ServiceNow Podcasts

Can't believe it's already been 6 years. Thank you to our amazing guests and specially our listeners. In this season finale episode, Juan and Tim rant about the honest no-bs discussions they've had in 2026 and what are the topics they are looking forward to cover in the next season. 

What does it actually mean to be AI native? Not the buzzword — the real thing. Host Bobby Brill brings together seven ServiceNow experts across six conversations for a complete picture of what AI native thinking, building, and working looks like right now. ━━━━━━━━━━━━━━━━━━━━━━━━ WHAT WE COVER ━━━━━━━━━━━━━━━━━━━━━━━━ DI LE — AI Ethicist & Human-Centered AI Strategist, ServiceNow The clearest definitions you'll find anywhere of responsible AI, ethical AI, and human-centered AI — and why all three are required if you're going to do this right. Plus: why AI native means AI as the operating system, not a feature. DR. ALAINA BEAVER — Global Head of Accessibility Customer Engagement, ServiceNow ServiceNow built the world's first AI model accessibility checker with the Global Accessibility Awareness Day Foundation — and open-sourced it on GitHub for free. Because responsible AI native behavior means holding AI itself accountable. ANAND THARANATHAN — Research Leader, ServiceNow A framework from cognitive science every AI builder needs: use, disuse, misuse, and abuse. The four modes of AI interaction — and why proper use is the only one that delivers. TARA BOGAVELLI & KATRINA STANKIEWICZ — Voice AI Research Team, ServiceNow How ServiceNow built a rigorous open-source evaluation framework for voice agents from scratch — and what cascade failures, transcription errors, and prosody failures actually sound like in practice. IAN THURLOW & ANDREW YAN — Software Engineering Manager & Software Engineer, ServiceNow The daily ground-floor reality of being AI native: AI as accelerator, AI as the new Stack Overflow, the calculator analogy, and why fundamentals matter more than ever. ━━━━━━━━━━━━━━━━━━━━━━ LEARN MORE ━━━━━━━━━━━━━━━━━━━━━━━━ ServiceNow Responsible AI: https://www.servicenow.com/responsible-ai AI Model Accessibility Checker: https://www.servicenow.com/accessibility-statement.html ServiceNow AI: https://www.servicenow.com/artificial-intelligence ━━━━━━━━━━━━━━━━━━━━━━━━ ABOUT THIS PODCAST ━━━━━━━━━━━━━━━━━━━━━━━━ Hosted by Bobby Brill. A ServiceNow podcast exploring the people, technology, and ideas shaping the future of work. #AINative #ServiceNow #ResponsibleAI #HumanCenteredAI #AIEthics #EnterpriseAI #FutureOfWork #NowAssist #ArtificialIntelligence #Podcast

Jason Doerr has spent years watching governance programs undermine themselves by cataloging everything without a use case, naming data stewards who have nothing to actually do, and building central teams that become blockers instead of enablers. In this episode, he walks through what pragmatic governance actually looks like: start with use cases, give stewards real work to action on, and let the central team set principles rather than police behavior. He also digs into PADU (Preferred, Acceptable, Discouraged, Unacceptable) as a practical roadmap framework, how LLMs can accelerate semantic layer creation without generating vanity metrics, and why the governance operating model is shifting toward agentic management ... whether the governance community is ready for it or not.

This is the takeaway episode with Jason Doerr who has spent years watching governance programs undermine themselves. He walks through what pragmatic governance actually looks like and digs into PADU (Preferred, Acceptable, Discouraged, Unacceptable) as a practical roadmap framework. 

Bob Seiner has spent decades in data governance, and in this episode he joins Juan and Tim to unpacks his new framework, Data Catalyst Cubed, which multiplies data governance by change management and data fluency. Miss any one of them, and you get zero. The problem is that leadership has never treated behavior change as part of the governance mandate, and most data programs have never connected with the change management expertise that already exists inside their organizations. 

Takeaway episode with Bob Seiner where he unpacks his new framework, Data Catalyst Cubed: multiplies data governance by change management and data fluency. Miss any one of them, and you get zero. 

