ServiceNow Podcasts

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 See omnystudio.com/listener for privacy information.

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. ” See omnystudio.com/listener for privacy information.

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 See omnystudio.com/listener for privacy information.

This is the takeaway episode with Bethany Sehon, Enterprise Data Leader at Capital One where she share lessons learned of building data products and ontologies in production long before they became buzzwords: dedicated data product managers (not side-of-desk), ontologists treated as first-class citizens, a VP-accountable governance process, and much more. See omnystudio.com/listener for privacy information.

Why multilingual content is a business risk, a compliance question, and an AI-readiness problem - not just a translation checkbox - with ServiceNow's Lyena Solomon. #ServiceNow #Localization #Globalization #AI Chapters 00:00 Cold open: "half ten" and the meaning problem 00:29 Welcome + episode topic 00:53 Meet Lyena Solomon 01:12 Why this isn't just translation 03:49 The word "order" - context matters 05:05 What is language governance? 06:04 Translating "pizza" 07:03 The regulatory reality (Quebec, EU AI Act) 09:30 Self-localization: Maori and Inuktitut 13:23 The real business risk of inconsistency 15:43 AI readiness and language risk 16:28 A support ticket in three languages 20:32 It's about trust, not just translation 21:11 Closing thought: the joy of understanding 22:17 Wrap-up + subscribe For more about ServiceNow - https://www.youtube.com/@ServiceNowDocs To watch these episodes on YouTube - https://www.youtube.com/watch?v=yxoHmZj5gOk&list=PLCOmiTb5WX3qvGq7Cp3o2KkCiplJyqQOK See omnystudio.com/listener for privacy information.

This is the start of our 7th year of doing the podcast. Juan and Tim kick off the 12th season sharing who are the upcoming guests and what's the latest on their mind about this crazy data and AI world: The new website ( honestnobsdata.com ) and discussed building a knowledge graph of the podcast itself Two types of semantics: for AI context vs. for system interoperability The vendor hype gap vs. practitioner reality AI agent proliferation and the governance surface area explosion AI cybersecurity: models escaping their sandboxes The economics of AI: accuracy, latency, and token cost as the new CAP theorem The great convergence, "triangle of consolidation" and “postmodern data stack” OSI and Apache Nessie Work as the center of gravity Bridging the operational and analytics worlds See omnystudio.com/listener for privacy information.

Voice AI sounds simple until you try to deploy it at scale — across airports, accents, languages, and thousands of employees at once. In this episode of ServiceNow Insights, host Bobby Brill sits down with Midam Kim, an ML engineer and linguist at ServiceNow, to unpack what it actually takes to build voice AI as an enterprise product. From the out-of-vocabulary problem (why AI still struggles with names) to why turn-taking in conversation is a linguistic skill most people never think about, Midam breaks down the human science behind the technology. In this episode: - Why voice AI is replacing typing as the default way to interact with enterprise systems - The difference between building for employees (B2B) vs. building for their customers (B2B2C) - Why an airport is one of the hardest possible environments for voice AI — and what ServiceNow does about it - The "out-of-vocabulary" problem: why AI still struggles with names, accents, and rare expressions - Why ServiceNow's secret sauce is hiring linguists, not just engineers - The linguistic framework behind every voice interaction: sounds, words, and turn-taking - Why voice AI is like teaching a kid to speak for the first time Chapters 00:00 — Welcome to ServiceNow Insights 00:22 — Meet Midam Kim, ML Engineer & Linguist 00:35 — Why voice is replacing typing 01:48 — What voice AI actually does for employees 03:40 — B2B vs. B2B2C: who's really using this? 05:11 — Desk employee vs. airport traveler: two different problems 06:42 — Building for an ever-changing environment 08:53 — Why airports are the hardest use case 09:54 — Accents, fluency, and the diversity problem 11:20 — "My Name Is. My Name Is. My Name Is." — the OOV problem 12:50 — The coffee shop name story 13:20 — How ServiceNow trains its models 14:59 — The 3 linguistic layers: sounds, words, interaction 16:38 — Midam's turn-taking story from Korea 18:33 — Why voice agents can't be "that person you avoid" 20:16 — "We can make it great" Subscribe for more ServiceNow Insights episodes on AI, voice technology, and enterprise innovation. Related episode: Voice AI Agent Evaluation — how ServiceNow measures whether voice AI meets human expectations. https://youtu.be/x7Ks932T18o For more about voice in AI from Midam Kim - https://youtu.be/3NUf6W_FMWs?is=wGc7BfyhiDp8JlOW #VoiceAI #EnterpriseAI #ServiceNow #ArtificialIntelligence #Linguistics #ConversationalAI #AIProduct #Podcast See omnystudio.com/listener for privacy information.

