Signal & Noise

What happens when the numbers say your advertising is working—but the business says otherwise?In this episode of Signal & Noise, Rio Longacre and Brett House sit down with Cimin Ahmadi Cohen, Founder and CEO of Idea Peddler, for a wide-ranging conversation about advertising, ad tech, entrepreneurship, media measurement, and one of the industry’s most persistent problems: ad fraud.Cimin brings a rare perspective. After rising through the ranks of major agencies, she walked away from the traditional agency path and ultimately founded Idea Peddler, an independent, woman-owned agency based in Austin with a satellite office in Santa Fe. She shares the story behind that decision, including how a drive through New Mexico helped give her the clarity to rethink what she wanted from her career and build an agency around a simple idea: doing good work for nice people.We also get into what it means to build a company differently. Cimin talks candidly about being a female founder, creating a workplace that gives parents greater flexibility, building outside the traditional New York agency ecosystem, and why Austin has become such a powerful environment for entrepreneurs and creative businesses.Then the conversation turns to some of the biggest problems in digital advertising.Cimin has become an outspoken critic of what she calls the “attribution illusion”—the idea that marketers can watch metrics like ROAS continue to improve even when revenue, profit, or other real business outcomes remain flat. She explains why platform-reported performance can be misleading, how advertising systems can take credit for sales that were already going to happen, and why marketers need independent measurement rather than allowing the companies selling the media to grade their own homework.That leads to a fascinating discussion about ad fraud, invalid traffic, MFA sites, audience extension, opaque programmatic supply chains, agency compensation models, and the financial incentives that can encourage nearly everyone in the ecosystem to keep more media dollars flowing.We also spend time talking about Dr. Augustine Fu, a repeat Signal & Noise guest and one of the industry's most persistent voices on digital ad fraud. Cimin shares her admiration for his willingness to challenge the industry, bring evidence, and ask questions many people would rather avoid.Other topics include why Cimin believes marketers should scrutinize cheap programmatic inventory, whether brands should audit their agencies more aggressively, the tradeoffs between transparency and principal-based buying, the future role of independent agencies, media in-housing, and how AI could eventually change parts of the media-buying process.It’s a smart, funny, occasionally provocative conversation with a leader who isn't afraid to question some of digital advertising’s most comfortable assumptions.If you work in media, advertising, marketing, ad tech, measurement, or simply want to understand where your digital advertising dollars actually go, this is an episode you won't want to miss.#SignalAndNoise #Advertising #AdTech #DigitalAdvertising #AdFraud #ProgrammaticAdvertising #MediaBuying #Marketing #MediaMeasurement #Attribution #ROAS #AgencyLife #Entrepreneurship #Austin #AI

Douglas Rushkoff has spent decades asking a question that feels more urgent than ever: Are we building technology to serve human beings, or slowly reorganizing human life around the needs of technology?

Recorded live at Advertising Week 2026 at MCM Creative Studios in New York City, Rio Longacre and Brett House sit down with Rushkoff—author, media theorist, professor, documentarian, and host of Team Human—for a wide-ranging conversation about AI, technology, human agency, and what we risk losing if intelligence becomes the only thing we value.

Rushkoff is not an AI doomer, but he is deeply skeptical of the inevitability narrative surrounding artificial intelligence. While much of the industry is focused on bigger models, greater automation, and the possibility of machine superintelligence, Rushkoff asks a different question: What happens to us along the way?

He explains where the idea for “Team Human” came from, beginning with a debate with futurist Ray Kurzweil over whether humanity should eventually “pass the evolutionary torch” to intelligent machines. Rushkoff’s response was simple: human beings are strange, social, emotional, embodied creatures—and that may be a feature rather than a limitation.

The conversation gets into how AI can either strengthen or weaken human thinking. Rushkoff argues that using AI before wrestling with a problem can encourage intellectual outsourcing, while using it afterward can help challenge assumptions, explore ideas, and expand perspective. The problem isn’t necessarily the technology. It’s what we ask the technology to do for us.

Rio, Brett, and Doug also debate whether AI really represents something fundamentally new or is the logical culmination of the internet; whether language models can ever become truly sentient; why embodiment, emotion, grief, intimacy, and lived experience remain difficult to reduce to computation; and whether intelligence alone is enough to constitute life.

They also dig into the economic incentives behind the AI boom, the enormous physical infrastructure required to support it, the environmental trade-offs of data centers and computation, and Rushkoff’s concern that the values of the people and institutions building these systems inevitably influence the systems themselves.

At the same time, the conversation explores the other side of the argument: AI’s potential to accelerate scientific discovery, solve enormously complex problems, improve medicine, address climate challenges, and give individuals capabilities that would have seemed impossible only a few years ago.

This is not a conversation about whether AI is “good” or “bad.” It is about what we want technology to be for—and whether, in the race toward increasingly capable machines, we can remember to stay human.

#ArtificialIntelligence #AI #DouglasRushkoff #TeamHuman #AdvertisingWeek #Technology #FutureOfAI #Media #HumanAgency #SignalAndNoise

What happens when the search result disappears—and the answer itself becomes the interface?

Recorded live at Zeta Live 2026 in New York City during Advertising Week, Brett House sits down with Saroop Bharwani, Co-Founder and CEO of Senso, for a deep dive into one of the biggest emerging questions in marketing: Who controls what AI says about your brand?

For decades, marketers optimized around search rankings, websites, paid media, and the familiar “10 blue links.” But consumers are increasingly turning to ChatGPT, Gemini, Perplexity, and other AI assistants for direct answers. As Saroop puts it, the answer is beginning to eat the entire funnel.

That creates an entirely new challenge for brands.

It’s no longer enough to rank on Google. Brands need to understand whether they appear in AI-generated answers, which sources those models are citing, how they compare with competitors, whether the information being surfaced is accurate—and ultimately whether they can influence the context from which AI systems generate their responses.

Saroop explains how his background building technology inside highly regulated industries shaped his perspective on AI. Long before generative AI became mainstream, financial institutions were already wrestling with issues that have suddenly become critical for every enterprise: accuracy, governance, explainability, permissions, data sovereignty, and trust.

The conversation explores why context may become one of the most important forms of enterprise intellectual property in the AI era. As agents begin operating across websites, product catalogs, knowledge bases, CRMs, data platforms, and internal systems, companies need a trusted layer that tells those systems what information is correct, who can access it, and where it came from.

Saroop also discusses Senso’s Verified Sources approach, creating an audit trail around AI-generated content that can show which sources were used, which humans verified the information, which agents generated it, and how that information changed over time.

Brett and Saroop also get into:

• Why AI assistants are rapidly changing consumer discovery
• The difference between being mentioned, ranked, and cited in an AI answer
• New metrics such as mention rate, citation rate, and citation share
• Why enterprises should start by measuring how AI currently represents their brands
• The growing importance of AEO and GEO
• Why fragmented enterprise context is such a major AI problem
• How brands can influence AI answers without simply flooding the web with “AI slop”
• Why verified content and traceability could become essential to maintaining trust
• The widening gap between frontier AI development and enterprise adoption
• Why human judgment, taste, and governance still matter
• What happens when AI systems move from answering questions to proactively recommending products
• And the increasingly blurry line between an advertisement and an AI-generated product recommendation

The conversation comes at an important moment for Saroop and Senso. Zeta Global recently announced an agreement to acquire Senso, bringing its AI discovery and verified-content capabilities into a much larger marketing technology ecosystem.