Day One Ready: What New Engineers Actually Need to Know About AI | Engineering Now Unlocked Starting your first engineering role — or coming back for a return offer — and wondering what AI actually changes about the job? This episode gives you the real answer, from two engineers living it every day. Jordan Shelton and Cynthia Mathenge sit down with Ian Thurlow (Senior Manager, Data Platform Software Engineering) and Andrew Yan (Software Engineer, Data Foundations) to talk about what day one looks like now, what AI tools actually do for early-career engineers, and what fundamentals still separate good engineers from great ones. If you’re about to start an internship, just got your return offer, or you’re a manager thinking about how to set new engineers up for success — this is the conversation you need before day one. What you’ll learn ✔ What AI actually changes about day-to-day engineering work (and what it doesn’t) ✔ Why the fundamentals matter more than ever — not less ✔ How to build a network at a company like ServiceNow, even if you start remotely ✔ How to use AI as a sounding board, not a crutch Chapters 00:00 Introduction — Engineering Now Unlocked 02:08 Meet Ian Thurlow and Andrew Yan 03:03 How AI is changing day-to-day engineering work 04:47 AI as an accelerator, not a replacement 09:04 AI as a sounding board  12:38 Leadership mindset in an AI-first team 13:46 Raising the bar for early-in-career talent 1 5:41 What your first 30 days should look like 17:43 This or That 19:16 Code reviews: the fastest way to learn that nobody talks about 20:57 Building your network — even fully remote 24:24 Ian and Andrew’s Work Advice 27:43 Outro Guests Ian Thurlow Senior Manager, Data Platform Software Engineering — ServiceNow Andrew Yan Software Engineer, Data Foundations — ServiceNow Hosts Jorden Shelton Technical Program Manager, AI Engineering & Delivery — ServiceNow Cynthia Mathenge Business Operations Manager, AI Engineering & Delivery — ServiceNow Bobby Brill ServiceNow Insights Links & Resources Learn more about ServiceNow Engineering → https://www.servicenow.com/company/careers/engineering.html ServiceNow Docs → https://docs.servicenow.com New to the channel? Subscribe so you never miss an episode of ServiceNow Insights.

Most organizations are treating AI governance as a compliance checkbox. Victoria Gamerman argues it's the gateway that finally forces organizations to confront the human side of AI adoption. In this episode, Victoria breaks down why the POC-to-operationalization gap is so hard to close, why people, process, and data each carry more human weight than most leaders acknowledge, and what "AI-ready data" actually means and a reminder that AI isn't a technology problem. It never was.

This is the takeaway episode with Victoria Gamerman who argues that AI Governance is the gateway that finally forces organizations to confront the human side of AI adoption. 

Everybody wants AI. AI adoption conversations are dominated by tools, models, and metrics. Far fewer organizations have figured out what to do with it once it's inside the building. The harder question, one that most leaders are avoiding, is what happens to the humans? Diana Wu David, Director of Futures at ServiceNow joins Juan and Tim to unpack what leaders should and should not be doing. 

This is the takeaway episode with Diana Wu David, Director of Futures at ServiceNow where we discuss AI adoption, the metrics and how far fewer organizations have figured out what to do with AI once it's inside the building.

Voice AI agent evaluation — why it's fundamentally harder than text, how cascade failures derail conversations invisibly, and ServiceNow's open-source framework to establish industry evaluation standards. Featuring real audio examples showing authentication failures, leaked reasoning, and latency problems. WHAT WE COVER  TARA BOGAVELLI — Research Engineer, ServiceNow Leading the open-source voice agent evaluation framework. Explains why existing benchmarks don't measure what matters and what ServiceNow is releasing to establish industry standards. KATRINA STANKIEWICZ — Staff Machine Learning Engineer, ServiceNow Cascade model architecture expert. Breaks down STT → LLM → TTS failure modes, named entity transcription challenges, and real audio example analysis. GABRIELLE GAUTHIER MELANÇON — Staff Applied Research Scientist, ServiceNow Multi-language evaluation specialist. Reveals why Large Audio Language Models lag behind, the native speaker requirement, and bot-to-bot simulation methodology.  CHAPTERS 0:00 Introduction — The evaluation gap 1:11 ServiceNow's Open-Source Framework Announcement — Tara Bogavelli 2:43 Meet the Researchers 3:43 Voice-Specific Challenges — Tara Bogavelli 5:03 Cascade Architecture: STT → LLM → TTS — Katrina Stankiewicz 7:57 The Named Entity Problem — Katrina Stankiewicz 10:06 Evaluation Metrics: Accuracy vs Experience — Gabrielle Gauthier Melançon 11:23 Bot-to-Bot Testing at Scale — Gabrielle Gauthier Melançon  14:30 The LALM Gap: Why Audio AI Judges Struggle — Tara Bogavelli 16:57 Real Audio Example: Flight Rebooking Gone Wrong 21:58 Breaking Down the Failures — Katrina Stankiewicz 28:30 Wrap-Up & Resources KEY INSIGHTS The Cascade Failure Problem: STT → LLM → TTS errors propagate invisibly Named Entity Transcription: The #1 enterprise blocker—names, confirmation codes, emails break authentication Accuracy vs Experience: Perfect task completion means nothing if users hang up due to poor experience LALM Gap: Large Audio Language Models lag behind text LLMs—human evaluators remain essential Latency Kills Conversations: Five-second pauses make users think the call dropped, breaking the experience even when tasks complete Open-Source Framework: ServiceNow releasing evaluation tools, metrics, and bot-to-bot simulation methodology for the industry. LEARN MORE Website: https://servicenow.github.io/eva/ GitHub: https://github.com/servicenow/eva Blog Post: https://huggingface.co/blog/ServiceNow-AI/eva Dataset: https://huggingface.co/datasets/ServiceNow-AI/eva ABOUT Hosted by Bobby Brill. ServiceNow Insights podcast explores AI research, real-world applications, and the people building the future of work. #VoiceAI #AIEvaluation #ServiceNow #MachineLearning #OpenSource #ConversationalAI #STT #TTS #LLM #VoiceAgents #AIResearch #Podcast