Most organizations deploying AI agents can’t answer a basic question: is it actually working? Not whether the agent runs — whether the process actually got better. In Episode 3 of our process mining and process intelligence series, Damian Pascale and Roz Parpia join host Bobby Brill to go deep on what it actually looks like to run process intelligence with AI in the mix — from the AI Visibility Gap, to a four-step framework for finding the right AI use cases, to what “closed loop intelligence” really means once agents are governing agents. This episode’s answer to the recurring question: don’t automate the chaos. Find it, understand it, improve it — then, and only then, streamline it. CHAPTERS 0:00 Introduction — Damian Pascale & Roz Parpia 0:56 “Don’t automate the chaos” — where the phrase comes from 2:25 Process mining vs. process intelligence — what actually changed 4:21 The linchpin: where AI fits across all three layers 5:36 The AI Visibility Gap — what most organizations are missing 6:56 A real example: when agent metrics look great, but quality doesn’t 8:13 The four-step framework: Find, Understand, Improve, Streamline 11:27 Where AI comes into streamlining — sizing the right use cases 13:19 Does the order of the four steps actually matter? 14:04 Task Mining — the human side process mining can’t see 15:19 A concrete example: the procurement approval bottleneck 16:31 The closed loop — six steps to continuous improvement 17:30 Why you can never skip the ‘detect’ step 18:05 Measuring real impact with the compare feature 19:25 Governance and AI Control Tower, explained simply 20:46 Mining the agents themselves — a third layer of visibility 21:36 Closed loop intelligence — the three layers, confirmed 22:48 Day one: what to do after deploying your first agent 23:38 Closing thoughts from both guests 24:26 Wrap-up IN THIS EPISODE • Why an AI agent doesn’t fix a broken process — it just runs the broken process faster • The real difference between process mining and process intelligence: three layers in one • The AI Visibility Gap: why almost every customer has deployed an agent, but few can prove it’s working • A real customer example — an agent that improved response time but quietly increased the reopen rate • The four-step framework for AI-ready process improvement: Find, Understand, Improve, Streamline • The 2–15 minute rule (and the 3–9 minute sweet spot) for sizing the right AI agent use cases • Why skipping straight to automation is exactly how you end up automating the chaos • Task Mining and the procurement approval example — 45 minutes across four systems, invisible to process mining alone • The six steps of the closed loop, and why the ‘detect’ step is the one everyone skips • Using the compare feature to measure whether an AI agent actually helped — or just moved the problem • AI Control Tower, explained simply — and how it becomes a third layer of process intelligence • Closed loop intelligence: the agent, the governance, and the agent’s own behavior — all observable, all improving GET STARTED If you’re a ServiceNow customer, you already have access to free evaluation projects — no license needed. https://www.servicenow.com/au/products/process-mining/get-started.html https://www.servicenow.com/docs/r/now-intelligence/process-mining/process-mining.html https://www.youtube.com/watch?v=TVrU0TQ7ldM https://www.youtube.com/watch?v=GLKROYqnc10 #ServiceNow #ProcessMining #ProcessIntelligence #AIAgents #AgenticAI #TaskMining #AIGovernance #AIControlTower #WorkflowAutomation #DigitalTransformation #ContinuousImprovement #ClosedLoopIntelligence #ServiceNowPodcast #EnterpriseAI #DontAutomateTheChaos See omnystudio.com/listener for privacy information.

Is process mining just Six Sigma with better software? Two former Lean Six Sigma consultants — now Product Managers at ServiceNow — answer that question. The answer is more interesting than you’d expect. Tomas Galle (Six Sigma Black Belt) and Roz Parpia (Green Belt) join host Bobby Brill to trace process intelligence from factory floors and sticky-note whiteboards to process maps generated in under ten minutes from data you already own. They cover the real cost of the old way, non-conformance, the ServiceNow Playbooks feature, Task Mining, and the question every AI agent deployment should be asking but usually isn’t: did the process actually get better? CHAPTERS 0:00 Introduction — Tomas Galle & Roz Parpia 2:02 Is process mining just Six Sigma? 4:13 The belt system explained — Black Belt, Green Belt, and the punchline 4:58 Manufacturing observation: what process improvement looked like before 7:51 The real cost of the old way — six figures, six months, one process 8:46 Customer reaction: ten years of work, solved in ten minutes 9:03 Where ServiceNow sits in the Process Intelligence market 10:56 Annual physical vs. wearable — continuous vs. snapshot 13:13 Conformance checking and the happy path 14:10 Non-conformance: what it is and why everyone should care 16:58 Static statistics vs. analysis on the move 17:05 Playbooks: responding to non-conformance in real time 18:54 How to get started today — free evaluation projects, no license needed 20:26 Task Mining: the human layer process mining can’t see 22:00 You’re already sitting on a goldmine 22:53 Closing thoughts IN THIS EPISODE • Why “that’s just Six Sigma” is actually the right reaction — and what it’s still missing • Frederick Taylor’s stopwatch, the Gemba walk, and how the factory floor became the IT service desk • Why a single process improvement engagement used to cost six figures and take up to six months • The Gartner Magic Quadrant for Process Intelligence — and Roz’s candid take on where ServiceNow really stands • The wearable vs. annual physical: why continuous process mining beats the yearly audit • Conformance checking and the happy path — what it means when your process deviates • Non-conformance explained with a real change management example (87% vs. 98% CAB approval) • How the ServiceNow Playbooks feature turns detection into real-time correction with one click • Task Mining: what people do in Outlook, Teams, and Excel that never appears in your process map • How to start mining your own data today — no license required, no IT admin needed GET STARTED If you’re a ServiceNow customer, you already have access to free evaluation projects — no license needed. https://www.servicenow.com/au/products/process-mining/get-started.html https://www.servicenow.com/docs/r/now-intelligence/process-mining/process-mining.html https://www.youtube.com/watch?v=TVrU0TQ7ldM https://www.youtube.com/watch?v=GLKROYqnc10 TAGS #ServiceNow #ProcessMining #ProcessIntelligence #SixSigma #LeanSixSigma #TaskMining #AIAgents #WorkflowAutomation #DigitalTransformation #ContinuousImprovement #NonConformance #Playbooks #ServiceNowPodcast #EnterpriseAI #ProcessImprovement #GembaWalk #ConformanceChecking See omnystudio.com/listener for privacy information.

