📢 Most AI initiatives stall not because of weak models, but because of weak execution.

In this episode of the Data Faces Podcast, David Sweenor sits down with Asa Whillock, CEO of Euphonic AI, to unpack what it really takes to operationalize AI inside the enterprise.

With experience spanning Adobe, Alteryx, and now a growth-focused AI startup, Asa explains why production AI depends less on model hype and more on data access, system alignment, and disciplined leadership. If you’re responsible for turning AI experiments into measurable business outcomes, this conversation will sharpen your thinking.

🔍 Key Takeaways:

1- Production AI is about context — not just model capability

2- Vertical enterprise systems create horizontal friction for AI

3- Metadata and human decision logic are often the missing layers

4- “Boring” infrastructure work determines long-term AI success

5- ROI comes from aligning AI to the metrics that actually drive your business

⏳ Timestamps for Easy Navigation:

00:00 – Welcome & episode overview

02:00 – Redefining operationalizing AI

04:15 – Why enterprise AI struggles across silos

08:30 – Signals that AI is ready for production

12:45 – Structured vs. unstructured data

15:00 – The decisions leaders delay

18:00 – Differentiation vs. distraction

25:15 – Models vs. data: what matters more

29:20 – Why infrastructure determines success

32:30 – Finding real ROI in AI

34:20 – Final advice for AI leaders

📩 More insights & resources:

👉 https://www.tinytechguides.com

🔗 Connect with Asa Whillock:

💼 LinkedIn: https://www.linkedin.com/in/asawhillock/

🌎 Website: https://www.euphonic-ai.com/

💬 What’s the biggest barrier to operationalizing AI in your organization? Share your perspective in the comments.

👍 If this was valuable, like the video and subscribe for more conversations with leaders shaping data and AI.

#OperationalizingAI #EnterpriseAI #AILeadership

📢 AI governance is moving faster than most companies can control—and that gap is where risk shows up.In this episode of the Data Faces Podcast, Gene Arnold, Partner Sales Engineer at Atlan, breaks down what AI governance actually looks like in real organizations—not policy decks or theory, but decisions, tradeoffs, and failures teams face every day.David Sweenor and Gene explore how AI governance differs from data governance, why most AI projects never reach production, and how metadata, accountability, and testing determine whether AI becomes an asset or a liability.This conversation is for leaders who want AI to scale without surprises.🔍 Key Takeaways:1- Why AI governance is not just an extension of data governance2- How biased outcomes emerge even when models “work as designed”3- The hidden risks of moving fast without ownership or traceability4- Why metadata and semantic context matter more than models5- A practical starting point for governing AI without slowing teams down⏳ Timestamps for Easy Navigation:00:00 – Podcast intro & Gene Arnold background02:10 – From data catalogs to AI governance07:05 – Data governance vs AI governance explained11:56 – The overlooked role of unstructured data16:31 – Why most AI projects fail in production19:18 – Real-world AI governance failures (Amazon, facial recognition)26:45 – How to detect and manage bias in AI systems27:02 – Practical advice for getting started with AI governance31:06 – Accountability, metadata, and the semantic layer36:10 – Final thoughts on adopting AI responsibly📩 More insights & resources:👉 Blog recap and show notes:https://tinytechguides.com/blog/why-the-biggest-ai-enthusiasts-care-most-about-governance/🔗 Connect with Gene Arnold:💼 LinkedIn: https://www.linkedin.com/in/genearnold/💬 What governance challenges are you seeing with AI in your organization? Share your perspective in the comments.👍 If this was useful, like the video, subscribe, and share it with someone leading AI or data initiatives.#AIGovernance #DataLeadership #EnterpriseAI

📢 Most companies invest heavily in data and AI—yet few see real business impact. Why?
In this episode of Data Faces, David Sweenor sits down with Randy Bean to unpack four decades of lessons from the front lines of data, analytics, and AI leadership.

Randy shares insights from his long-running Fortune 1000 benchmark surveys, explains why culture—not technology—remains the biggest blocker, and outlines what separates effective data leaders from those who struggle to deliver value.

This conversation is practical, candid, and aimed squarely at executives responsible for turning AI ambition into operational results.