Juan and Tim's Friday rant covers a lot of ground, from Juan's TED takeaways on AI's unprecedented speed and what it means for humanity, to the uncomfortable shift data teams need to make: work and decisions are the point, not pipelines and gold layers. They dig into what Medallion Architecture 2.0 looks like (feedback loops, insights to action, agent governance), why organizational design theory applies directly to agent swarms, and what library science can teach us about the future data stack. The thread running through all of it: the humans who thrive in this moment won't be the ones who build the most, but the ones with taste.

Data teams spend enormous energy building pipelines, platforms, and governance frameworks but often skip the most fundamental step: truly understanding what people are actually asking for. In this episode, Juan and Tim sit down with data librarians Jenna Jordan and Amalia Child to explore why library science may be the missing lens for data work. At the heart of the conversation is the reference interview, a structured technique librarians use to uncover a user's "true information need," which almost never matches the first question they ask. From establishing trust and listening without judgment, to asking open-ended questions and verifying whether the need was actually met, the reference interview offers a rigorous, repeatable framework for anyone serving data users. If you've ever wondered why data projects deliver less value than expected, this episode will reframe the problem entirely and give you a practical toolkit to start closing the gap.

Data teams obsess over pipelines and platforms but often skip the most fundamental step: truly understanding what people are actually asking for. We chat with data librarians Jenna Jordan and Amalia Child who share a framework for exactly that; it's called the reference interview, and it might be the most practical toolkit data teams have never used.

AI governance at scale — what it means, how to do it, and what regulations you need to know now. Host Bobby Brill brings together five ServiceNow experts across two conversations for a complete 20-minute briefing on governing AI in the enterprise. ━━━━━━━━━━━━━━━━━━━━━━━━ WHAT WE COVER ━━━━━━━━━━━━━━━━━━━━━━━━ RAVI KRISHNAMURTHY — VP, AI Platform, ServiceNow Why hidden AI is one of the biggest unmanaged risks in the enterprise — and why governance is an accelerator, not a brake. PETER WEIGT — Responsible AI, ServiceNow The innovation paradox: how AI Control Tower makes governance a team sport and breaks down the silos that slow AI deployment down. SAMPADA CHAVAN — AI Control Tower, ServiceNow How AI Control Tower was built, what the discovery problem really looks like, and why compliance must be baked into the AI lifecycle — not bolted on at the end. ANDREA LAFOUNTAIN — AI Legal, ServiceNow The three regulatory frameworks every enterprise needs to know: EU AI Act, Colorado AI Act, and NIST. Plus: the compliance strategy that scales across all of them. NAVDEEP GILL — Responsible AI, ServiceNow The math on enterprise AI compliance — why it's exponential — and how AI Control Tower's automated discovery keeps you ahead of it. ━━━━━━━━━━━━━━━━━━━━━━━━ CHAPTERS ━━━━━━━━━━━━━━━━━━━━━━━━ 0:00 Introduction 1:23 The Hidden AI Problem — Ravi Krishnamurthy & Sampada Chavan 5:33 AI Control Tower in Practice — Peter Weigt & Sampada Chavan 7:37 The Regulatory Landscape — Andrea LaFountain & Navdeep Gill 14:38 Compliance in Action & Key Deadlines 17:05 Wrap-Up ━━━━━━━━━━━━━━━━━━━━━━━━ KEY DATES TO KNOW ━━━━━━━━━━━━━━━━━━━━━━━━ EU AI Act enforcement: August 2026 Colorado AI Act enforcement: June 2026 NIST AI RMF: Voluntary framework, increasingly referenced by regulators ━━━━━━━━━━━━━━━━━━━━━━━━ LEARN MORE ━━━━━━━━━━━━━━━━━━━━━━━━ ServiceNow AI Control Tower: https://www.servicenow.com NIST AI Risk Management Framework: https://www.nist.gov/artificial-intelligence ━━━━━━━━━━━━━━━━━━━━━━━━ ABOUT THIS PODCAST ━━━━━━━━━━━━━━━━━━━━━━━━ Hosted by Bobby Brill. A ServiceNow podcast exploring the people, technology, and ideas shaping the future of work. #AIGovernance #ServiceNow #AIControlTower #ResponsibleAI #EUAIAct #EnterpriseAI #AICompliance #FutureOfWork #NowAssist #Podcast