If AI assistants and agents increasingly become the front door through which consumers discover brands, products, services, and information, the implications are enormous.

The next marketing battleground may not be the search results page.

It may be the answer itself.

#SignalAndNoise #ZetaLive #AdvertisingWeek #ArtificialIntelligence #GenerativeAI #AI #AEO #GEO #AnswerEngineOptimization #MarketingTechnology #MarTech #AdTech #Senso #ZetaGlobal #ChatGPT #AgenticAI #FutureOfMarketing

What happens when marketing stops being a collection of channels, campaigns, and technologies—and starts becoming an intelligence function?

Recorded live at Zeta Live 2026 in New York City, during Advertising Week, Brett House sits down with Ed See, Chief Growth Officer at Zeta Global, for a wide-ranging conversation about AI, customer intelligence, the changing role of the marketer, and what the next generation of marketing organizations will look like.

Ed brings a unique perspective to the discussion. After decades advising some of the world’s largest companies—including senior roles at McKinsey and Deloitte—he made the move from adviser to operator at Zeta. His view of marketing is refreshingly straightforward: ultimately, marketers are responsible for creating profitable customers.

That means moving beyond impressions, clicks, ROAS reports, and other activity metrics toward understanding where real growth opportunities exist. Ed breaks the challenge into four fundamental capabilities: recognizing a pocket of opportunity, reaching it, delivering something relevant, and measuring the result.

From there, Brett and Ed explore why identity and signals are becoming foundational to modern marketing. Personalization is no longer enough. The opportunity is precision: understanding not only who someone is, but what they need, when they need it, and when a genuine transition or buying moment is occurring.

The conversation also turns to one of the biggest changes facing marketers: consumers increasingly have AI agents sitting between themselves and brands. Marketers have spent decades learning how to influence people emotionally. But as Ed points out, LLMs “think and do”—they don’t feel. That creates an entirely new constituency that brands will need to understand, influence, and remain discoverable to.

Brett and Ed also dig into the changing marketing organization: the rise of the technically fluent marketer, Ed’s earlier thinking around the Chief Marketing Technology Officer, why hyper-specialization may give way to broader marketing roles, and how AI could finally begin breaking down some of the organizational silos that have existed for decades.

They also discuss AthenaOS, Zeta’s vision for an intelligence layer spanning enterprise data and AI; why proprietary context may become some of a company’s most important intellectual property; the importance of both data sovereignty and AI sovereignty; and how agencies, technology companies, and brands may divide responsibilities in this new operating model.

And, naturally, they finish with the big question: Is AI going to kill us all—or unlock an extraordinary new period of human progress?

This is the third Signal & Noise conversation with a Zeta Global executive, following our discussions with Christian Monberg, Chief Technology Officer, at Cannes Lions, and Neej Gore, Chief Data Officer, at POSSIBLE. A huge thank-you to the Zeta team for welcoming Signal & Noise to Zeta Live and providing an incredible setup for the conversation.

For anyone thinking about the future of marketing, AI, customer intelligence, MarTech, AdTech, agencies, or the modern enterprise, this is one you won’t want to miss.

#SignalAndNoise #ZetaGlobal #ZetaLive #ArtificialIntelligence #AI #Marketing #MarTech #AdTech #CustomerIntelligence #MarketingTechnology #AgenticAI #EnterpriseAI

What happens to the enterprise marketing stack when AI becomes more than another feature—and starts changing how data, technology, people, and decisions actually work together?

Recorded live at Advertising Week 2026 in New York City at MCM Creative Studios, Rio Longacre and Brett House sit down with two longtime friends of the show and two of the smartest voices in data and marketing technology: Zack Wenthe, Director of Product Marketing & Customer Data Evangelist at Tealium, and Adrian Bolosan, Head of ISV GTM, AMER Enterprise & Emerging at Databricks.

This conversation goes well beyond the usual AI talking points. It’s a wide-ranging discussion about how enterprises are actually adapting their MarTech and AdTech stacks for an AI-driven world—and what’s real versus the latest industry hype.

The conversation starts with one of the industry’s favorite words: composability. Has the composable stack actually won? Or has it become another catch-all marketing term? Zack and Adrian explore why enterprises are moving applications closer to their data, the growing role of federated architectures, and why flexibility increasingly matters more than buying another monolithic platform.

From there, the discussion gets into the foundation underneath every AI strategy: data. AI is only as useful as the signals feeding it. The group discusses data collection, tag management, governance, identity resolution, consent, context, and why enterprises need to understand what data they have before trying to unleash agents on top of it.

They also tackle one of the biggest unresolved questions in marketing technology: Where should decisioning live? In the data cloud? The CDP? The CRM? Or directly inside the activation channel? The answer may increasingly depend on the moment—combining historical intelligence from platforms like Databricks with real-time signals captured at the edge.

The conversation then turns to how AI is changing the marketing organization itself. The traditional marketing technologist is evolving into the marketing engineer—someone who can connect systems, data, agents, models, and workflows while understanding the business problem being solved. At the same time, marketers are gaining the ability to interrogate enterprise data through natural language instead of waiting on analysts, engineers, or data science teams.

Rio, Brett, Zack, and Adrian also dig into:

•

The resurgence of tag management in an agentic world

•

Why context may matter more than perfect one-to-one personalization

•

Identity resolution and the changing economics of third-party identity

•

“Perishable moments” and the need for real-time customer decisioning

•

How AI could break down data and organizational silos

•

The rise of disposable, use-case-specific applications

•

Whether the predicted “SaaSpocalypse” is actually happening

•

How AI is changing consulting, systems integration, and enterprise technology buying

•

Why the companies that win with AI will combine strong data foundations with people who know how to turn technology into business outcomes

For anyone working in MarTech, AdTech, customer data, enterprise AI, cloud data platforms, advertising, or digital transformation, this is an outstanding conversation about where the enterprise tech stack is headed.

A huge thank-you to Tealium, one of the sponsors that helped make our Advertising Week possible and an ongoing sponsor of Signal & Noise.

And a special thanks to MCM Creative Studios for hosting Signal & Noise LIVE during Advertising Week 2026.

Hosted by: Rio Longacre & Brett House
Guests: Zack Wenthe, Tealium | Adrian Bolosan, Databricks
Recorded live: Advertising Week 2026 | New York City

#ArtificialIntelligence #EnterpriseAI #MarTech #AdTech #CustomerData #CDP #DataCloud #MarketingTechnology #AIinMarketing #AgenticAI #ComposableArchitecture #DataStrategy #DigitalTransformation #MarketingEngineering #CustomerExperience #Databricks #Tealium #AdvertisingWeek #AdvertisingWeek2026 #SignalAndNoise

What happens when everyone has access to the same AI?

