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

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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.

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

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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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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.

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Most companies slapping the "data product" label on existing data assets are fooling themselves. Bethany Sehon, Enterprise Data Leader at Capital One, has been building data products and ontologies in production long before they became buzzwords. Bethany joins Juan and Tim to share lessons learned: dedicated data product managers (not side-of-desk), ontologists treated as first-class citizens, a VP-accountable governance process, and much more.

Topics discussed:

• Data products as a discipline, not a label
• Governance process as the forcing function
• Dedicated roles, not side-of-desk work
• Ontology as a strategic function
• Standardization is necessary but not sufficient
• Less is more on scope
• Trust is consumer-specific
• AI expands what counts as a data product

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.

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.