, with a focus on AI-powered sales enablement.

What are the efficiency gains from the implementation of AI within sales enablement?

S.R. They are quite blatant, mainly in terms of time savings. Thanks to AI, salespeople quickly access information that used to take hours to research.

For a complete sales team, there is at least 20% time saving, which equates to one working day every week. Marketing teams and content managers can save up to 50% of their time.

Is this an opportunity for getting rid of salespeople?

S.R. No, it’s not. Salespeople only spend one third of their time actually selling. Two thirds are devoted to paperwork, CRM, preparation. Automation frees up their time from what isn’t their core business.

Are certain sales roles more threatened than others?

S.R. It depends mainly on the products being sold. The buyer only spends 5% of their time with the salesperson, 80% of the purchasing process happens on the web. So the remaining 5% must involve a complex buying process to require human intervention. In other cases, self-service is way sufficient, even in B2B.

In a nutshell, Sales Enablement becomes AI-Powered Sales?

S. R. For the past two years, we’ve been talking more about ‘AI-powered sales enablement’ than traditional Sales Enablement. This concept is more immediately understandable and evokes innovation in the commercial approach. Worthy of note, for English-speaking markets, we keep that term ‘sales enablement’, but for French-speaking markets, ‘Modern Selling’ is the term of choice. It is more meaningful, especially when discussing AI agent integration, for French-speaking audiences who do not always understand the word ‘enablement’.

What can you tell us about reliability and security concerns?

S.R. Classic AI models, be it Gemini, ChatGPT, or others are used to research public information. If I want to know who you are or what your company does, there’s nothing better than such a tool that will browse the web, LinkedIn, and other sources to obtain this information. But if I want to process proprietary information, an internal document database, some meeting minutes, an internal conversation, etc., we must ensure data privacy and security at all cost. In this case, we’d rather work with on-premise language models that we can monitor end-to-end. We’ll feed them with this internal information and secure that information.

What are your views regarding the future of AI-powered Sales Enablement?

S.R. It’s very difficult to predict the future even three to six months from now. Our focus revolves around artificial intelligence. Its performance is increasingly impressive and prices more affordable. Right now, I’m particularly focused on sales coaching and training, and commercial skills development. Simulation and capturing a real-life interview in audio is already possible, but video is still relatively expensive, almost €50 per hour. With the lightning-fast progress in synthetic voices and images, I’m convinced that within three to six months, we’ll be able to offer much more advanced technologies at prices that will allow widespread distribution of this type of agent to sales populations and businesses.

In Conclusion: The AI-Powered Sales Revolution

These past ten years, Visionary Marketing has written quite a few pieces and white papers on the subject of Sales Enablement. Since then, technological progress and sales team transformation have been lightning fast. This interview with Stephane Renger perfectly illustrates this profound transformation of the sales domain.

AI-powered sales enablement is now fully operational, and this technology is redefining commercial efficiency. The productivity gains quoted by Salesapps — 20% for sales teams, 50% for marketing teams — are a sign that something serious is happening now.

What struck me at this major B2B event in Paris, was the profound transformation of the market offering in this domain. A few years ago, this side of the water, many Sales Enablement vendors were present, either international or local. In just a couple of years, Salesapps* established itself as the undisputed leader in this sales enablement market in French-speaking countries and is now conquering other markets.

The future looks exciting: between conversational AI agents, personalised coaching, and predictive analysis, tomorrow’s salesperson will have tools at her disposal of unmatched power. We haven’t yet seen the end of the evolution of the sales function, and digital technologies are playing a major role in this upheaval.

[*Disclosure: Visionary Marketing worked for Salesapps in 2022]

The post AI Sales Enablement: 20% Time Savings for Sales and 50% for Marketing appeared first on Marketing and Innovation.

