ServiceNow Insights

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
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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
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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
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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.
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IN THIS EPISODE
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DAVID HOARE — Group VP, Digital Content & Design, ServiceNow
ANAND THARANATHAN — Group VP, Product Research & Insights, ServiceNow
DANTLEY DAVIS — SVP of Design, ServiceNow ━━━━━━━━━━━━━━━━━━━━━━━━
CHAPTERS
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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
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• 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
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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
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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.

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WHAT WE COVER
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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.

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LEARN MORE
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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

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ABOUT THIS PODCAST
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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
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