In this special live episode of Signal & Noise, recorded in front of an audience at MCM Creative Studios during Advertising Week New York, Brett House and Rio Longacre sit down with Matt Krepsik, CEO of MediaRadar, and Vas Bakopoulos, SVP and Head of Industry Research at MMA Global, for a fast-moving conversation about AI intelligence, marketing, creativity, and competitive advantage.

The discussion starts with new research from MediaRadar examining how advertising and media professionals are actually using AI. Among 500 industry professionals surveyed in July 2026, 87% said they actively use AI in advertising and media decisions, while 92% reported changes to products, services, or pricing as AI reshapes the industry. But widespread adoption raises a much bigger question: If everyone has the same tools, where does real differentiation come from?

Matt and Vas dig into the tension between AI adoption and AI trust: data provenance, outdated information, hallucinations, proprietary data, governance, and why brands need to think carefully about the intelligence and data sitting underneath their models. The conversation explores why the competitive advantage may increasingly live not in the model itself, but in the data, memory, skills, guardrails, workflows, and proprietary knowledge surrounding it.

From there, the panel goes deep on what AI means for the future of advertising: agency operating models, in-housing, the ad-tech supply chain, creative intelligence, and the opportunity to finally reconnect planning, creative, activation, and measurement.

And then comes one of the biggest ideas of the conversation: when everyone has the same box of AI-powered crayons, the advantage shifts back to the painter.

Matt and Vas make the case that the future of marketing will place an even greater premium on taste, craft, judgment, perspective, and distinctive ideas. AI can accelerate production, analyze enormous numbers of permutations, and expose new opportunities—but it still takes people to decide what is worth creating in the first place.

It’s thoughtful, provocative, occasionally irreverent, and a lot of fun—exactly what a live Signal & Noise conversation should be.

A huge thank you to MCM Creative Studios for giving us an incredible home for the evening, and especially to MediaRadar, the main sponsor of our Advertising Week activation, for helping make this special live event possible. This was also a milestone for Signal & Noise: our first episode recorded in front of a live studio audience.

If you work in advertising, media, marketing, AI, data, or creative, this is one you’ll want to hear.

Topics include:

• AI adoption in advertising

• AI trust and data provenance

• Proprietary data

• Brand data sovereignty

• AI governance • agency transformation

• In-housing

• Creative intelligence

• Taste and craft

• Human creativity

• Advertising measurement

• Agentic AI

• The future of marketing

#ArtificialIntelligence #AI #AdvertisingWeek #Advertising #Marketing #AdTech #MarTech #MediaRadar #MMAGlobal #CreativeAI #AgenticAI #MarketingAI #SignalAndNoise

If AI can execute in seconds what agencies once billed for in hours, what should clients pay for next?

Filmed live at Advertising Week 2026 at MCM Studios in New York City, this episode of Signal & Noise features Adam Woods, Global Chief Product Officer, and Amy Crowther, CEO, Americas, at Incubeta. They join hosts Rio Longacre and Brett House for a candid conversation about the future of agencies, the economics of AI, and what it takes to connect technology, talent, and data around better business outcomes.

Building an agency operating system means more than assembling a collection of tools. Adam and Amy discuss how agencies can bring creative, media, measurement, and client-owned intelligence into a more connected way of working—and why changing how people work is just as important as changing the technology.

From creative production and data infrastructure to client relationships and pricing, this conversation gets into the practical decisions behind agency transformation.

In this episode:

• Beyond billable hours: How AI challenges staffing-based fees and traditional service models, and why the real opportunity is in how agencies reinvest efficiency gains.

• Connecting creative and media: Why clients still struggle with the gap between creative ideas and media execution, and how shared performance data can give teams a stronger starting point.

• Creative intelligence in practice: How understanding which assets work—and why—can inform production, testing, and optimization, rather than leaving creative teams waiting for feedback.

• Client-owned data and intelligence: Why brands should build on their own knowledge, how fragmented tools create disconnected data, and what agencies can do to help clients retain control.

• Measuring business impact: Moving beyond click-through rates, cheap impressions, and platform-reported success to questions about profitable growth, customer acquisition costs, and incremental contribution.

• Bringing brand and performance together: Why short-term results need to be understood alongside long-term brand investment, and how agencies can help CMOs make a stronger case to CFOs.

• Making transformation practical: Why the starting point may be as simple as getting a brand’s creative assets consistent, proving value, and building momentum from there.

• Rethinking the value exchange: When to challenge an RFP, why trust matters, and how outcome-based pricing can work when costs, incentives, and shared goals are realistically aligned.

Adam brings a product and technology perspective to building connected capabilities. Amy grounds the discussion in the realities of leading teams, educating clients, and turning those capabilities into work that makes a difference.

Together, they make the case for an agency relationship built around informed decisions, shared accountability, and measurable value. AI expands what is possible; the harder work is deciding what to build, how to deploy it, and which problems are worth solving.

Watch or listen, then tell us: What should the agency of the future be accountable for—and how should clients pay for it?

Subscribe to Signal & Noise on YouTube or follow on Spotify for more conversations about data, technology, AI, and the people shaping advertising.

#SignalAndNoise #AdvertisingWeek2026 #AdWeek2026 #Incubeta #FutureOfAgencies #AgencyTransformation #CreativeIntelligence #AgenticAI #MarketingEffectiveness

Creative Judgment in the AI Era | Brandon Mikolaski | Signal & Noise LIVE at Advertising Week 2026

As AI makes content faster and easier to produce, what makes creative work worth paying attention to?

Filmed live at Advertising Week 2026 at MCM Studios in New York City, this episode of Signal & Noise brings together hosts Rio Longacre and Brett House with Brandon Mikolaski, Director of Creator Partnerships at Forbes Creator Network, for a candid, funny, and wide-ranging discussion about creativity, audience trust, and the human judgment behind great work.

Brandon brings a perspective shaped by stand-up comedy, creative leadership, branded entertainment, and his time leading creative and content at GSTV. Now at Forbes Creator Network, he helps connect creators’ expertise and communities with opportunities across video, podcasts, live events, and original programming.

The conversation explores where AI and automation genuinely help creative teams, where they fall short, and why experience, taste, and a strong point of view still matter. Brandon shares how his team at GSTV used automation to produce tens of thousands of video variations, while keeping quality control and creative decisions in human hands. He also explains why he uses AI for research and operational tasks while keeping campaign ideas rooted in collaboration and human insight.

In this episode:

• Creative judgment in an AI-driven industry: Why generating more content doesn’t automatically mean making better work, and how experienced creatives bring context, intention, and taste to the tools.

• Building Forbes Creator Network: How Brandon approaches creator selection, why demonstrated expertise matters, and what publishers can offer beyond distribution.

• Credibility and brand partnerships: Why the strongest integrations start with a real connection between the creator and the brand, and how audiences recognize when that connection is missing.

• Attention, context, and measurement: Lessons from GSTV about understanding where people are, what they’re doing, and how creative fits into their day.

• The value of showing up: Why live events, comedy, music, and shared experiences can build relationships that go deeper than likes and shares.

• Leading creative teams: Making room for experimentation, exposing junior talent to new environments, and knowing when to defend an idea or let go of your ego.