Engineering teams are building ten times — even a hundred times — more than they could two years ago. That's a win, but one not without challenges. Because the cost of building the right thing has climbed exponentially. In this episode of the ServiceNow Insights podcast, host Bobby Brill sits down with three leaders who are living this tension from three distinct angles: the content and design leader who first spotted the productivity math problem, the design VP pushing for discernment over speed, and the research lead keeping the human at the center. ━━━━━━━━━━━━━━━━━━━━━━━━ IN THIS EPISODE ━━━━━━━━━━━━━━━━━━━━━━━━ DAVID HOARE — Group VP, Digital Content & Design, ServiceNow ANAND THARANATHAN — Group VP, Product Research & Insights, ServiceNow DANTLEY DAVIS — SVP of Design, ServiceNow ━━━━━━━━━━━━━━━━━━━━━━━━ CHAPTERS ━━━━━━━━━━━━━━━━━━━━━━━━ 0:00 Introduction & Guest Intros 1:13 David: The AI Philosophy — ChatGPT as genuine inflection point 3:02 David: Economic viability — why AI unlocks what was never possible before 3:12 Anand: Three-person startups scaling to $100M+ 3:45 Dantley: From 3D Studio Max to Jarvis — AI as human superpower 6:29 Anand: The customer north star hasn't changed 7:10 David: Engineering's survival problem — the 100x production gap 8:32 David: Andrew Ng's PM-to-engineer ratio + the cost of building wrong 9:40 Dantley: Nine concepts in an hour — design velocity and discernment 12:04 Dantley: The hip-hop tastemaker — slowing down as part of the process 14:20 David: Content governance — the fox guarding the hen house 16:21 Anand: Trust and the human-AI system 17:20 Dantley: AI surprise — UI tech stacks, feature completeness & hidden tech debt 20:21 18-Month Close — Anand, Dantley & David ━━━━━━━━━━━━━━━━━━━━━━━━ KEY TAKEAWAYS ━━━━━━━━━━━━━━━━━━━━━━━━ • Engineering is the first function to see massive AI productivity gains — but that creates a gap every other function has to survive • The cost of building has dropped. The cost of building the wrong thing has climbed exponentially • Discernment is the bottleneck — not speed. Nine concepts in an hour still needs a tastemaker • AI quality is only as good as the content signals it receives — governance is not optional • The customer north star hasn't changed. AI just changes how fast you can move toward it • Customer value is the only metric that matters. Everything else is the path to it ━━━━━━━━━━━━━━━━━━━━━━━━ ABOUT THIS PODCAST ━━━━━━━━━━━━━━━━━━━━━━━━ Subscribe for new episodes on AI, product, engineering, and the future of work. #ServiceNow #AI #ArtificialIntelligence #ProductDesign #SoftwareEngineering #ContentGovernance #DesignLeadership #AIStrategy #ProductManagement #EngineeringLeadership #TechLeadership #FutureOfWork #ServiceNowInsights #MachineLearning #Innovation #DesignThinking #TechPodcast #AIProductivity #DigitalTransformation #CustomerValue See omnystudio.com/listener for privacy information.

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. See omnystudio.com/listener for privacy information.

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 See omnystudio.com/listener for privacy information.

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. See omnystudio.com/listener for privacy information.

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. See omnystudio.com/listener for privacy information.

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. See omnystudio.com/listener for privacy information.

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. See omnystudio.com/listener for privacy information.

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. See omnystudio.com/listener for privacy information.

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. See omnystudio.com/listener for privacy information.

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. See omnystudio.com/listener for privacy information.

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. See omnystudio.com/listener for privacy information.