Are AI and developers the world’s best friends or is artificial intelligence a threat to the future of programmers? As artificial intelligence models are becoming increasingly sophisticated, many questions are raised about the future of developers across the industry. Will AI replace programmers entirely, as Eric Schmidt and Dario Amodei are predicting? Will junior developers be facing extinction, as Steve Yegge surmised in a now-famous blog post? Or are we witnessing the dawn of a new era in which technology amplifies human creativity rather than replacing it? I interviewed Nathaniel Okenwa, Developer Evangelist at Twilio, to pick his brains about this question, and his conclusion is that, in the future, software development will undoubtedly remain human-driven even though many changes will occur. The video recording of that interview is available at the end of this blog post.

Developers and AI: the Path to the Future of Coding

With nearly a decade of hands-on programming experience and a unique perspective on developer community engagement, Nathaniel Okenwa brought both technical depth and strategic insight to this conversation about the evolving landscape of software development.

Spreading the Gospel of Developer Tools

“My parents celebrated when I got that job title of developer evangelist,” Okenwa said. “I speak and meet with developers, online or in person, and I talk about the tools and the technologies they’re using. A part of this job is being with the community and then spreading the good news of Twilio as well.”

AI and programmers, a love-hate relationship? — image https://antimuseum.com

For those unfamiliar with the company, “Twilio is a customer engagement platform and one of the providers helping businesses with their customer support, communication tools, and APIs.”

The Junior Developer Dilemma

The elephant in the room for the Tech industry is the fate of junior developers. Steve Yegge’s provocative piece ‘The Death of the Junior Developer’ has sparked intense debate, suggesting that AI won’t make inexperienced developers smarter but will enable experienced programmers to eliminate the need for juniors altogether. A daunting perspective for young programmers.

Nathaniel offers a more nuanced perspective that challenges this binary thinking .

Often, a company doesn’t hire junior developers for their current capabilities. They recruit them because they’re investing in what they will become in the future. Junior developers need to exist if we are going to have mid-level senior developers, developer leaders, and architects at a later time.

Nathaniel is right. Programming isn’t just about syntax and algorithms; it’s about developing problem-solving instincts, understanding business contexts, and learning to translate human needs into technological solutions.

‘If you want to create the next generation of builders, then I don’t think junior developers are going to disappear in the long term. We may forget how important they are for a little bit, but they will definitely make a comeback later on.’

The Eric Schmidt Prophecy: Six Months to Obsolescence?

The urgency of these questions intensified when Eric Schmidt, former CEO of Alphabet, made bold predictions about the timeline for developer displacement. His assertion that developers would be reduced to merely correcting AI output within six months and potentially eliminated entirely within a year sent shivers down the spines of the programming community.

Nathaniel acknowledges the partial truth in Schmidt’s predictions while advocating for a more sophisticated understanding of what developers do. “I think there are elements of truth in it, but I think the situation is a bit more nuanced. AI is, in my mind, another Industrial Revolution. In this context, it means we’ll be looking for repeatable tasks that are extremely simple and how to replace them with technology.”

AI and developers: programming isn’t just about writing code, it’s about solving business issues — image of a banker in a large banking institution https://antimuseum.com

The industrial revolution analogy is particularly apt here. Just as mechanisation didn’t eliminate human work but transformed it, AI appears poised to reshape rather than replace programming roles. “I think AI is going to take some aspects of programming and make them so cheap from an effort perspective that it’s not necessarily going to be the best use of people’s time. However, I think developers aren’t just folks that are repeatedly solving minor syntax sentences. They are creative builders coming up with different ways of taking a real-world problem and abstracting it into technology pieces.”

AI and Developers: The Abstraction Ladder

One of the most compelling aspects of Nathaniel’s perspective is his emphasis on abstraction as the key to understanding how AI will transform development work. Rather than replacing developers, AI represents another rung on the abstraction ladder that programmers have been climbing for decades.

Right now, I can use my programming skills to build a website and serve it to millions of people on the Internet. Thirty or forty years ago, I would have needed a whole set of different skills to make that happen. I would have needed so many more hardware skills and so many more specific high-level networking skills. And all of those things have been abstracted away for me to really focus on making this website really fast and performant.