Along the way, the discussion moves from comedy clubs and concert crowds to the New York City subway, exploring how curiosity and everyday observation feed creative thinking.

Brandon’s approach to AI is practical: use it to get better at your job while protecting the judgment that makes your work distinctive. For marketers, creators, agency leaders, and anyone building a creative team, this conversation offers a grounded look at what that means in practice.

Watch or listen, then tell us: Where is AI making your creative work better, and which decisions do you still keep firmly in human hands?

Subscribe to Signal & Noise on YouTube or follow on Spotify for more conversations about data, technology, AI, and the people shaping advertising.

#SignalAndNoise #AdvertisingWeek2026 #AdWeek2026 #CreatorEconomy #CreativeLeadership #AIAndCreativity #ForbesCreatorNetwork #BrandStorytelling

Programmatic advertising has spent years getting smarter about audiences. But what if the bigger problem is that buyers still don’t fully understand what they’re actually buying?

Eric Tilbury, Vice President of Programmatic Ops & Solutions Engineering at Inuvo, joins Rio Longacre and Brett House for a deep dive into the machinery underneath modern programmatic advertising—and why better technology doesn’t necessarily produce better media buying.

Eric is an operator first. He has spent more than a decade inside campaign management, supply optimization, contextual decisioning, CTV, DSPs and the plumbing that determines whether a bid request ever reaches a buyer in the first place. That experience has also made him one of the more outspoken voices in AdTech on supply quality, measurement and what he sees as the industry’s bad habits.

The conversation starts with a deceptively simple idea: ID-free targeting is not necessarily the same thing as buying ID-free inventory. A contextual strategy may not require an identifier, but DSP settings around frequency caps, conversion tracking and optimization can still systematically favor impressions with IDs—or prevent ID-free opportunities from reaching the campaign at all.

From there, Eric explains why clean supply is not just a brand-safety exercise. Buyers need to know the publisher, seller and path behind an impression, especially in CTV and mobile apps. Cheap CPMs and attractive platform metrics can conceal low-quality placements, opaque reselling, bot traffic and inventory no advertiser would intentionally choose.

The discussion also gets into one of Eric’s biggest contrarian ideas: iOS and other ID-light environments may represent an overlooked buying opportunity. When advertisers optimize around what is easiest to identify and attribute, they can systematically ignore valuable consumers—and sometimes reward the inventory most capable of gaming their measurement systems.

Eric also breaks down Inuvo’s IntentKey approach, which uses content, concepts, sentiment and real-time signals to make buying decisions without building the decision around an individual user profile.

And then the conversation turns to measurement. Eric argues that platform ROAS, CPA and CTR should be treated as directional optimization metrics—not as the ultimate source of truth. If advertising is actually working, the question should be whether it created incremental business results that would not have happened otherwise.

Rio, Brett and Eric also explore:

• Why DSP auto-optimization can send campaigns toward terrible inventory
• The difference between attribution and incrementality
• Why CTV supply-path verification should be much easier than it often is
• How fraudsters benefit from the industry’s obsession with identifiers
• Why contextual intelligence becomes more valuable as open-web behavior changes
• Whether AI agents and MCP interfaces will weaken DSPs—or actually make them stronger
• Why buyer judgment still matters in an increasingly automated media ecosystem
• Eric’s skepticism toward probabilistic ID bridging
• And how a 4:30 a.m. training schedule and Brazilian jiu-jitsu fit the mindset of an AdTech operator

This is AdTech school for anyone who wants to understand what actually happens between an advertiser’s strategy and the impression that eventually gets bought.

If you work in programmatic, media, CTV, contextual advertising, measurement or AdTech, this one goes deep.

Subscribe to Signal & Noise for more conversations with the people building—and challenging—the future of advertising, data, media and AI.

#AdTech #ProgrammaticAdvertising #Programmatic #CTV #DigitalAdvertising #MediaBuying #ContextualAdvertising #Advertising #Marketing #MarTech #AI #Measurement #Identity #SignalAndNoise

For years, LiveRamp played the role of “Switzerland” in digital advertising: a neutral identity layer connecting brands, agencies, publishers, platforms, data providers, and measurement partners. But with Publicis moving to acquire LiveRamp, can infrastructure owned by one of the world’s largest agency groups still credibly claim neutrality?

In this episode of Signal & Noise, Rio Longacre and Brett House sit down with Mathieu Roche, Co-founder and CEO of ID5, to explore what happens when one of advertising’s most important shared rails suddenly has an owner with a very different strategic position.

Mathieu argues that the industry now has a genuine reason to build alternatives—but replacing RampID is not the same thing as replacing LiveRamp. LiveRamp’s position was built through identity resolution, onboarding, publisher integrations, data collaboration, activation, measurement, and years of network effects. Recreating that requires more than another identifier. It may require an entirely new coalition of independent companies.

The conversation gets into what that new identity commons could actually look like. Mathieu explains why identity should function more like a common currency or language between buyers and sellers—open, interoperable, and usable across platforms rather than controlled by either side. He also explains why identity is just as critical for measurement as targeting: if advertisers cannot connect exposure to outcomes across devices and environments, they cannot prove performance or optimize investment.

Rio, Brett, and Mathieu also dig into ID5’s acquisition of TrueData, which Mathieu describes as a kind of “Rosetta Stone” connecting different identifiers, devices, users, households, and offline data into the ID5 ecosystem. The discussion explores deterministic versus probabilistic identity, why conventional “match rate” metrics can be misleading, and why the real question is whether an identifier can actually be found and activated against in the wild.

They also examine the publisher side of the equation: how identity can increase bid density and monetization, how ID5 works alongside companies such as Optable and Permutive, and why publishers need deeper knowledge of their audiences if they want to regain leverage from the buy side.

Finally, the conversation turns to agentic trading, CTV, and the future of advertising infrastructure. Mathieu believes agentic buying could compress parts of today’s programmatic supply chain—but rather than making identity less important, increasingly granular and automated buying may make a trusted identity layer even more essential.

If LiveRamp can no longer be Switzerland, the next Switzerland may not be a single company at all. It may be a modular network of specialists—and the race to build it has already begun.

#AdTech #DigitalIdentity #LiveRamp #ID5 #ProgrammaticAdvertising #AgenticAI #Advertising #DataPrivacy #CTV #Publishers #MarketingTechnology #SignalAndNoise

AI may live in the cloud, but the infrastructure behind it is very physical: land, power plants, transmission lines, water systems, construction crews—and communities deciding whether they actually want massive data centers in their backyard.

In this episode of Signal & Noise, Rio Longacre and Brett House sit down with Dawson Kluesner, Director of External Affairs for the Wyoming Association of Municipalities and Coordinator for the Wyoming Economic Development Association. Dawson sits at a fascinating intersection: he works with the cities and towns evaluating major infrastructure projects while also working alongside the economic-development community trying to attract investment to Wyoming.

Wyoming has emerged as an increasingly attractive destination for hyperscale data centers. It has abundant energy resources, available land, a long history as an energy-producing state, and communities looking for ways to diversify their tax bases. But the boom is also generating many of the same concerns appearing around the country: electricity demand, water consumption, noise, environmental impact, public subsidies, infrastructure costs and whether data centers create enough permanent jobs to justify their enormous footprint.