🔍 Key Takeaways:
1- Why the Chief Data Officer role has expanded—but still struggles
2- How AI has reshaped executive attention on data foundations
3- The difference between defensive and offensive data leadership
4- Why culture and organizational readiness matter more than tools
5- What a value-first data and AI strategy actually looks like

⏳ Timestamps for Easy Navigation:
00:00 – Welcome to Data Faces & guest introduction
00:53 – Randy Bean’s career path into data and analytics
03:24 – The origin and impact of the Data & AI Leadership Survey
05:08 – What’s advanced—and what’s stalled—in CDO roles
10:51 – Why culture, not technology, blocks AI adoption
14:08 – GenAI adoption: hype vs. real progress
17:23 – AI’s renewed focus on data quality and foundations
21:43 – What separates effective data leaders from the rest
27:59 – Business vs. technical leadership in data roles
30:27 – What a value-first data strategy looks like
33:51 – Where to find Randy’s research and writing

📩 More insights & resources:
👉 Blog & episode recap: https://tinytechguides.com/blog/culture-eats-ai-for-breakfast/

🔗 Connect with Randy Bean:
💼 LinkedIn: https://www.linkedin.com/in/randybeannvp/
🌎 Website & research: https://randybeandata.com

💬 What resonated most with you from this conversation? Share your take in the comments.
👍 If this was useful, like the video, subscribe, and follow Data Faces for more leadership conversations.

#DataLeadership #AILeadership #ChiefDataOfficer

📢 AI is everywhere—but what’s real, what’s hype, and where is the business value actually coming from?In this episode of the Data Faces Podcast, David Sweenor sits down with Tom Davenport, Distinguished Professor at Babson College and one of the most trusted voices in analytics and AI. They unpack where AI is delivering durable value today, why generative AI may be overvalued, and what leaders should realistically expect as we move through 2025 and into 2026.This is a grounded conversation for executives and practitioners who want clarity—not speculation—on how AI is reshaping work, decision-making, and enterprise strategy.🔍 Key Takeaways:1- Why generative AI is overhyped—and where real value still exists2- What most organizations misunderstand about agentic AI today3- The shift from individual AI use to enterprise-level impact4- Why disciplined experimentation matters more than pilots5- How AI is quietly changing workflows, not just tools⏳ Timestamps for Easy Navigation:00:00 – Welcome & introduction to Tom Davenport02:10 – What’s real vs hype in AI today04:20 – Are we in an AI bubble?05:50 – Agentic AI: real use cases vs experimentation07:00 – Is generative AI analytics “on steroids”?08:55 – One underestimated AI shift coming by 202610:59 – Where enterprise AI value will show up first13:20 – Why generative AI requires new disciplines17:15 – Jobs, education, and the limits of AI predictions28:10 – Governance vs enablement in AI30:55 – The positive case: AI, workflows, and business change32:35 – Final thoughts📩 More insights & resources:👉 Blog & episode write-up: https://tinytechguides.com/blog/why-boring-ai-use-cases-will-win-in-2026/🔗 Connect with Tom Davenport:💼 LinkedIn: https://www.linkedin.com/in/davenporttom/💬 What’s your take—where do you see real AI value today? Drop your thoughts in the comments.👍 If this conversation was useful, like, share, and subscribe for more practical insights on AI, data, and analytics leadership.#AILeadership #Analytics #BusinessValue

📢 2025 was the year AI met the real world. No demos. No hype. Just results—and hard lessons.
In this special Data Faces year-in-review episode, we synthesize insights from 27 conversations with leaders across data, analytics, and AI to surface what actually mattered in enterprise adoption.

Rather than new models or bigger tools, the story of 2025 centered on strategy, operational maturity, agent management, and the human realities behind AI at scale. This episode distills a full year of dialogue into one clear narrative for data leaders who need signal, not noise.

🔍 Key Takeaways:
1- Why most GenAI projects failed—and it wasn’t the technology
2- How AI agents shifted from novelty to core infrastructure
3- What governance looks like when speed still matters
4- Where AI delivered value: narrow, unglamorous, high-impact work
5- Why culture, alignment, and ethics became the real constraints

⏳ Timestamps for Easy Navigation:
00:00 – Opening & scope of the 2025 review
01:10 – Strategy over technology: where projects broke down
03:05 – AI agents move from tools to infrastructure
04:35 – Real enterprise value in narrow workflows
05:19 – Culture, alignment, and human failure modes
06:09 – Ethics, fairness tradeoffs, and real-world consequences
07:58 – Adoption shifts and governance as a value driver
09:06 – Managing agents at scale
10:02 – Automation that makes people better, not obsolete
11:09 – What 2025 teaches us going forward

📩 More insights & resources:
👉 https://tinytechguides.com/data-faces-podcast/

🔗 Connect with Data Faces:
🌎 Website: https://tinytechguides.com/data-faces-podcast/
🎧 Subscribe on Spotify, Apple Podcasts, and YouTube

💬 What resonated most from 2025—strategy, agents, or people?
👍 If this was useful, like, share, and subscribe for future episodes.

#DataFaces #EnterpriseAI #DataLeadership

📢 Can open source, AI, and enterprise analytics really coexist? Absolutely—and Bruno Trimouille from Posit is here to explain how.  

