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.

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.