Dawson argues that the answer isn't simply to say yes or no to data centers. The real question is whether communities can structure projects so residents are genuinely better off after they arrive.

The conversation explores:

• Why Wyoming has become attractive to data center developers—and why energy availability may now be one of the biggest constraints on AI infrastructure.
• How Wyoming is trying to protect residential ratepayers, including Black Hills Energy's large-load tariff requiring major industrial customers to pay for the infrastructure needed to serve them.
• Why water remains one of the biggest concerns Dawson hears at community meetings, and how newer cooling technologies are changing the debate.
• The economics of data centers: massive construction activity, relatively smaller permanent workforces, property and electricity tax revenue, and the opportunity to diversify Wyoming's economy.
• The growing demand for electricians, welders, HVAC technicians, plumbers and other skilled trades—and why data center investment could help build a workforce that benefits Wyoming well beyond AI.
• Nuclear power, natural gas, carbon sequestration, wind and the enormous energy buildout required to support the next generation of computing.
• Community benefit agreements, tax incentives and what developers should owe the communities hosting these projects.
• Why opposition to data centers cuts across political lines—and why early, transparent community engagement may matter as much as the underlying technology.

Ultimately, Dawson offers a straightforward test for whether Wyoming's data center boom succeeds: not how many facilities get built or how many gigawatts come online, but whether the projects leave local communities financially stronger, with better infrastructure and greater economic capacity without creating unsustainable demands on residents or resources.

It's a conversation about AI—but also about energy, economics, infrastructure, public trust and the increasingly difficult question of who should bear the costs of building the physical backbone of the digital economy.

Subscribe to Signal & Noise for more conversations at the intersection of media, data, technology and AI.

#ArtificialIntelligence #AI #DataCenters #Wyoming #Infrastructure #Energy #EconomicDevelopment #NuclearEnergy #Technology #SignalAndNoise

AI makes it easier to create content. Getting people to care is still the hard part.

On this episode of Signal & Noise, host Rio Longacre and guest host Krish Raja sit down with Gregory Kennedy, founder of Vibe Your SaaS, for a candid conversation about memes, founder-led marketing, creative talent, and building an independent business in the AI era.

Gregory traces his path from New York’s design scene and an award-winning website he built in 1997 to two decades in Silicon Valley and a move to Seattle. He explains how a founder-coaching idea grew into Vibe Your SaaS—a business combining content and go-to-market consulting, a newsletter, and startup pitch competitions—and why his Bay Area network remains central to his work.

Along the way, the conversation tackles what it takes to build an audience, develop a recognizable voice, and turn attention into an opportunity to do business.

In this episode:

• The anatomy of a meme: How recognizable moments, humor, and an unexpected twist make an idea travel—and why Gregory thinks the Drake meme needs to retire.

• Building an audience from zero: The posting, replies, persistence, and personal commitment behind Gregory’s presence on X, plus why your platform should fit your strengths and personality.

• Founder-led marketing with a point of view: How Gregory uses his voice to attract the right clients, why controversy carries real risks, and how he advises founders to stay in a lane they understand and can defend.

• Taste, brand, and AI-generated content: Why Gregory believes taste has always mattered, how consistent quality builds recognition, and why cheaper content doesn’t make attention easier to earn.

• “Become inevitable”: The connection between founder conviction, a real customer need, a clear market story, and building an audience before you launch.

• Silicon Valley, Seattle, and startup culture: Gregory’s case for the Bay Area’s concentration of talent and ambition, and his perspective on how Seattle’s business policies have affected his own plans.

• AI and the human side of sales: Why automating outreach doesn’t automatically win customers—and the value of a founder’s ability to make decisions on the spot.

• Talent, careers, and smaller teams: A debate about leaning into your strengths, valuing individual contributors, and using AI to build more with fewer resources.

Plus, Gregory makes his pick between taste and distribution, questions the return on in-person B2B events, and shares why he’s optimistic about the opportunities opening up for solo founders and small teams.

For founders, marketers, and creators trying to earn attention in a crowded feed, this is a conversation about finding what you’re good at, showing up consistently, and giving people a reason to remember you.

Connect with Gregory:
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#SignalAndNoise #GregoryKennedy #VibeYourSaaS #B2BMarketing #FounderLedMarketing #Memes #AI #Startups #GoToMarket

The open web has survived search, social media, programmatic advertising, privacy regulation, and the rise of the walled gardens. But does generative AI represent a fundamentally different threat?

In this episode of Signal & Noise, Rio Longacre sits down one-on-one with Kurt Donnell, President & CEO of Freestar, while Brett House is away.

Kurt brings an unusual perspective. He began his career as an M&A attorney, moved into media and operations, helped take YogaWorks public, and eventually joined Freestar as its first CEO. Under his leadership, Freestar says it has grown sevenfold, expanded to 185 employees across 15 countries, and returned more than $1 billion to publishers.

Rio and Kurt tackle an increasingly urgent question for digital media: Is the publisher crisis still primarily a monetization problem, or has AI created a structural break in the economics of the open web?

Kurt explains how the historical bargain between publishers and platforms is changing. Search engines consumed publisher content but also sent traffic back. AI answer engines can deliver the answer without the click. Larger publishers with strong brands, direct audiences, and genuine utility have generally held up better, while SEO-dependent long-tail publishers have faced greater pressure.

The conversation goes well beyond AI.

Rio and Kurt discuss why original journalism remains essential even in a world of podcasts, Substacks, creators, and AI summaries. Someone still has to produce the reporting—the raw material everyone else analyzes, summarizes, and synthesizes. Without a sustainable model for creating that information, the ecosystem suffers.

They also unpack the economics of programmatic: supply-path duplication, unnecessary intermediaries, declining trust, and why the open web remains harder for advertisers to understand than closed platforms. Kurt argues one of the walled gardens’ greatest advantages is simplicity and transparency—the ability to show advertisers what happens when they put money into the system.

Kurt also explains Freestar’s pubOS, a unified operating layer to help publishers manage header bidding, yield optimization, analytics, identity, integrations, demand partners, and managed services with less complexity.

Rio and Kurt explore:

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Why publishers need to become better marketers

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Whether subscriptions, commerce, events, and licensing can supplement ads

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Why email and newsletters may be among the most valuable assets pubs control

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How first-party data and authenticated audiences can improve economics

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Why trust and verification remain barriers to monetizing pub signals

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Whether cleaner supply paths can rebuild advertiser confidence

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Why CPM and fill rate can be misleading

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The role of curation and first-party audience signals

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Whether agentic trading can remove unnecessary layers from programmatic

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What publishers must own directly as discovery shifts from search

On agentic advertising, Kurt is cautiously optimistic. Direct communication between scaled buyer and seller systems could improve targeting, packaging, reporting, and transparency, but the market remains early and infrastructure still needs to be recreated.

This is a conversation about much more than publisher yield. It is about whether independent digital media can build a new economic model around direct relationships, trusted content, cleaner advertising infrastructure, better data, and dramatically less friction before AI permanently reshapes how audiences discover information.