How are open source tools reshaping enterprise data science? What role does AI play in bridging business and technical teams? In this episode, Posit CMO Bruno Trimouille breaks down how his team supports millions of users—while staying true to an open source mission.

🎯 Whether you’re a data leader, marketer, or innovator, you’ll learn practical approaches to balancing innovation with governance, productizing models into apps, and using AI for both technical and marketing acceleration.

🔍 Key Takeaways:

1- Why a code-first approach delivers trust, transparency, and reproducibility

2- How AI bridges the gap between business users and data scientists

3- What Posit’s B2B open source flywheel model looks like behind the scenes

4- Why governance doesn’t have to kill speed—in fact, it can enable scale

5- How marketing teams can harness Gen AI for content, segmentation & insights

⏳ Timestamps for Easy Navigation:

00:00 – Intro & guest welcome

00:52 – What is Posit? (formerly RStudio)

02:06 – Bruno’s journey: Engineer to CMO

04:53 – Open source, code-first, and the future of data science

07:32 – AI’s impact on productivity and risk in analytics

09:13 – Solving the governance vs. speed tension

11:29 – Using models & apps to make insights business-ready

13:13 – Building a business on open source: Posit’s flywheel

15:31 – Why organizations bet on open data tools

18:09 – Community-building as a growth engine

21:31 – How Posit marketing uses Gen AI every day

24:04 – AI for personalization, segmentation & ABM

27:51 – Multimedia & interactive learning with Gen AI

30:31 – Using AI for data insights & campaign analysis

33:14 – How AI is reshaping the marketing org chart

34:04 – Future of data-driven marketing leaders

35:49 – Final thoughts from Bruno

📩 More insights & resources:  

👉 https://tinytechguides.com/blog/category/data-faces-podcast/  

🔗 Connect with Bruno Trimouille:  

💼 LinkedIn: https://www.linkedin.com/in/brunotrimouille  

🌎 Website: https://posit.co

💬 What do you think? Drop your thoughts in the comments!  

👍 Enjoyed this video? Like, share & subscribe for more AI insights!

#OpenSourceAI #DataScienceLeadership #ResponsibleAI

📢 Most AI failures don’t come from the model—they come from the data feeding it.
In this episode of the Data Faces Podcast, Tina Chace, VP of Product Management at Solidatus, explains why incomplete lineage, missing context, and silent upstream changes quietly undermine AI systems long before anyone notices.

Tina shares lessons from deploying AI and machine learning in major banks, breaking down how column-level lineage and business context prevent cascading failures across systems, teams, and decisions.

🔍 Key Takeaways:
1- Why 90% of AI production issues trace back to data quality problems.
2- How technical and business lineage work together to build trust.
3- Why column-level tracking exposes the hidden transformations behind every metric.
4- How visibility without control increases anxiety across data teams.
5- Where organizations should start to get quick wins without “boiling the ocean.”

⏳ Timestamps for Easy Navigation:
00:00 – Intro: David Sweenor introduces Tina Chace
00:54 – Tina’s early career and the origins of her data skepticism
02:28 – The 90% data problem in AI and ML deployments
03:24 – What data lineage actually captures
06:47 – The rounding-error problem that compounds at scale
07:59 – Bridging the language gap across data, reporting, and business teams
09:46 – Who really owns data quality and lineage?
12:43 – Technical vs. business lineage, with real examples
16:24 – Managing complexity across systems, teams, and tech stacks
18:16 – Why documenting “everything” never works
23:28 – Data lineage in generative AI and RAG systems
30:57 – Why AI makes complete lineage non-negotiable
33:26 – The trust paradox: more visibility, more skepticism
34:27 – How to get started without boiling the ocean
35:35 – Closing remarks

📩 More insights & resources:
👉 Blog: https://tinytechguides.com/blog/data-lineage-for-ai-why-truth-beats-hope-in-banking/

🎧 Listen to the Data Faces Podcast:
YouTube: https://www.youtube.com/playlist?list=PLzrDACjTQ4OBoQ8qM1FMGBwYdxvw9BurR
Spotify: https://open.spotify.com/show/6SmGkQGvZQSAT1O7g1l2yF
Apple Podcasts: https://podcasts.apple.com/us/podcast/data-faces-podcast/id1789416487

🔗 Connect with Tina Chace:
LinkedIn: https://www.linkedin.com/in/tina-chace-rho-5433133b/
Solidatus: https://www.solidatus.com

💬 What’s the biggest data trust challenge in your organization? Tell us in the comments.
👍 Like, share, and subscribe for more conversations with leaders shaping AI, analytics, and data strategy.

