📢 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