There's a gap at the heart of most organisations and data hasn't filled it. Pete Williams, an experienced data leader, has spent years watching companies build sophisticated data capabilities, only to see insight stall before it reaches action. In this episode, we diagnose why: organisations still make decisions along traditional vertical lines, while data sits in a horizontal layer that was never given real authority. AI doesn't solve this misalignment, it exposes it! Pete makes a compelling case for why fixing this structural void is the defining challenge for data leadership today.

This is the takeaway episode with Pete Williams, an experienced data leader, who argues that organisations still make decisions along traditional vertical lines, while data sits in a horizontal layer that was never given real authority. Pete makes a compelling case for why fixing this structural void is the defining challenge for data leadership today.

Juan and Tim have a rant session with a special guest in person, Jesús Barrasa, well known in the knowledge graph space. We covered AI trends, the balance between operational vs. strategic work, knowledge graphs, context graphs. The most valuable part of our rant was our pragmatic discussion about ontologies stripping them down to what they actually are and why they matter. Starting to work with ontologies? Think about these two dimensions: WHAT (Formal, Explicit and Shared Meaning) and WHY (Interoperability, Automation).

Most organisations know they're not getting value from their data and AI investments, but few are willing to name the real culprit: the boardroom. In this episode, Kyle Winterbottom joins Juan and Tim to argue that the mandates handed to CDAOs are fundamentally broken because the people hiring them don't understand what good looks like. Boards continue to build capability without starting with the end in mind, creating a culture where data leaders are rewarded for building things rather than delivering outcomes. The result? An entire industry that has learned to play the wrong game. 

This is the takeaway episode with Kyle Winterbottom where he argues that the mandates handed to CDAOs from the board are fundamentally broken because the people hiring them don't understand what good looks like. The result? An entire industry that has learned to play the wrong game. 

What is Agentic AI — and what can it actually do for your business? In this episode of the podcast, host Bobby Brill brings together three conversations with the people building Agentic AI at ServiceNow into one 20-minute briefing. Whether you're leading a team, managing a platform, or just trying to understand what your company is investing in - this episode gives you the full picture. ━━━━━━━━━━━━━━━━━━━━━━━━ WHAT WE COVER ━━━━━━━━━━━━━━━━━━━━━━━━ RITA CASTILLO — VP of Design, Platform & AI The clearest explainer of Agentic AI you'll find. Rita walks through the full evolution from scripted automation to agentic systems, what makes agentic different, and how the orchestrator-and-agent hierarchy works at ServiceNow. AMRUTHA RAMESH — Visiting Researcher, ServiceNow Amrutha built Agent Ada — a real AI agent designed to turn enterprise data into actionable insights. She explains how Ada understands your goal, adapts to who's asking, and delivers analysis your team can actually use. ISSAM LARADJI & ANITHA RAGHAVAN — Research Scientist & Product Manager, ServiceNow How do you know if your AI's insights are trustworthy? Issam and Anitha built InsightBench — a benchmark that uses AI to evaluate AI — to answer exactly that question. ━━━━━━━━━━━━━━━━━━━━━━━━ CHAPTERS 0:00  Introduction 1:30  What Is Agentic AI? — Rita Castillo, VP of AI Design 6:00  How Agents Work as a Team 10:30  Meet Agent Ada — Amrutha Ramesh, Visiting Researcher 16:00  Can You Trust Your AI? — Issam Laradji & Anitha Raghavan 21:30  Wrap-Up ━━━━━━━━━━━━━━━━━━━━━━━━ GUESTS ━━━━━━━━━━━━━━━━━━━━━━━━ Rita Castillo — VP, AI Design at ServiceNow Amrutha Ramesh — Visiting Researcher, ServiceNow Issam Laradji — Research Scientist, ServiceNow Anitha Raghavan — Product Manager, ServiceNow ━━━━━━━━━━━━━━━━━━━━━━━━ LEARN MORE ━━━━━━━━━━━━━━━━━━━━━━━━ ServiceNow Agentic AI: https://www.servicenow.com More episodes: Narrative Analytics with Agent Ada: https://youtu.be/k1P9o2glq90 InsightBench & AgentPoirot: https://youtu.be/0_ikQQ82qAk Understanding Agentic AI with Rita Castillo: https://youtu.be/JyFdcQChYLs ━━━━━━━━━━━━━━━━━━━━━━━━ ABOUT THIS PODCAST ━━━━━━━━━━━━━━━━━━━━━━━━ Hosted by Bobby Brill. ServiceNow INsights podcast explores the people, technology, and ideas shaping the future of work. #AgenticAI #ServiceNow #AIAgents #ArtificialIntelligence #FutureOfWork #EnterpriseAI #DataAnalytics #NowAssist #MachineLearning #Podcast