Featuring: Kurt Donnell, President & CEO, Freestar
Hosted by: Rio Longacre, Signal & Noise

#SignalAndNoise #KurtDonnell #Freestar #AdTech #Publishing #OpenWeb #ProgrammaticAdvertising #DigitalMedia #GenerativeAI #AgenticAI #PublisherMonetization #FirstPartyData #HeaderBidding #SupplyPathOptimization #Journalism #MediaEconomics #pubOS

Few people have had a better vantage point on the transformation of advertising, technology, and work than Rishad Tobaccowala.

Over a 37-year career at Publicis Groupe, Rishad helped pioneer its digital businesses, chaired Digitas and Razorfish, and ultimately served as Global Chief Strategist and Growth Officer. Today, he is an author, teacher, adviser, senior adviser to Publicis Groupe, co-founder of the Athena Project on Modern Leadership, and one of the industry’s most original thinkers on how people and organizations navigate change.

In this wide-ranging, smart, funny, and deeply thought-provoking conversation, Rishad joins Brett House and Rio Longacre to challenge one of the biggest assumptions surrounding technological transformation: companies don’t transform simply because they buy new technology, reorganize, or deploy AI. Companies transform when their people do.

Rishad argues that AI represents something fundamentally different from previous technology waves—not simply another tool, but what he calls an emerging form of “alien intelligence.” The question, then, isn’t whether organizations have an AI strategy. It’s whether they are willing to rethink how work gets done, how people are rewarded, where power sits, and what human beings uniquely contribute.

The conversation explores why transformation is ultimately about power, politics, people, and partnering; why technology moves faster than organizations; and why simply making the old model more efficient is like putting a faster engine into a car when what you really need is to design an airplane.

Rishad also explains his idea of “de-bossification”—the decline of management built around control and the rise of leadership built around influence. He compares the organization of the future to jazz rather than classical music: more improvisational, collaborative, fluid, and less dependent on rigid hierarchy.

Along the way, they dig into:

• Why AI may change nearly every white-collar job without necessarily eliminating work
• The rise of portfolio careers, fractional talent, freelance work, and agentic employees
• The six skills Rishad believes matter most: creativity, curiosity, cognition, collaboration, convincing, and communication
• Why knowledge may become abundant while judgment, imagination, and wisdom become more valuable
• How individuals can “rearchitect” their careers instead of waiting for an employer to provide purpose
• Why leaders should “untie, not cut” when leaving organizations
• What Publicis got right in its decade-long transformation—and why changing incentives and behavior mattered as much as acquisitions and technology
• Why agencies, consultancies, and other services businesses will struggle to keep selling hours and FTEs in an AI-enabled economy

Rishad also shares a deceptively simple definition of success: the freedom to spend your time in ways that give you joy.

This is a conversation about AI, but even more, it is about work, leadership, careers, agencies, reinvention, and human agency. And it’s a reminder from one of advertising’s great strategic thinkers that the hardest part of transformation has never been the technology.

#SignalAndNoise #RishadTobaccowala #FutureOfWork #Leadership #Reinvention #Advertising #Marketing #Agencies #PublicisGroupe #DigitalTransformation #OrganizationalTransformation #GenerativeAI #AgenticAI #Careers #FutureOfAgencies #Workplace #BusinessStrategy #HumanIntelligence #Debossification #RethinkingWork

What does it take to walk away from a prestigious legal career, build a technology company from scratch, take it public—and then reinvent the business along the way?

In this episode of Signal & Noise, Brett House and Rio Longacre sit down with Bryan Leach, Founder, CEO, President & Chairman of Ibotta, for a wide-ranging conversation about entrepreneurship, commerce media, measurement, AI, and why he chose Denver as the place to build.

Bryan’s path to technology was anything but conventional. After Harvard, Oxford as a Marshall Scholar, Yale Law School, a Supreme Court clerkship, and a successful career at Bartlit Beck, he realized he wanted to build something of his own. The idea that became Ibotta began on an airplane, when Bryan watched another traveler photograph receipts and started thinking about how smartphones could capture purchase data—and eventually make rebates dramatically easier for consumers.

What started as a cash-back app has evolved into something much larger: the Ibotta Performance Network (IPN), which distributes digital promotions through major platforms and retailers. Today, much of Ibotta operates behind the scenes, powering offers inside experiences like Walmart and other publisher partners rather than requiring consumers to use the Ibotta app itself.

The conversation goes deep on one of advertising’s hardest problems: proving outcomes. Bryan explains why a redemption isn’t enough, why incrementality matters, and how Ibotta is trying to shift CPG promotions away from impressions, clicks, and blanket discounts toward measurable incremental sales and a true cost-per-outcome model. He also explains how persistent retailer IDs and item-level purchase data make it possible to compare exposed and unexposed consumers and measure actual lift.

They also dig into:

• Why Bryan left law even after reaching a career most people would never walk away from—and how one mentor’s decision to invest became a pivotal moment in Ibotta’s creation.
• How Ibotta grew from roughly 2 million to 20.9 million redeemers, with much of that growth coming through third-party publishers.
• Why Bryan believes promotions should increasingly be treated as an always-on performance channel, with spending governed by incremental economics rather than fixed annual budgets.
• How AI is already changing Ibotta’s product, software development, measurement capabilities, and internal workflows.
• The transition from private company to public company—and the lessons of building one of Colorado’s landmark technology companies.
• Why Bryan sees Denver’s culture, talent base, quality of life, and civic accessibility as competitive advantages for founders.

It’s a conversation about much more than coupons or cash back. It’s about transforming promotions from a century-old marketing tactic into a measurable growth engine—and what Bryan learned from taking Ibotta from an idea to a public technology company along the way.

#SignalAndNoise #Ibotta #BryanLeach #CommerceMedia #RetailMedia #CPG #PerformanceMarketing #AdTech #MarTech #Incrementality #MarketingMeasurement #DigitalPromotions #RetailTech #CustomerData #AI #ArtificialIntelligence #Entrepreneurship #Founders #Startups #DenverTech #ColoradoTech #IPO #BusinessLeadership #Innovation #Marketing #Advertising #ConsumerBehavior #FutureOfCommerce

Generative AI has made it radically easier—and cheaper—to create advertising. But if every brand can suddenly produce infinite variations using the same models, what creates competitive advantage?

In this episode of Signal & Noise, Rio Longacre and Brett House sit down with Alex Collmer, Founder & Executive Chairman of Vidmob, to explore why the next battleground in advertising may not be content generation. It may be the intelligence that determines what to create, why it works, and what brands learn from every ad they put into market.

Alex traces Vidmob’s evolution from a marketplace connecting marketers with video creators into what it now calls a creative data company. The turning point came when Vidmob connected its production systems directly to advertising platforms and suddenly gained access to performance data. That exposed a major disconnect: marketers spent years optimizing audiences, bids, identity, attribution, geography, and media—but creative remains one of advertising’s most important and least-instrumented variables.

Alex explains what creative data means: a structured understanding of the thousands of decisions contained within an ad—from messaging and narrative arc to logo placement, pacing, framing, imagery, and brand cues—and how those decisions correlate with business and campaign outcomes.

