ServiceNow Podcasts

#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.comSee omnystudio.com/listener for privacy information.

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 See omnystudio.com/listener for privacy information.

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. See omnystudio.com/listener for privacy information.

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.See omnystudio.com/listener for privacy information.

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.See omnystudio.com/listener for privacy information.

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 translationHow a name that isn't in the training data breaks the first model in the chainThe three-model cascade behind every voice agent, and what gets lost at each handoffA voice agent that hears a confirmation code correctly three times and rejects it three timesWhy formality — tu or vous — is a fluency problem, not a grammar problemWho 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/x7Ks932T18oGuests: Lyena Solomon, Director of Globalization and Accessibility Midam Kim, Machine Learning EngineerTara Bogavelli, Research EngineerKatrina Stankovic, Staff Machine Learning EngineerGabrielle 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 #AIGovernanceSee omnystudio.com/listener for privacy information.

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 See omnystudio.com/listener for privacy information.

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. See omnystudio.com/listener for privacy information.

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 problem00:29 Welcome + episode topic00:53 Meet Lyena Solomon01:12 Why this isn't just translation03:49 The word "order" - context matters05:05 What is language governance?06:04 Translating "pizza"07:03 The regulatory reality (Quebec, EU AI Act)09:30 Self-localization: Maori and Inuktitut13:23 The real business risk of inconsistency15:43 AI readiness and language risk16:28 A support ticket in three languages20:32 It's about trust, not just translation21:11 Closing thought: the joy of understanding22:17 Wrap-up + subscribeFor more about ServiceNow - https://www.youtube.com/@ServiceNowDocsTo 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 Chapters00:00 — Welcome to ServiceNow Insights00:22 — Meet Midam Kim, ML Engineer & Linguist00:35 — Why voice is replacing typing01:48 — What voice AI actually does for employees03:40 — B2B vs. B2B2C: who's really using this?05:11 — Desk employee vs. airport traveler: two different problems06:42 — Building for an ever-changing environment08:53 — Why airports are the hardest use case09:54 — Accents, fluency, and the diversity problem11:20 — "My Name Is. My Name Is. My Name Is." — the OOV problem12:50 — The coffee shop name story13:20 — How ServiceNow trains its models14:59 — The 3 linguistic layers: sounds, words, interaction16:38 — Midam's turn-taking story from Korea18:33 — 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 #PodcastSee 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 #DontAutomateTheChaosSee 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? CHAPTERS0:00 Introduction — Tomas Galle & Roz Parpia2:02 Is process mining just Six Sigma?4:13 The belt system explained — Black Belt, Green Belt, and the punchline4:58 Manufacturing observation: what process improvement looked like before7:51 The real cost of the old way — six figures, six months, one process8:46 Customer reaction: ten years of work, solved in ten minutes9:03 Where ServiceNow sits in the Process Intelligence market10:56 Annual physical vs. wearable — continuous vs. snapshot13:13 Conformance checking and the happy path14:10 Non-conformance: what it is and why everyone should care16:58 Static statistics vs. analysis on the move17:05 Playbooks: responding to non-conformance in real time18:54 How to get started today — free evaluation projects, no license needed20:26 Task Mining: the human layer process mining can’t see22:00 You’re already sitting on a goldmine22:53 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 STARTEDIf 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.htmlhttps://www.servicenow.com/docs/r/now-intelligence/process-mining/process-mining.htmlhttps://www.youtube.com/watch?v=TVrU0TQ7ldMhttps://www.youtube.com/watch?v=GLKROYqnc10 TAGS#ServiceNow #ProcessMining #ProcessIntelligence #SixSigma #LeanSixSigma #TaskMining #AIAgents #WorkflowAutomation #DigitalTransformation #ContinuousImprovement #NonConformance #Playbooks #ServiceNowPodcast #EnterpriseAI #ProcessImprovement #GembaWalk #ConformanceCheckingSee 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 #CustomerValueSee 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, ServiceNowThe 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, ServiceNowServiceNow 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, ServiceNowA 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, ServiceNowHow 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, ServiceNowThe 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-aiAI Model Accessibility Checker: https://www.servicenow.com/accessibility-statement.htmlServiceNow 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 #PodcastSee 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 15:41 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.

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.See omnystudio.com/listener for privacy information.

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. CHAPTERS0:00 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 Bogavelli16:57 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 #PodcastSee omnystudio.com/listener for privacy information.

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.See omnystudio.com/listener for privacy information.

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.See omnystudio.com/listener for privacy information.

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.See omnystudio.com/listener for privacy information.

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, ServiceNowWhy 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, ServiceNowThe 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, ServiceNowHow 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, ServiceNowThe 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, ServiceNowThe 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 Introduction1:23 The Hidden AI Problem — Ravi Krishnamurthy & Sampada Chavan5:33 AI Control Tower in Practice — Peter Weigt & Sampada Chavan7:37 The Regulatory Landscape — Andrea LaFountain & Navdeep Gill14:38 Compliance in Action & Key Deadlines17:05 Wrap-Up━━━━━━━━━━━━━━━━━━━━━━━━KEY DATES TO KNOW━━━━━━━━━━━━━━━━━━━━━━━━EU AI Act enforcement: August 2026Colorado AI Act enforcement: June 2026NIST AI RMF: Voluntary framework, increasingly referenced by regulators━━━━━━━━━━━━━━━━━━━━━━━━LEARN MORE━━━━━━━━━━━━━━━━━━━━━━━━ServiceNow AI Control Tower: https://www.servicenow.comNIST 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 #PodcastSee omnystudio.com/listener for privacy information.

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.See omnystudio.com/listener for privacy information.

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.See omnystudio.com/listener for privacy information.

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

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. See omnystudio.com/listener for privacy information.

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. See omnystudio.com/listener for privacy information.

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 Introduction1:30 What Is Agentic AI? — Rita Castillo, VP of AI Design6:00 How Agents Work as a Team10:30 Meet Agent Ada — Amrutha Ramesh, Visiting Researcher16:00 Can You Trust Your AI? — Issam Laradji & Anitha Raghavan21:30 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 #PodcastSee omnystudio.com/listener for privacy information.

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.See omnystudio.com/listener for privacy information.

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. See omnystudio.com/listener for privacy information.

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

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. See omnystudio.com/listener for privacy information.

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!See omnystudio.com/listener for privacy information.

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.