ServiceNow Podcasts

In this special rant episode, Sonny Rivera joins Juan and Tim during a live recording and rant about AI-generated dashboards, semantic layers, and what it takes to trust an answer. The situation: dashboards slop is easy to build which makes foundation matter more, not less. We discussed:

• Building a dashboard was never the hard part. Being able to explain where a number came from is.
• Speed without a governed foundation produces wrong answers faster. As Sonny put it, “fast and wrong is still wrong.”
• Modeling data is really modeling the business. The job has not changed. How it gets done is changing.
• A single source of truth did not hold up. Favors an interoperable set of semantic layers and context, with shared definitions where people actually care.
• Logic written in the moment, in a dashboard or a pipeline, often never makes it back into the semantic layer. Closing that loop is the gap to fix.
• Friction between teams is a sign that a definition matters. Make the differences visible rather than forbidding them.
• Trust is what data leaders can least afford to lose, which is why testing belongs in dashboards too.
• Writing code is no longer the slow part. Product management and testing are becoming the bottlenecks.

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Nick Zervoudis, data product manager, trainer, and consultant at Value from Data & AI, joins Juan and Time to talk about why many data teams end up working like a service desk and what to do about it. The answer is: DO DISCOVERY! In this conversation, we cover how a service desk model breaks down when a request can’t be described in a few words, why dashboards get opened once and then forgotten, and how product thinking differs from project management in data work. We also discuss how to start doing discovery without a full transformation, and what AI does and doesn’t change about that work.

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This is the takeaway episode with Nick Zervoudis, data product manager, trainer, and consultant at Value from Data & AI, joins Juan and Time to talk about why many data teams end up working like a service desk and what to do about it. The answer is: DO DISCOVERY!

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Mohammed Aaser, former chief data officer at McKinsey and former chief data strategy officer at Domo, recently visited Juan and shared his framework on talent in the AI era. It was incredibly insightful and we asked if he could come back to the podcast (after 5 years!) His argument is that the potential of AI will be unlocked by humans because humans understand the problems, and are driven/motivated to solve the problems.

In this episode, Mohammed lays out a framework for talent in the AI era. Automation is already taking over the known, repeatable work. What remains is the unknown, and that takes people who keep learning and who take initiative. The framework plots people on two dimensions, learning and drive. You have Doers, Philosophers, Commanders, Hackers, Strategist, Innovators, Game changers and more. Topics discussed

• Automation and the innovation engine
• The known versus the unknown
• Two dimensions of talent
• The talent quadrants
• The multiplier effect
• Commanders and scaling
• Environments that don’t build initiative
• Rarity of high-potential people
• Investing in human potential
• The human mind as the limit
• Critical thinking through discomfort

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This is the takeaway episode with Mohammed Aaser, former chief data officer at McKinsey and former chief data strategy officer at Domo. He recently visited Juan and shared his framework on talent in the AI era. It was incredibly insightful and we asked if he could come back to the podcast (after 5 years!) His argument is that the potential of AI will be unlocked by humans because humans understand the problems, and are driven/motivated to solve the problems.

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How do leaders preserve executive trust and credibility? Dora Boussias, executive advisor and former data executive returns to the podcast to unpack this question. Her answer: protect your judgement.

In this episode, Dora presents the trust equation that most leaders grew up with: expertise + track record + judgment. Dora’s point is that the equation still holds, but each part now means something different. Nobody will out-know AI, so expertise is less about what you know and more about asking the right questions and challenging assumptions. A track record is harder to read when you can’t tell whether a good result came from you or from the model. Judgment carries the most weight now, and it’s the part leaders are most likely to hand off without noticing. Topics discussed:

• Trust equation under AI
• Expertise is changing
• Track record is harder to read
• Judgment carries the most weight
• Decision fog
• Speed isn’t everything
• Ford’s rehiring of experienced engineers
• Business fundamentals matter more
• Alignment and accountability are cross-functional
• Real value is “so what”

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This is the takeaway episode with Dora Boussias, executive advisor and former data executive. We unpack the question "How do leaders preserve executive trust and credibility?" Her answer: protect your judgement. In this episode, Dora presents the trust equation that most leaders grew up with: expertise + track record + judgment. Don't miss it!

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Agents will build data infrastructure at every layer and become its primary consumers. Tristan Handy, President and Co-founder of Fivetran / dbt Labs joins Juan and Tim where we unpack the big shift that is happening: teams need to move from working in the process (picking up individual tickets) to working on the process (building the factories that handle those tickets). In this episode, we break down what this means for data roles, why open components matter more than vendor stacks, and the socio-political problem nobody's talking about, deciding what counts as truth so your agents (and humans) ask the right questions in the first place. Topics discussed:

• Agents as builders and consumers
• Data roles are collapsing
• From working IN to working ON
• Loop building as the competitive advantage
• Business understanding beats technical specs
• From humans in the loop to humans out of the loop
• Open components over vendor stacks
• Defining truth is a political problem
• Data observability becomes business observability
• Real-time becomes necessary
• Humans delegate outcomes, set goals, sit in control

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This is the takeaway episode with Tristan Handy, President and Co-founder of Fivetran / dbt Labs where he joins Juan and Tim to discuss how agents will build data infrastructure at every layer and become its primary consumers. We unpack the big shift that is happening: teams need to move from working in the process to working on the process, what this means for data roles, why open components matter more than vendor stacks, and the socio-political problem nobody's talking about, deciding what counts as truth so your agents (and humans) ask the right questions in the first place.