The conversation turns to generative AI. Alex argues foundation models themselves are rapidly becoming commodities. If every competitor has access to the same technology, simply having better AI won’t create a durable advantage. The differentiator is proprietary intelligence brands bring into those systems: what they know about their creative, customers, performance, and how their brand communicates effectively.

That raises an even bigger strategic question: Should brands allow platforms to own that intelligence—or build and control it themselves?

Alex discusses Vidmob360 and the shift from static dashboards to APIs, MCP services, assistants, and agents that make creative intelligence actionable inside existing marketing workflows. Instead of receiving a PowerPoint weeks after a campaign launches, marketers can interrogate live creative performance, identify fatigue, understand why assets are succeeding or failing, generate better briefs, and connect insights directly to production and media decisioning.

Rio, Brett, and Alex also tackle the danger of creative sameness. Generative AI can produce thousands of variations, but volume isn't originality. Continuous optimization can easily become a feedback loop in which every brand learns from yesterday's winners and gradually begins looking and sounding the same.

They also explore what all of this means for designers and creative professionals. Alex sees AI less as a replacement for great creatives than another powerful tool—and argues the best technically literate creators may enter an entirely new playing field. Real-time performance feedback can also finally give creative teams something they've historically lacked: a direct learning loop between what they make and how audiences respond.

Plus: why Alex once raced humans against horses, why click-through rate is the creative metric he'd eliminate tomorrow, and the human capability he believes becomes even more valuable as AI improves: storytelling.

Topics include: Creative intelligence • Creative data • Generative AI • Vidmob360 • MCP and AI agents • Creative optimization • Brand sovereignty • Walled gardens • Creative measurement • Content abundance • Advertising automation • Creative fatigue • Human creativity • The changing role of designers

#SignalAndNoise #AlexCollmer #Vidmob #CreativeIntelligence #CreativeData #GenerativeAI #Advertising #AdTech #Marketing #MarketingTechnology #CreativeTechnology #AIAdvertising #AgenticAI #AIAgents #MCP #BrandStrategy #DigitalAdvertising #CreativeOptimization #MarketingAI #Media #PerformanceMarketing #CreativeStrategy #FutureOfAdvertising #ContentCreation #BrandMarketing

Retail media was built on a powerful promise: connect advertising directly to transactions and finally show marketers what their media dollars produced. But as the category matures, a harder question is replacing simple attribution: Did the advertising actually cause the sale?

In this episode of Signal & Noise, Rio Longacre and Brett House sit down with Jeffrey Cohen, Chief Business Development Officer at Skai and former Principal Evangelist at Amazon Ads, for a wide-ranging conversation about the evolution of commerce media—and what happens as intelligent agents begin taking over work once performed through dashboards, spreadsheets, rules engines, and manual campaign management.

Jeff had a front-row seat during Amazon Ads’ extraordinary rise. He explains what Amazon got right, from the simplicity of “search, find, buy” to its ability to connect media exposure with actual commerce. But from outside Amazon, he now sees the larger challenge: consumers don’t live inside walled gardens. TikTok can drive an Amazon purchase. Prime Video can influence a Walmart sale. In-store activity affects digital behavior, and digital media influences what happens on the shelf.

That creates a measurement reckoning. ROAS and last-touch attribution can tell marketers what received credit for a transaction, but not necessarily what caused it. The conversation digs into incrementality, cross-channel effects, independent measurement, and why historically siloed media, shopper marketing, trade promotion, and commerce teams increasingly need to operate together.

Then the discussion moves from dashboards to agents.

Jeff explains how Skai is approaching this transition through Celeste, Skai Studio, MCP connectivity, and agentic workflows. Instead of asking an AI assistant for an insight, marketers can build systems capable of analyzing signals, finding audiences, recommending budgets, creating campaigns, monitoring performance, and escalating decisions to humans for approval.

The goal, Jeff argues, shouldn't be another marketing black box, but a glass box: automation where marketers can understand the logic behind decisions, establish guardrails, audit actions, and retain control over critical choices—especially where budgets are concerned.

One particularly striking finding: Jeff says 45% of the questions asked through Celeste could not have been answered manually within the traditional Skai platform. Agents aren't simply making analytics faster; they're combining data and performing analysis that previously required people to jump between systems, download reports, manipulate data, and assemble the answer themselves.

The implications extend far beyond media buying. Jeff describes Skai's own company-wide agentic transformation and the emergence of a new kind of marketer: the builder—someone who may not be a traditional software engineer but understands systems, processes, data, business rules, and how to turn them into intelligent workflows.

In this episode:

• How Amazon Ads became an advertising giant
• Why “search, find, buy” proved so powerful
• Attribution vs. incrementality
• Why platforms end up “grading their own homework”
• The convergence of retail media, CTV, social, and in-store
• Celeste, Skai Studio, MCP, and agentic workflows
• Why agents need guardrails, auditability, and human oversight
• Why major budget decisions still require human approval

• Why AI transformation is really operating-model transformation
• The marketer as a builder
• Why companies need to rethink workflows instead of automating them

The big question is no longer whether AI can optimize another campaign or generate dashboards. It’s what decisions we delegate, what governance those systems require, and where human judgment remains essential.

Hosted by Rio Longacre and Brett House on Signal & Noise.

#RetailMedia #CommerceMedia #AmazonAds #AdTech #MarketingTechnology #AgenticAI #MarketingAI #RetailMediaNetworks #Incrementality #Advertising #MediaBuying #Skai #DigitalAdvertising #SignalAndNoise

AI agents can retrieve, combine, analyze, infer from, and act on enormous amounts of data—often at a speed no human compliance team can match. But access to data does not automatically confer the right to use it. So who sets the rules, and who remains accountable when an AI system crosses the line?

In this episode of Signal & Noise, Brett House and Rio Longacre sit down with Richy Glassberg, Co-Founder and CEO of SafeGuard Privacy, for a candid and wide-ranging conversation about privacy, consent, AI governance, and the digital advertising industry’s long history of creating problems it later asks technology to solve.

Richy brings a rare perspective to the discussion. He helped build CNN.com’s commercial business, co-founded the IAB, worked across publishing, agencies, ad tech, and media, and now leads a company focused on making privacy compliance and vendor diligence standardized, operational, and auditable.

The conversation begins with a provocative argument: AI may not require an entirely new category of privacy law because AI is ultimately software—and existing rules governing data use, discrimination, consent, and accountability still apply. The real challenge is enforcing those rules as AI dramatically increases the speed, scale, and complexity of data use.

Richy explains why companies are now responsible for privacy compliance throughout their vendor chains, including the DSPs, publishers, data brokers, identity providers, models, APIs, and other partners involved in a transaction. When one black box passes data to another black box—and AI begins making decisions across the entire chain—policies and promises are no longer enough. Organizations need standardized diligence, enforceable controls, ongoing monitoring, and proof.

The group also examines why today’s consent system is fundamentally broken. Cookie banners have created consent fatigue without giving consumers meaningful understanding or control. Privacy policies are rarely read, permissions do not travel cleanly across platforms, and people can opt out in one place only to reappear in the same identity graph somewhere else.