Stop treating AI governance like a compliance checkbox handed down from the boardroom. Kierra Dotson joins Juan and Tim to argue that the chasm between ambitious AI strategy and messy technical reality exists precisely because we've made governance someone else's problem. The fix? Empower engineers, data scientists, and product owners to own their role in shaping ethical, sustainable AI — not as a burden, but as the thing that makes the work actually matter.

This is the takeaway episode with Kierra Dotson where she argues that the chasm between ambitious AI strategy and messy technical reality exists precisely because we've made governance someone else's problem. 

AI is the New UI… But Is Anyone Getting Value? Big summer. New roles. Same obsession with transformation. In this episode, Pete and Kat compare notes on what happens when you step into new gigs right as AI goes from “this is cool” to “okay… but is it actually doing anything?” They get into: How AI is still, inconveniently, a people problem Why “AI is the new UI” is about to stop being a slogan Why most organisations don’t have an AI problem… they have a focus problem And yes… the AI reckoning that’s coming when everyone looks up and goes “oh no...what have we done?” A sneak peek at what’s coming at Knowledge (it’s big 👀) And Pete bravely preparing for a life without proper Australian coffee (thoughts and prayers)

What does dentistry have to do with data analytics? More than you'd think. Thais Cooke went from cleaning teeth to cleaning datasets and her unconventional path turned out to be a superpower. In this episode, Thais joins Juan and Tim to talk about why subject matter expertise is the ultimate force multiplier, how to build trust with stakeholders (hint: "I don't know" is a valid answer), and why the best data professionals aren't technical specialists — they're problem solvers. 

This is the takeaway episode where Thais Cooke joins Juan and Tim to talk about why subject matter expertise is the ultimate force multiplier. Oh, and what does dentistry have to do with data analytics? More than you'd think!

Your dashboards show you what's happening. Process mining shows you why — and what to do about it. In this episode, Bobby Brill sits down with Dan Grady to demystify process mining: what it is, how it works within the ServiceNow platform, and why organizations using it are uncovering years of hidden inefficiency they never knew existed. Dan walks through real customer stories — from a double approval process for printer toner that was costing 2 days per request to a single form-field change that saved 7 years of manual work per month — and explains how process mining moves you from raw data to actionable insight faster than any traditional approach. Dan Grady has a technology career that spans over 25 years. The last 10 spent at ServiceNow. In his current role as Director of Product Management, he is focused on helping customers identify process improvement opportunities through the use of ServiceNow's Process and Task Mining solution. Outside of the office, he enjoys spending time with his wife and 3 girls, public speaking, bad NY sports teams, a good happy hour deal, and storytelling. Whether you're new to ServiceNow or a seasoned admin, this episode will change how you think about your workflows. 🔗 Get started with Process Mining: https://www.servicenow.com/community/process-mining-article/servicenow-process-mining-formerly-process-optimization-faq/ta-p/2309083 📄 Learn more about ServiceNow: https://www.servicenow.com/docs/ 🎓 ServiceNow Docs YouTube Channel: https://www.youtube.com/channel/UCQjE37R-Y4DTq7kUWPO83Wg Chapters 0:00 – Introduction & Dan Grady's Background 1:13 – What Is Process Mining? (The X-Ray for Your Business) 2:08 – Why Dashboards Alone Aren't Enough 3:56 – From Raw Data to Actionable Insights 4:59 – The Printer Toner Story: Removing a Step, Saving Two Days 6:01 – The Spaghetti Bowl: Visualizing Your Processes 7:34 – The "47 Years" Moment That Shocks Every Customer 8:43 – Finding Automation Opportunities with Process Mining 10:49 – Best Practices: How to Avoid Getting Overwhelmed 12:35 – Getting Started: First Steps on the Platform 15:11 – Real Customer Use Cases (Approvals, Routing & More) 17:59 – Why You Never Stop Needing Process Mining 20:50 – The Analytics Stack: Operational Reporting → Platform Analytics → Process Mining 22:45 – Closing & Where to Learn More

Want to get the honest no bs scoop of what happened at the Gartner Data & Analytics in Orlando, then this short and sweet episode is for you. This was an impromptu recording where Sanjeev Mohan and Ryan Dolley join Juan and Tim to share what they learned. 