#DataLineage #DataQuality #AITrust

📢 What happens when culture and technology collide—do they compete, or do they enhance one another?
In this episode of Data Faces, Gina von Esmarch, Founder and CEO of Adesso Associates, shares how cultural heritage, storytelling, and emerging technologies work together to shape stronger communities and brands.

🔍 Key Takeaways:1- How AI and cultural identity can evolve together, not apart.2- Why authenticity and values are the foundation of innovation.3- How diversity of thought drives long-term business success.

📩 Watch the full episode here: https://www.youtube.com/watch?v=F8-j7nqzsGk
👉 More insights: https://prompts.tinytechguides.com/s/the-data-faces-podcast
💬 How do you see culture influencing technology in your industry?
👍 Like, share & subscribe for more insights.
#DataFaces #AI #CultureAndTech #Leadership #Innovation

📢 What happens when AI becomes your sales coach? In this episode, we explore how artificial intelligence is reshaping how sales teams learn, ramp, and perform.Join David Sweenor, Founder of TinyTechGuides, as he talks with Matt Magne, Senior Enablement Manager of Revenue Enablement at LaunchDarkly, about the new frontier of AI-driven sales enablement — from AI role plays to digital coaching and the future of human + machine collaboration.🔍 Key Takeaways:-- Why AI is redefining how SEs and AEs practice and master sales conversations-- How LaunchDarkly integrates AI role plays to accelerate ramp time and confidence-- The balance between automation and authentic human coaching-- The data challenges still holding back innovation in enablement-- Why the future of enablement is about augmentation, not replacement⏳ Timestamps for Easy Navigation:00:00 – Welcome to Data Faces with David Sweenor01:00 – Meet Matt Magne and LaunchDarkly06:10 – What revenue enablement really means08:25 – How AI role plays improve ramp and coaching11:00 – Do reps prefer training with bots or humans?14:00 – Space repetition, practice, and performance17:10 – Building hybrid AI + human onboarding programs21:00 – Overcoming skepticism about AI training26:00 – The “skeptical buyer” prompt and realistic simulations27:15 – Human + machine collaboration: finding the right balance31:20 – Beyond scale: personalization and productivity with AI34:50 – The future of sales enablement: data, integration, and intelligence39:30 – Final takeaway: staying human in an AI-driven world📩 More insights & resources:👉 Read more at: https://open.substack.com/pub/davidsweenor/p/augmented-intelligence-the-future?r=1s6e48&utm_campaign=post&utm_medium=web&showWelcomeOnShare=true🔗 Connect with Matt Magne:💼 LinkedIn: https://www.linkedin.com/in/exrocker/🌎 LaunchDarkly: https://launchdarkly.com💬 What do you think — can AI make us better sales coaches? Drop your thoughts in the comments!👍 If you enjoyed this episode, like, share, and subscribe for more insights from Data Faces.#AIinSales #SalesEnablement #DataFacesPodcast

📢 What’s the real end game of AI? Is it about smarter machines—or smarter organizations?In this episode of Data Faces, Eric Kavanagh, AI analyst and syndicated radio host of DM Radio, joins David Sweenor, founder of TinyTechGuides, to explore what success in AI truly means for business, technology, and society.Eric has spent decades helping enterprises navigate the data-driven world. He argues that AI isn’t a monolith—it’s a tool that works best when humans stay in control, governance evolves, and organizations learn to use intelligence responsibly.🔍 Key Takeaways:1- Why the “end goal” of AI is human productivity, not machine autonomy2- How corporate hierarchies will evolve as AI becomes embedded in workflows3- Why transparency, audit logs, and explainability will define governance success4- The risks of “big model” complexity and why small, focused models may win5- Why AI will change how we work more than what we do⏳ Timestamps for Easy Navigation:00:00 – Welcome and introduction to Eric Kavanagh01:00 – How Eric built a career at the intersection of data and media05:20 – Defining the “end goal” of AI: business, life, and technology08:45 – Where humans remain central—and why that won’t change12:10 – Transparency, bias, and the challenge of giant AI models19:00 – Real-world AI agents: when automation works and when it doesn’t22:50 – The rise of small language models and the age of execution29:45 – Governance, audit logs, and minimal viable oversight33:00 – The future of jobs, creativity, and collaboration39:00 – Where to find Eric Kavanagh and DM Radio📩 More insights & resources:👉 Blog recap: https://tinytechguides.com/blog/why-80-of-ai-projects-fail-and-the-three-boring-decisions-that-save-the-other-20/🔗 Connect with Eric Kavanagh:💼 LinkedIn: https://www.linkedin.com/in/erickavanagh/🌎 Website: https://dmradio.biz💬 What do you think? What should the end game of AI be? Drop your thoughts below!👍 Enjoyed this conversation? Like, share & subscribe for more real-world AI and data insights.#AI #DataFacesPodcast #EricKavanagh