This historical perspective illuminates a pattern that AI-anxious people are missing. Each generation of developers has built upon increasingly sophisticated foundations, allowing them to tackle more complex problems without getting bogged down in lower-level implementation details.

AI, the printing press and developers. A brilliant analogy by Twilio’s Nathaniel Okenwa — image produced with Midjourney

The printing press analogy further clarifies this progression: “If we think about the printing press, at first you needed to have lots of people who would sit down and handwrite a book in order for you to make 100 copies. The printing press came around, and the amount of effort and skills to achieve that shrunk considerably. But you still needed someone who could run that printing press.”

The Inconvenient Truth: Not Everyone Ascends

However, this progression toward higher abstraction levels raises uncomfortable questions about inclusivity and capability. Not every developer possesses the intellectual agility to continuously climb the abstraction ladder, and there’s value in acknowledging this reality.

Nathaniel addresses this concern with characteristic optimism while maintaining realism. “I suppose there will always be people who remain comfortable doing what they are doing in the ways they are doing it. But with technology making so many more different things available, what’s going to happen is users, customers, and the general public are all going to expect more from our technologies and from us.”

The market forces driving this evolution are relentless. As AI enables higher-quality experiences at scale, customer expectations rise accordingly, creating pressure on all technology providers to evolve or risk irrelevance.

“The folks who aren’t meeting these higher standards of experiences will not be able to deliver the value that their customers and employers are expecting from them. If they don’t continue to meet that bar of expectation that is growing higher and higher, especially as AI helps people to develop new ways of doing this, they will be left behind.”

The Digital Transformation Paradox

This raises an interesting paradox about digital transformation that I’ve observed throughout my career in technology consulting. Thirty years ago, we predicted that traditional industries like banking would be disrupted by digital-native competitors. Yet established banks have largely survived, adapting gradually while maintaining their market positions.

Nathaniel offers an insightful perspective on this seeming contradiction: “It didn’t kill banks, but I would argue that even if maybe they took a while to get there, the way we interact with our banks is completely different from the way we interacted with banks when we were younger. There are 18-year-olds who have no idea what a chequebook is.”

The transformation happened, but more gradually and less dramatically than predicted. This pattern suggests that AI’s impact on development may follow a similar trajectory—profound but evolutionary rather than revolutionary.

The GitHub Co-pilot Paradigm: Integration Over Replacement

Perhaps the most practical insight from our conversation concerns how AI tools are being adopted by developers. The success of GitHub Copilot and similar tools demonstrates that integration beats replacement as an adoption strategy.

“Sometimes there are engineers who are reluctant to use AI or don’t want to go out of their way to bring AI into their workflows. But when Copilot came out, when other tools that have that technology built into the applications, the interfaces they are already using, the adoption increases significantly.”

This observation reveals a crucial truth about technology adoption: the most successful innovations enhance existing workflows rather than demanding entirely new ones. The future of development tools lies not in replacing programmers but in making them more effective within familiar environments.

The Copy-Paste Continuum

One of the most honest moments in our conversation addressed the reality of code reuse – something every developer practises but few discuss openly. The fear that AI will turn programmers into mindless copy-paste operators misses the historical context of how developers have always worked.

“Copying and pasting has been an integral part of the engineer’s journey for decades, and it’s not going anywhere soon. Even if we are copying and pasting from a different place, and even if we are no longer using Ctrl-C and Ctrl-V, we have always been learning from the code that others write.”

The key distinction lies not in the source of solutions but in the developer’s understanding of what they’re implementing. “The real issue is that of developers who go and find a solution online and copy and paste it without understanding what is going on. Now, if people do that with AI, they may create a black box, and that’s going to be great when it works. But when it doesn’t, when you receive a phone call in the middle of the night, and that production’s gone down, that’s a different kettle of fish. Having a black box is not going to be something that a huge company, a huge enterprise, is going to want to rely on.”