Juan and Tim rant about whats on their mind going into Gartner Data and Analytics conference: "Context" is going to be the word of the week, The Execution Gap, Decision intelligence might be the bridge and the thing nobody's talking about yet: context lock-in. 

The Human in the Loop | Ethical AI with Di Le  ServicveNow Insights Podcast - hosted By Bobby Brill What does it actually mean to build AI responsibly? Not the buzzword version. The real version. In our latest episode, I sat down with Di Le — AI Ethicist and Human-Centered AI Strategist at ServiceNow — and she broke it down in a way I hadn't heard before. Most people use Ethical AI, Responsible AI, and Human-Centered AI interchangeably, and Di breaks down exactly where each one lives and how they apply to building AI that aligns with our societal values. Fairness. Transparency. Bias. Beyond evaluation and technical talking points, these are also design decisions with real consequences for real people — and operationalizing them is harder than most organizations want to admit. One line from Di that stopped me: "People have crossed oceans and built monuments in honor of our capability to think. And I just want people to preserve that and not surrender it so freely." That's the episode in one sentence. To learn more about Ethical AI and reseatch from Di Le and more - https://aisel.aisnet.org/cgi/viewcontent.cgi?article=1020&context=sighci2025 https://aisel.aisnet.org/cgi/viewcontent.cgi?article=1025&context=sighci2024  https://www.youtube.com/watch?v=QhVY-85A-Wk&t=5s ServiceNow Insights Podcast 🎧 The Human in the Loop — now available wherever you listen to podcasts. 0:00 – Introduction & Di Le's Background 1:35 – Is Ethical AI Just About Preventing Skynet? 2:22 – Responsible AI vs. Ethical AI vs. Human-Centered AI 6:34 – Why All Three Are Deeply Interconnected 8:39 – When "Amazing" Tech Goes Wrong: The Adobe Example 10:07 – Fairness, Transparency & Accountability Explained 12:06 – The Mug Factory: Understanding Bias in AI 14:52 – Transparency & Interpretability in AI Design 16:48 – Keeping Humans Genuinely in the Loop 18:06 – Accountability & Human Oversight 20:39 – Why Bias Isn't a Bug You Can Just Fix 22:30 – How ServiceNow Approaches Responsible AI 23:11 – Why Di Chose ServiceNow (The Origin Story) 27:09 – One Thing Everyone Should Do With AI 28:38 – Closing

Most data and AI initiatives don't fail on technology — they fail on meaning. In this episode, Juan and Tim chat with Säde Haveri, who makes the case that metadata is the bridge between systems and people: the language and structures that help humans understand, trust, and use data in their daily work. Her focus isn't on building smarter pipelines, but on turning complexity into shared understanding so that data actually drives decisions and behavior change. Because if your data doesn't speak your language, it simply won't work for you.

This is the takeaway episode with Säde Haveri where she makes the case that metadata is the bridge between systems and people. Most data and AI initiatives don't fail on technology — they fail due to the lack of meaning. If your data doesn't speak your language, it simply won't work for you.