Other topics include:

• Why an AI agent should never have more authority than the person or organization it represents
• The tension between giving AI more context and protecting individual privacy
• Why human oversight remains essential in agentic systems
• How marketers should assess and monitor every company handling their data
• Why privacy diligence must become machine-readable for real-time agent decisions
• The failure of one-to-one targeting and the industry’s obsession with questionable audience data
• How poor frequency management is damaging the connected TV experience
• Why better privacy practices could become a mark of data quality and competitive differentiation
• The threat AI-generated content poses to trusted information and the open internet
• Whether consumer-controlled data and permission agents could produce a healthier advertising ecosystem

It’s a funny, blunt, and occasionally uncomfortable conversation about what responsible data use should look like when machines can move faster than the institutions meant to govern them.

Learn more about SafeGuard Privacy: https://safeguardprivacy.com/

#ArtificialIntelligence #DataPrivacy #AIPrivacy #AIGovernance #Consent #DigitalAdvertising #AdTech #AgenticAI #PrivacyTech #MarketingTechnology #DataGovernance #ProgrammaticAdvertising #SignalAndNoisePodcast

Everyone is talking about AI. Far fewer people have spent decades actually building AI and data systems inside some of the world’s largest organizations.

In this episode of Signal & Noise, Brett House and Rio Longacre sit down with Zoher Karu, Head of AI at Taelor, to separate AI hype from what it actually takes to create measurable business value.

Zohar brings an unusually broad perspective. His career has taken him through McKinsey, Sears, Citi, eBay, Blue Shield of California, and now Taelor—an AI-powered men’s clothing rental service attempting to combine machine intelligence with human styling expertise.

Across those very different businesses, Zohar argues that the same lesson keeps resurfacing: the technology is rarely the hardest part.

The conversation starts with one of enterprise AI’s least glamorous truths: bad data doesn’t disappear because you put an LLM on top of it. As Zoher puts it, AI can simply give you “bad answers faster.” Data governance, business processes, organizational knowledge, and change management remain foundational.

From there, the discussion gets practical. Zoher explains how Taelor is attempting to teach machines something surprisingly difficult: taste. Matching clothes to a person requires understanding not just size and style, but weather, occasion, context, individual preferences, previous feedback—and even whether two individually appropriate pieces of clothing actually work together. That becomes a window into a much bigger conversation about the future of personalization.

Generative AI dramatically expands the amount of customer context businesses can process, how quickly they can respond to new signals, and the number of individualized experiences they can create. Instead of choosing among three versions of an email, brands could theoretically generate an almost infinite number of variations for individual customers.

The discussion also tackles the uncomfortable economics of enterprise AI. Companies are spending enormous amounts on models, infrastructure and tokens—but are they actually redesigning the business processes required to capture the ROI?

Zoher argues that automating pieces of an existing workflow may deliver incremental efficiency, while the much larger opportunity comes from asking whether that workflow should exist at all.

Finally, the conversation explores what may become one of the most important issues in enterprise AI: context. Agents can access data, but data alone doesn't contain all the rules, judgment and institutional knowledge humans use to make decisions. Capturing that tacit business knowledge—and making it available to AI systems—could become a critical source of competitive advantage and intellectual property.In this episode:

* Why dirty data can derail even sophisticated AI

* Why AI transformation is really organizational transformation

* The gap between AI spending and measurable ROI

* Why simply automating existing processes isn't enough

* How AI is changing personalization and recommendation systems

* How Taelor combines human stylists with machine intelligence

* Why context and business knowledge matter as much as models

* Whether AI is actually eliminating jobs or simply changing them

* Why change management may be the biggest barrier to enterprise AI

* The continuing importance of human judgment in increasingly autonomous systems

The companies that win the AI race may not be the ones with the most sophisticated models. They may simply be the ones that figure out how to build AI that people actually use.

#ArtificialIntelligence #AI #EnterpriseAI #GenerativeAI #AgenticAI #Personalization #CustomerExperience #DataStrategy #DataGovernance #MachineLearning #DigitalTransformation #AITransformation #ChangeManagement #MarTech #RecommendationEngines #FutureOfWork #SignalAndNoise #Podcast

Artificial intelligence may be the most transformative technology of our generation—but according to Fern Potter, AI alone is not a strategy.In this episode of Signal & Noise, hosts Rio Longacre and Brett House sit down with Fern Potter, Co-Founder of Intelligent Assistance, to explore why the greatest opportunity in AI isn’t replacing people—it’s amplifying human judgment.After more than two decades leading strategy, product, partnerships, and commercial growth across agencies, media, and ad tech—including serving as Chief Strategy & Growth Officer at Multilocal—Fern made the leap to entrepreneurship. Alongside her co-founders, she launched Intelligent Assistance around a simple but powerful philosophy: AI creates the most value when it enhances human expertise, context, creativity, and accountability rather than attempting to eliminate them.The conversation begins with Fern’s journey from agency leadership to founding an AI company at a moment when enterprises are rushing to deploy generative AI. She explains why so many organizations start with technology instead of business problems—and why that approach almost always leads to disappointing results.From there, the discussion explores what “intelligent assistance” actually means in practice. Fern explains how organizations should determine which work should be automated, which decisions should remain firmly human, and how AI can become a force multiplier instead of another disconnected productivity tool.Rio and Brett also dive into one of the episode’s central themes: the difference between intelligence and autonomy. Just because AI can make a decision doesn’t mean it should. Fern discusses the critical role of context, accountability, governance, and human oversight as organizations increasingly rely on AI-assisted workflows.Drawing on her experience helping reshape programmatic advertising through curation and supply-side innovation, Fern shares lessons that extend far beyond media. The trio explores how unchecked automation created inefficiencies and opacity in advertising—and why enterprise AI risks repeating many of the same mistakes if organizations optimize solely for automation instead of outcomes.The conversation also tackles larger questions about the future of work. What happens to agencies, consultancies, and professional services when small AI-enabled teams can accomplish what once required dozens of people? Which human capabilities become more valuable as technical execution becomes increasingly automated? And how should leaders redesign organizations around “thinking power” rather than simply reducing headcount?Whether you’re leading AI initiatives, building products, transforming marketing organizations, or simply trying to understand what responsible AI adoption looks like, this episode offers a thoughtful, practical framework for moving beyond the hype toward meaningful business impact.In this episode, you’ll learn:* Why AI is not a business strategy* The difference between automation, autonomy, and intelligent assistance* Why most enterprise AI initiatives fail to deliver commercial value* How to combine AI with human judgment for better outcomes* Lessons enterprise AI can learn from programmatic advertising* Why organizational redesign matters more than technology deployment* Which uniquely human skills become more valuable in the AI era* How leaders should think about governance, accountability, and trustIf you enjoy conversations about AI strategy, marketing transformation, organizational design, and the future of work, be sure to subscribe to Signal & Noise for weekly conversations with the leaders shaping the future of business and technology.#SignalAndNoise #ArtificialIntelligence #AI #GenerativeAI #FutureOfWork #HumanCenteredAI #Leadership #BusinessTransformation #Marketing #MarTech #AdTech #DigitalTransformation #EnterpriseAI #Innovation #Technology #IntelligentAssistance