Data leaders are being asked to ship real AI outcomes while the foundations are still messy. In this conversation, Dave Shuman, Chief Data Officer at Precisely, breaks down what actually determines whether AI adoption sticks, from hiring “comb shaped” talent to building trusted data products that make AI outputs believable and usable.

If you are building in data, AI, or analytics, this episode is a practical map for what needs to be true before AI can move from demos to dependable, repeatable impact.

Key Takeaways

Comb shaped talent beats narrow specialization, AI work rewards people who can span multiple skills and collaborate well

Adoption is a trust problem, and trust starts with data integrity, lineage, context, and a semantic layer that business users can understand

Open source drives the innovation, commercialization makes it safe and usable at enterprise scale, especially around security and support

Data must be fit for purpose, start every AI project by asking what data it needs, who curates it, and what the known warts are

Humans are still the last mile, small workflow choices can make adoption jump, even when the model is already accurate

Timestamped Highlights

00:56 The shift from T shaped to comb shaped talent, what modern AI teams actually need to look like

05:36 Hiring for team fit over “world class” niche skills, and when to bring in trusted partners for depth

07:37 How open source sparks the ideas, and why enterprises still need hardened, supported versions to scale

11:31 Where AI adoption is today, why summarization is only the beginning, and what unlocks “AI 2.0”

13:39 The trust stack for AI, clean integrated data, lineage, context, catalog, semantic layer, then agents

19:26 A real adoption lesson from machine learning, and why the human experience decides if the system wins

A line worth stealing

“You do not just take generative AI and throw it at your chaos of data and expect it to make magic out of it.”

Pro Tips for data and AI leaders

Hire and build teams like Tetris, fill skill voids across the group instead of chasing one perfect profile

Use partners for the sharp edges, but require knowledge transfer so your team levels up every engagement

Make adoption easier by designing for human behavior, sometimes the smallest workflow tweak beats more accuracy

Build governed data products in a catalog, then validate AI outputs side by side with dashboards to earn trust fast

Call to Action

If this helped you think more clearly about AI adoption, talent, and data foundations, follow the show and turn on notifications so you do not miss the next episode. Also, share it with one data or engineering leader who is trying to get AI out of pilots and into real workflows.