AI is transforming how businesses operate and how early-career talent grows. In this episode, UTG Unlocked, Mark Stockford (GVP, Global Cloud Operations) and Alyssa Gerhart (former intern, now full-time employee) share how AI is reshaping work at ServiceNow—from strategic impact to day-to-day execution. Our guest hosts Jorden Shelton and Cynthia Mathenge guide the conversation and explore real AI use cases like Unity, RAG-based duplicate detection, and intent detection, while emphasizing the importance of critical thinking and strong fundamentals.  In this episode, designed not just for recent interns, you’ll learn how AI is expanding career paths, how teams like Global Cloud Services power innovation behind the scenes, and what interns and early-career professionals can do now to grow: stay curious, use AI intentionally, seek mentors, and don’t just consume—contribute.  UTG is the engine behind the scenes here at ServiceNow — enabling innovation, maintaining production environments, supporting internal teams, and driving operational excellence. It connects strategy to execution by combining engineering, cloud operations, and technology operations to deliver stable, high-performing systems that allow the business and customers to succeed.  For more information about the Early Careers program visit - https://careers.servicenow.com/early-careers/  00:00 Welcome & What ‘UTG Unlocked’ Is All About  02:50 Meet the Panel: Mark, Alyssa, Jorden & Cynthia  04:29 Segment 1: How AI Is Impacting the Business (Customers vs. Employees)  06:26 Skills That Matter in an AI-Powered Workplace  09:52 Real AI Use Cases: Unity, Agents, and Faster Ops  13:57 AI and Career Growth: New Roles, New Paths, Partnering with AI  18:56 Advice for Early-Career Talent: Stay Curious, Build, Contribute  20:41 Segment 2 Kickoff: Rapid-Fire Fun 28:50 Pulling Back the Curtain: What is GCS  30:37 GCS as a Superhero: Operating in the Shadows Like Batman   31:35 The Hidden Work: Solving Customer-Created Problems & Root-Cause Hunting  33:37 Alyssa’s Journey: Intern to FTE, Mentorship, and Scaling Developer Productivity  35:38 What’s Next: Emerging Tech on the Radar (AI to Quantum Computing)  37:15 Closing Takeaways: Keep Learning, Use AI Wisely, Ask Questions, and Give Back  40:12 Final Words & Where to Learn More 

Juan and Tim are back for a LIVE episode with Nachiket Mehta, an experienced data leader who has lived and breathed the “shift left”. In this episode we unpack how ontology, events and culture unlock enterprise AI.  We discuss the gap between what your systems think is happening and what's actually happening on the ground and dive into real world examples: a trailer with expensive merchandise sat forgotten in a yard for weeks. $35M in delayed orders. The math added up, but nobody saw it coming. Why? Because we're obsessed with cleaning data and building dashboards, but nobody mapped the happy path vs. the exceptions actually happening on the ground.  What is delivery when the customer isn't home? What's "lost in transit" versus "sitting in our own yard"? The solution? Send your ontologist to the fulfillment center. Build tiger teams. Shift your data teams left to act like software product teams. And most importantly: connect the five why's back to your OKRs, or you're just building features nobody needs.

This is the takeaway episode with Nachiket Mehta, an experienced data leader who has lived and breathed the “shift left”. In this episode we will unpack how ontology, events and culture unlock enterprise AI. We discuss why your ontologist needs to visit the fulfillment center, how to shift data teams from afterthought to proactive partner, and why the five why's matter more than your tech stack.

Tim and Juan unpack why Vaibhav Gupta, co-creator of BAML, states that most agentic AI code out there is dumb. It's ugly, fragile, and built by people who've never had to wrestle with probabilistic systems before. But here's the thing: we've seen this movie before. Remember when building websites was painful? Then jQuery showed up... then React... then Tailwind. Abstraction always wins. It just takes time (and about 50 bad frameworks for every good one). The gaming industry also figured out about unreliable systems decades ago. The takeaway is that the real bottleneck isn't abstracting the AI, instead it's abstracting the failure. Oh, and this episode has a live demo (first time we ever do this!). 

This is the takeaway episode with Vaibhav Gupta, co-creator of BAML, where we unpack why most agentic AI code out there is dumb. If you like what you hear, you should listen to the full episode. 

Juan and Tim grab a beer and rant about decision intelligence and context graphs, MCP vs Skills, and how companies really have a work problem (not a data/AI problem) and what is the maturity model to get that work done

Tired of executives breathlessly asking "what's our AI strategy?" while your data team drowns in 1,000 dashboards that nobody uses? In this episode, Juan and Tim cut through the noise with Juan Gorricho a data and analytics executive with more than 25 years of experience delivering business value at TD Bank, Visa, The Walt Disney Company We cover: Why do data leaders keep building vanity metrics instead of business value? How do you go from being an "order-taking reporting team" to a trusted decision intelligence partner? And why does every data governance initiative feel like building policies nobody wants to follow? The secret? Do it by slice. Not top-down. Not bottom-up. By slice—connecting each data product to real business outcomes while building reusable foundations underneath. If you're ready to turn your data team's NPS around and build trust that actually sticks, this one's for you. Spoiler: It starts with being brave enough to tell executives the truth. And btw, "I bet 40 columns drive 90% of your business."

This is the takeaway episode of our chat with Juan Gorricho, a data and analytics executive with more than 25 years of experience delivering business value at TD Bank, Visa, The Walt Disney Company. We cover: Why do data leaders keep building vanity metrics instead of business value? How do you go from being an "order-taking reporting team" to a trusted decision intelligence partner? And why does every data governance initiative feel like building policies nobody wants to follow? And so much more. If you liked these takeaways, listen to the full episode!

