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
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 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
See omnystudio.com/listener for privacy information.

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

For more about ServiceNow - https://www.youtube.com/@ServiceNowDocs
To watch these episodes on YouTube - https://www.youtube.com/watch?v=yxoHmZj5gOk&list=PLCOmiTb5WX3qvGq7Cp3o2KkCiplJyqQOK

See omnystudio.com/listener for privacy information.

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

Most organizations deploying AI agents can’t answer a basic question: is it actually working? Not whether the agent runs — whether the process actually got better.
In Episode 3 of our process mining and process intelligence series, Damian Pascale and Roz Parpia join host Bobby Brill to go deep on what it actually looks like to run process intelligence with AI in the mix — from the AI Visibility Gap, to a four-step framework for finding the right AI use cases, to what “closed loop intelligence” really means once agents are governing agents.
This episode’s answer to the recurring question: don’t automate the chaos. Find it, understand it, improve it — then, and only then, streamline it.

CHAPTERS
0:00 Introduction — Damian Pascale & Roz Parpia
0:56 “Don’t automate the chaos” — where the phrase comes from
2:25 Process mining vs. process intelligence — what actually changed
4:21 The linchpin: where AI fits across all three layers
5:36 The AI Visibility Gap — what most organizations are missing
6:56 A real example: when agent metrics look great, but quality doesn’t
8:13 The four-step framework: Find, Understand, Improve, Streamline
11:27 Where AI comes into streamlining — sizing the right use cases
13:19 Does the order of the four steps actually matter?
14:04 Task Mining — the human side process mining can’t see
15:19 A concrete example: the procurement approval bottleneck
16:31 The closed loop — six steps to continuous improvement
17:30 Why you can never skip the ‘detect’ step
18:05 Measuring real impact with the compare feature
19:25 Governance and AI Control Tower, explained simply
20:46 Mining the agents themselves — a third layer of visibility
21:36 Closed loop intelligence — the three layers, confirmed
22:48 Day one: what to do after deploying your first agent
23:38 Closing thoughts from both guests
24:26 Wrap-up

IN THIS EPISODE
• Why an AI agent doesn’t fix a broken process — it just runs the broken process faster
• The real difference between process mining and process intelligence: three layers in one
• The AI Visibility Gap: why almost every customer has deployed an agent, but few can prove it’s working
• A real customer example — an agent that improved response time but quietly increased the reopen rate
• The four-step framework for AI-ready process improvement: Find, Understand, Improve, Streamline
• The 2–15 minute rule (and the 3–9 minute sweet spot) for sizing the right AI agent use cases
• Why skipping straight to automation is exactly how you end up automating the chaos
• Task Mining and the procurement approval example — 45 minutes across four systems, invisible to process mining alone
• The six steps of the closed loop, and why the ‘detect’ step is the one everyone skips
• Using the compare feature to measure whether an AI agent actually helped — or just moved the problem
• AI Control Tower, explained simply — and how it becomes a third layer of process intelligence
• Closed loop intelligence: the agent, the governance, and the agent’s own behavior — all observable, all improving

GET STARTED
If you’re a ServiceNow customer, you already have access to free evaluation projects — no license needed.
https://www.servicenow.com/au/products/process-mining/get-started.html
https://www.servicenow.com/docs/r/now-intelligence/process-mining/process-mining.html
https://www.youtube.com/watch?v=TVrU0TQ7ldM
https://www.youtube.com/watch?v=GLKROYqnc10

#ServiceNow #ProcessMining #ProcessIntelligence #AIAgents #AgenticAI #TaskMining #AIGovernance #AIControlTower #WorkflowAutomation #DigitalTransformation #ContinuousImprovement #ClosedLoopIntelligence #ServiceNowPodcast #EnterpriseAI #DontAutomateTheChaos
See omnystudio.com/listener for privacy information.