The COBOL Conundrum: Legacy Systems and Human Resistance

Our conversation touched on one of the most persistent challenges in enterprise software: the prevalence of legacy systems built in languages like COBOL that have resisted modernisation for decades. This serves as a fascinating case study in the relationship between technological possibility and human decision-making.

I think we underestimate the power of people. People are sometimes afraid of change. They sometimes want to rely on what has worked for years. And so, even if AI presents a great solution, it’s going to be people who decide whether to take it on board or not.

This observation cuts to the heart of why technological predictions often prove overly optimistic. Technical feasibility doesn’t guarantee adoption, and institutional inertia remains a powerful force in shaping the pace of change.

Advice for the Next Generation of developers

For students and emerging developers wondering how to navigate this uncertain landscape, Nathaniel offers pragmatic guidance that emphasises exploration.

“The first thing is to explore and experiment. Experiment with the new technologies. Find out what those new job titles are going to be and chart the path to become an expert. Either a specialist in using AI as a tool, or an expert in creating AI, or an expert in maximising its performance, or an expert in building the tools that AI will use.”

Nathaniel’s emphasis on APIs is particularly noteworthy: “APIs are nothing new. The better your API, the better the AI agents that will resort to it.” This suggests that developers who understand how to build AI-friendly interfaces will find themselves increasingly valuable.

Most importantly, he advocates for focusing on uniquely human contributions: “Don’t just focus on the typical tasks a junior developer is asked to do because AI can do them really well. Instead, focus on the ways that you can bring a unique twist to your solution.”

AI and Developers: Embracing Change While Staying Human

As our conversation with Nathaniel Okenwa demonstrates, the future of developers isn’t about choosing between human programmers and AI systems—it’s about understanding how these technologies can work together to solve increasingly complex problems. The most successful developers will be those who embrace AI as a powerful tool while focusing on the uniquely human aspects of software development: creativity, problem-solving, and the ability to translate human needs into technological solutions.

In a sense Nathaniel’s advice isn’t that different from Steve Yegge’s. You’ll have to become an expert to make good use of AI; it’s not artificial intelligence that will turn you into an expert.

Rather than fearing obsolescence, developers should view this moment as an opportunity to evolve, much as previous generations adapted to new programming languages, frameworks, and paradigms. The future belongs to those who can harness AI’s capabilities while providing the human insight, creativity, and understanding that no algorithm can replicate.

The future of developers isn’t about replacement—it’s about enhancement, evolution, and the continued pursuit of building technology that serves human needs. In that mission, developers remain not just relevant but essential.

The post The Future of Developers in the Age of AI appeared first on Marketing and Innovation.

How do luxury brands maximize experiences in sports events? I attended the 2025 Monte-Carlo Masters, which showed a strong presence of elite brands fighting for high-end customer engagement. Brands such as Rolex, Sergio Tacchini, and Replay can be found advertised almost everywhere at the famous tennis tournament. These brands use the values of this tennis tournament’s identity, which are class, prestige, excellence, and exclusivity, to reinforce their brand image. In this article we will be looking into the strategy behind premium companies and their connection to the Monte-Carlo Masters Tennis Tournament.

Luxury Brands Maximize Experiences in Sports Events

This photo was made using Midjourney and Adobe Photoshop.

Sports Sponsorship in Luxury Branding

Luxury brands have had a history of gravitating towards sports such as tennis, golf and equestrian sports because these sports emphasized precision, elegance, and tradition. Brands saw that it seemed like a good fit for their deluxe identity due to the traditional affluent audiences that these sports offered.

Only the best of the best athletes competing at these events align with the values of the most luxurious brands that they are the best of what they do. These brands are able to prolong their exclusivity while opening up visibility to sports viewership.

As brands become bigger and sports viewership grows, stylish brands are opening up to collaborations with bigger sports that may not have as much class or prestige, such as football and basketball.

Strategic Brand Positioning at the Monte-Carlo Masters

What once was known as the Monte-Carlo Masters is now known as the Rolex Monte-Carlo Masters. Rolex has positioned themselves front and center at a prestige tournament. Not only are they in the title of the tournament, they are on the logo and can be found everywhere at the tournament itself.