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#Localization #Globalization #ServiceNow #AI

A translation can be word-for-word accurate and still be completely wrong. In this episode, Bobby Brill sits down with Sam Smyth, Product Manager on the globalization side at ServiceNow, to get into what happens after you decide to go multilingual — the tools, the governance, and the quiet work nobody sees until it breaks.

Sam spent fifteen years in localization operations and management before joining ServiceNow, and he's blunt about what actually goes wrong: it's rarely the translator. It's the content you sent them. We get into Localization Workspace, the new globalization agent that builds a glossary from your knowledge articles in minutes, why every customer still insists on a human in the loop, and the one mistake that will break your product UI in six languages at once.

Past Localization episodes:
https://youtu.be/4ka4qLk8Uj0
https://youtu.be/cGXCnABXKow

Guest: Sam Smyth, Product Manager - ServiceNow

Chapters
00:00 "People don't see localization until it goes wrong"
00:22 Translation vs. localization — a quick recap
00:56 Meet Sam Smyth: fifteen years in localization
01:37 When a perfect translation is still wrong
03:34 Why customers don't want to leave the platform
04:07 Localization Workspace: no more spreadsheets and email chains
05:13 The surprise: everybody still wants a human in the loop
05:38 Speed, cost, quality — picking all three
06:55 Inside the globalization agent 08:50 Who approves the terms? (And which shade of green?) 09:55 Language governance and the invisible costs
11:49 AI, volume, and doing the work upstream
13:02 Where to actually start with a glossary
16:28 A day in the life of a localization program manager
18:56 Shifting left: from firefighting to strategy
20:35 The one mistake: never hardcode your UI
22:10 Wrap-up

ServiceNow Training and Certification:
https://www.servicenow.com/services/training-and-certification.html

ServiceNow Insights podcast playlist:
https://www.youtube.com/playlist?list=PLCOmiTb5WX3qvGq7Cp3o2KkCiplJyqQOK

Servicenow.com: https://www.servicenow.com

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AI doesn't create your problems. It inherits the business choices you never made and the results nobody owns. David Krauza, VP of Data Strategy and Governance at Comcast, joins Juan and Tim to make the case that most "data problems" aren't data problems at all. They are accountability problems, definition problems, assumption problems, and what-are-we-even-measuring problems. The fix isn't better tooling. Data teams are so focused on delivery that they never stop to ask what the business is actually trying to do. It's going to the C-suite, asking about the top three goals, and following the definitions until the cracks show up. Topics discussed:

• AI inherits your unmade decisions
• The output trap
• Definitions are the real foundation
• Talk to the business
• Data as a cost center vs. a value driver
• Single source of data, multiple versions of the truth
• Think like a product owner
• The revenge of the liberal arts majors

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This is the takeaway episode with David Krauza, VP of Data Strategy and Governance at Comcast who makes the case that AI doesn't create your problems, it inherits the business choices you never made and the results nobody owns. David joins Juan and Tim and discuss the accountability problems, definition problems, assumption problems, and what-are-we-even-measuring problems.

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Most quantum conversations are stuck on the timeline question: five years out? Two? Q-Day, the day current RSA encryption breaks, keeps getting pulled forward. But the actual risk to your data is already here. Adversaries are harvesting encrypted data today to decrypt it once quantum gets there. That makes this a data management problem right now.

John Licata, ServiceNow's futures director and quantum lead joins Juan and Tim, to unpack what this actually means for enterprise data teams. They get into where quantum computing does things classical AI genuinely can't do well: drug discovery, supply chain simulation, personalized medicine, where the quantum + AI combination changes everything, and who even owns quantum strategy? CISO? COOs? But the real argument is on the business value topic, not just focusing on risk mitigation which is where the real argument lives right now.

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This is the takeaway episode with John Licata, ServiceNow's futures director and quantum lead where Juan and Tim unpack what quantum means for enterprise data teams.They get into where quantum computing does things classical AI genuinely can't do well: drug discovery, supply chain simulation, personalized medicine, where the quantum + AI combination changes everything, and who even owns quantum strategy? CISO? COOs? But the real argument is on the business value topic, not just focusing on risk mitigation which is where the real argument lives right now.

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What did I say? What did I mean? Do you understand me? We ask each other those three questions constantly without noticing. Now we ask them of machines — and this episode is about what happens in the gap.

