ServiceNow Insights

#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.com

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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 translation
How a name that isn't in the training data breaks the first model in the chain
The three-model cascade behind every voice agent, and what gets lost at each handoff
A voice agent that hears a confirmation code correctly three times and rejects it three times
Why formality — tu or vous — is a fluency problem, not a grammar problem
Who 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/x7Ks932T18o

Guests:
Lyena Solomon, Director of Globalization and Accessibility
Midam Kim, Machine Learning Engineer
Tara Bogavelli, Research Engineer
Katrina Stankovic, Staff Machine Learning Engineer
Gabrielle 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 #AIGovernance

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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

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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
━━━━━━━━━━━━━━━━━━━━━━━━
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
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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
━━━━━━━━━━━━━━━━━━━━━━━━

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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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.

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Voice AI agent evaluation — why it's fundamentally harder than text, how cascade failures derail conversations invisibly, and ServiceNow's open-source framework to establish industry evaluation standards. Featuring real audio examples showing authentication failures, leaked reasoning, and latency problems.

WHAT WE COVER 

TARA BOGAVELLI — Research Engineer, ServiceNow
Leading the open-source voice agent evaluation framework. Explains why existing benchmarks don't measure what matters and what ServiceNow is releasing to establish industry standards.

KATRINA STANKIEWICZ — Staff Machine Learning Engineer, ServiceNow
Cascade model architecture expert. Breaks down STT → LLM → TTS failure modes, named entity transcription challenges, and real audio example analysis.

GABRIELLE GAUTHIER MELANÇON — Staff Applied Research Scientist, ServiceNow
Multi-language evaluation specialist. Reveals why Large Audio Language Models lag behind, the native speaker requirement, and bot-to-bot simulation methodology. 

CHAPTERS
0:00 Introduction — The evaluation gap
1:11 ServiceNow's Open-Source Framework Announcement — Tara Bogavelli
2:43 Meet the Researchers
3:43 Voice-Specific Challenges — Tara Bogavelli
5:03 Cascade Architecture: STT → LLM → TTS — Katrina Stankiewicz
7:57 The Named Entity Problem — Katrina Stankiewicz
10:06 Evaluation Metrics: Accuracy vs Experience — Gabrielle Gauthier Melançon
11:23 Bot-to-Bot Testing at Scale — Gabrielle Gauthier Melançon 
14:30 The LALM Gap: Why Audio AI Judges Struggle — Tara Bogavelli
16:57 Real Audio Example: Flight Rebooking Gone Wrong
21:58 Breaking Down the Failures — Katrina Stankiewicz 28:30 Wrap-Up & Resources

KEY INSIGHTS

The Cascade Failure Problem: STT → LLM → TTS errors propagate invisibly Named Entity Transcription: The #1 enterprise blocker—names, confirmation codes, emails break authentication Accuracy vs Experience: Perfect task completion means nothing if users hang up due to poor experience LALM Gap: Large Audio Language Models lag behind text LLMs—human evaluators remain essential Latency Kills Conversations: Five-second pauses make users think the call dropped, breaking the experience even when tasks complete Open-Source Framework: ServiceNow releasing evaluation tools, metrics, and bot-to-bot simulation methodology for the industry.

LEARN MORE

Website: https://servicenow.github.io/eva/ GitHub:
https://github.com/servicenow/eva Blog Post:
https://huggingface.co/blog/ServiceNow-AI/eva Dataset: https://huggingface.co/datasets/ServiceNow-AI/eva

ABOUT

Hosted by Bobby Brill. ServiceNow Insights podcast explores AI research, real-world applications, and the people building the future of work. #VoiceAI #AIEvaluation #ServiceNow #MachineLearning #OpenSource #ConversationalAI #STT #TTS #LLM #VoiceAgents #AIResearch #Podcast

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