TAKEAWAY - AI Inherits the Business Choices You Never Made with David Krauza

#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.