Another brand that has strategically positioned itself is Sergio Tacchini. Being at the tournament itself, it is impossible to miss; every ball kid and many employees working for the tournament wear a piece of clothing from Sergio Tacchini. Just being at the tournament, you are constantly being advertised to, whether you realize it or not; everywhere you look, you are reading another brand name.

Other brands, such as Maserati and Emirates, help back the elitist and prestigious image of the tournament.

Monaco, home of the tournament, is known for its wealth as well as its opulent residents, one more reason to advertise an elegant brand, as the target market is mainly wealthy individuals. “According to the World Population Review, Monaco is the richest country in the world in terms of GDP per capita and is regarded as the “billionaires’ playground.”

Celebrities and top-level athletes being at the tournament make being at the event feel like it’s only for those of wealth, class, and elegance.

This photo was made using Midjourney and Adobe Photoshop.

Brand and Customer Experiences

Some of the most exclusive experiences at the Monte-Carlo Masters are sponsored by posh brands. VIP lounges and luxury suites are curated for high-end customers and guests.

Additionally, behind-the-scenes access, meet-and-greets with athletes, and fancy gifting moments allow brands to showcase their exclusivity even more to only those that can afford them. Hospitality packages include gifts, discounts, special access, and events made to feel extraordinarily classy.

The Société des Bains de Mer (SBM), which is responsible for venues at the event, creates gourmet dining opportunities as well as private lounges mimicking luxury brands.

Customers at the Monte-Carlo Masters are rewarded just by being at the event itself. These rewards include access to limited edition merchandise, entries to giveaways or raffles, and the opportunity to use the VR Tennis simulator.

To further show status of class and order, boutiques at the event are limited to a certain amount of people at a time to prevent cluttering.

When a customer buys a ticket for the tennis match, they are not just coming to watch one game, they are spending practically the whole day there. Even in between matches there are activities to be done around the venue.

These activities include, finding something to eat (there are a lot of choices), VR tennis simulator, walking around and exploring the area, shopping in the countless boutiques and tennis stores.

Scarcity and “Limited Edition”

The value of going to a sports event comes from the electric anticipation of not knowing what will happen. Fans come to witness firsthand the action and to purchase limited edition products showing they were there. Rolex and other brands offer merchandise exclusive to the event itself, signifying someone was at the event.

A piece of limited edition merchandise to show you were at the Rolex Monte-Carlo Masters is also an advertisement every time you wear it in public.

Short-term promotions and exclusive collaborations build a sense of urgency used to encourage customers to buy their product. Brands use this to drive up their exclusivity, giving only people that were at the event a chance to purchase something that will remind them of how special that moment was.

This photo was made using Midjourney.

Content and Social/Digital Media

Social media has completely changed this world as we know it, and that is not only limited to the social aspect, but it has changed the business world drastically. Content creation is a great way to gain publicity, and what better place than a sports event to promote your upscale brand? Brands such as Replay and Malongo took advantage of this event sponsorship and made social media promotions showing their collaboration with the renowned event.

Rolex and Tennis

Rolex has been partnering with tennis for 46 years, since 1978.

Made with Napkin AI.

Key Statistics about Luxury Brands in Sports Events

These statistics show that the tournament continues to improve their technology and assets. Furthermore increasing their brand identity relating to top class.

Maximizing luxury brands customer engagement in sports events: facts and figures about the 2025 Monte-Carlo Masters – infographic done with Canva

Conclusion

Classy, lavish brands strive to take advantage of events such as the Monte-Carlo Masters tournament to build brand identity and to increase publicity. The Monte-Carlo Masters serves as a sort of “playground” for luxurious brands to strategize, promote, and attach themselves to the values that the event portrays.

Brands that strive to align with excellence, class, prestige, and elitism promote themselves using the Monte-Carlo Masters to represent these traits. These companies take advantage of the tournament knowing that the audience is generally wealthier.

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