Host Bobby Brill pulls together conversations with the ServiceNow linguists, research engineers, and AI governance leads working on voice, language, and chat, including a real recording of a voice agent failing in real time. Same words. Different meaning. And a machine in the middle trying to work out which one you intended.

In this episode:
Why a single English word carries no meaning without context, and what that does to translation
How a name that isn't in the training data breaks the first model in the chain
The three-model cascade behind every voice agent, and what gets lost at each handoff
A voice agent that hears a confirmation code correctly three times and rejects it three times
Why formality — tu or vous — is a fluency problem, not a grammar problem
Who decides what's acceptable for an AI to say when the same word is fine in London and not in Chicago.

Check out our past episodes on voice and voice AI:
https://youtu.be/cGXCnABXKow
https://youtu.be/yxoHmZj5gOk
https://youtu.be/x7Ks932T18o

Guests:
Lyena Solomon, Director of Globalization and Accessibility
Midam Kim, Machine Learning Engineer
Tara Bogavelli, Research Engineer
Katrina Stankovic, Staff Machine Learning Engineer
Gabrielle Gauthier-Melançon, Staff Applied Research Scientist
Louis-Philippe Morin, AI Governance Product Manager

CHAPTERS

0:00 "Half ten" — same words, an hour apart
2:04 What did I say: the word "order"
3:19 The name a machine can't hear
5:23 Three models handing meaning to each other
8:19 Naming conventions 10:04 Listen: a voice agent fails
14:47 Tu or vous: talking to you correctly
16:39 Who decides what's acceptable
18:49 Teaching it the way you'd teach a kid
20:15 Why any of this matters

For more information, see:

ServiceNow Training and Certification: http://www.servicenow.com/services/training-and-certification.html

ServiceNow Community: https://community.servicenow.com/community

ServiceNow Insights Podcast: https://www.youtube.com/playlist?list=PLCOmiTb5WX3qvGq7Cp3o2KkCiplJyqQOK

For general information about ServiceNow, visit: http://www.servicenow.com/

#ServiceNow #VoiceAI #AI #ConversationalAI #Localization #AIGovernance

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Shachar MEIR has spent 20 years fixing data teams and understanding why they fail. In this live episode, Shachar joins Juan and Tim to discuss the reason why they fail and what they should be doing in order to succeed.

Shachar argues that data leaders default to what they can control (cough cough technology_ and underinvest in people, process, and culture. A new CDO comes in, migrates legacy databases to a cloud data platform, and two years later you have the same problems on a better platform.

We get into what's actually missing: building trust in data (which takes time and breaks fast), creating incentives so people actually use what data teams build, and spending real time in the business which is not asking what dashboards they need, but what problems they have.

Topics discussed:

• Technology is not why data teams fail
• Why data leaders default to technology
• People, process and CULTURE
• Data trust
• Incentives
• Understanding the business
• Dashboard bloat
• Self-service analytics
• Dashboards aren't dead
• What CDOs do well and what they miss

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This is the takeaway episode with Shachar Meir, who has spent 20 years fixing data teams and understanding why they fail. In this live episode, Shachar joins Juan and Tim to discuss the reason why they fail and what they should be doing in order to succeed.

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Patrick McGarry, Federal CDO at ServiceNow and author of The Adaptive Organization, has spent years watching organizations accumulate data and tools but fail to convert them into decisions. Overhead grows. Outcomes don't. Hence “Intelligence Without Action Is Just Overhead.” Patrick joins Juan and Tim to walk through why federal data programs stall (the idea-to-deployment lag is long enough that the tech moves on), why incentives matter more than mandates, and why the right question should not be "how do we get AI ready" and instead should be "how do we make our organization accountable for AI."

Topics discussed:

• Intelligence vs. action
• Ownership and outcomes
• Federal government's speed problem
• The CATALOG framework: Culture & Talent, Analytics & AI, Technology & Architecture, Alignment, Leadership & Governance, Operations & Delivery, Growth & Measurement.
• Governance as clarity, not bureaucracy
• Open standards, vendor lock-in, semantics, interoperability
• Regulatory standards as a double-edged sword

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This is the takeaway episode with Patrick McGarry, Federal CDO at ServiceNow and author of The Adaptive Organization, who has spent years watching organizations accumulate data and tools but fail to convert them into decisions. Overhead grows. Outcomes don't. Hence “Intelligence Without Action Is Just Overhead.”

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Most companies slapping the "data product" label on existing data assets are fooling themselves. Bethany Sehon, Enterprise Data Leader at Capital One, has been building data products and ontologies in production long before they became buzzwords. Bethany joins Juan and Tim to share lessons learned: dedicated data product managers (not side-of-desk), ontologists treated as first-class citizens, a VP-accountable governance process, and much more.

Topics discussed:

• Data products as a discipline, not a label
• Governance process as the forcing function
• Dedicated roles, not side-of-desk work
• Ontology as a strategic function
• Standardization is necessary but not sufficient
• Less is more on scope
• Trust is consumer-specific
• AI expands what counts as a data product

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