Juan and Tim got together for a beer to rant about what's on their mind, the latest in the data world and share Data Day Texas takeaways

Dr. Irina Steenbeek is a data practitioner, and author of multiple books including Data Lineage from a Business Perspective. In this episode, Juan and Tim discuss with Irina the practical realities of implementing data lineage and its relationship to Data and AI governance. For example, do you really need to have a comprehensive technical lineage? A lot of honest no-bs nuggets in this episode!

This is the takeaway episode with Dr. Irina Steenbeek, a data practitioner, and author of multiple books including Data Lineage from a Business Perspective. In this episode, Juan and Tim discuss with Irina the practical realities of implementing data lineage and its relationship to Data and AI governance. 

A conversation with Jody Elliott, Head of IT Risk and Sustainability at National Grid, on embedding responsible AI and sustainability at scale.  In this episode of the ServiceNow Executive Circle Podcast, Jody Elliott, Head of IT Risk and Sustainability at National Grid, explores how sustainability, risk, and AI can work together to create real business value. Operating across critical national infrastructure in the UK and US, Jody shares why “good green operations are just good operations” and why sustainability and responsible AI must be embedded by design, not treated as standalone initiatives.  The conversation covers:  How IT sustainability, e-waste policy, emissions reduction, and regulatory compliance can drive efficiency, trust, and innovation  Practical, real-world AI use cases, including how generative AI can improve compliance and risk oversight by analysing large volumes of unstructured data  The importance of AI literacy, human-in-the-loop governance, and strong control frameworks  How organisations should prepare for emerging regulation, including the EU AI Act and CSRD  What’s next—from rising energy demand and supply chain risk to the next wave of responsible technology  Tune in now for a practical, real-world look at how responsible AI and sustainability can drive efficiency, trust, and innovation at scale.  If you’ve got an idea for a topic, would like to propose a guest for the show or discuss any of the points raised in this episode with a ServiceNow representative, just send an email to executivecircleuki@servicenow.com   And if you are not already an EXECUTIVE CIRCLE member and would like to learn more about our exclusive membership and all the benefits it brings, please visit.  

AI, Skills, and the Future of Work at Aviva  In this episode of the ServiceNow Executive Circle podcast, Kat Finch is joined by Dan Godfrey, Group People Transformation and Talent Director at Aviva, to explore how people strategy, technology, and AI are coming together to reshape the future of work. Dan shares how Aviva’s scale, legacy, and long history influence its approach to transformation, and why operational readiness, adoption, and change management are just as critical as the technology itself. From learning and recruitment to people technology and data analytics, the conversation highlights how HR and IT must work in lockstep to deliver meaningful outcomes for both colleagues and customers.  The discussion also dives into the evolving role of AI in skills development, workforce planning, and customer experience. Some key topics include:  The evolving role of AI in skills development, workforce planning, and customer experience  Highlighting how Aviva is using AI to surface skills and support “squiggly careers”  Showing how AI helps colleagues navigate future career paths and learning opportunities  Emphasising the importance of human-in-the-loop governance, trust, and responsible use of AI  Looking ahead to how AI could democratise financial services by making financial knowledge more accessible  Reinforcing why investing in peoples capability remains the most critical transformation for organisations  If you’ve got an idea for a topic, would like to propose a guest for the show or discuss any of the points raised in this episode with a ServiceNow representative, just send an email to executivecircleuki@servicenow.com   And if you are not already an EXECUTIVE CIRCLE member and would like to learn more about our exclusive membership and all the benefits it brings, please visit.  

Ontologies are suddenly everywhere but they didn’t come out of nowhere. In this episode, Juan and Tim chat with Professor Oscar Corcho, who has been doing ontology engineering since the early 2000s (check out his book Ontological Engineering from 2003), to demystify what ontologies actually are, where they came from, and why that history still matters today. If you’re new to ontologies or feeling lost in the current hype, this episode gives you the grounding you didn’t know you needed to understand where the field has been and where it should be going next.

This is the takeaway episode of our conversation with Professor Oscar Corcho, who has been doing ontology engineering since the early 2000s where we demystify what ontologies actually are, where they came from, and why that history still matters today. 

Juan and Tim rant about Context Graphs and categorizations of how companies work with data.

Tim and Juan chat with Tony Baer and Matt Housley, hosts of the “It’s About Data” podcast about what are the trends they are seeing with the start of 2026. We talked about AI magical thinking, Agentic architectures, Graphs, careers and much more.