Super Data Show

AI governance is landing on data teams, whether anyone assigns it to them or not. Dia Adams, who did data work in the White House at the Office of Management and Budget and now chairs the DC think tank The AI Table, makes the case that regulation will never keep pace with AI, so data people have to be the ones who cut through the hype inside their companies.

The backdrop: AI regulation in the US is being written by the states, not Congress. Over 1,200 AI law changes in two years, and not one from Congress. We get into what data work at the White House actually looks like, what states are banning, and why NIST's AI Risk Management Framework matters even though it's voluntary.

Like this sort of thing? Join me every Thursday at 12:00 Eastern to catch my guest interviews live and get your questions answered on LinkedIn, YouTube and Substack.

LinkedIn: https://www.linkedin.com/in/ryandolley

YouTube: https://www.youtube.com/c/superdatabrothers

Substack: https://www.superdatablog.substack.com

And keep up with all my work, https://www.ryandolley.com

Can a BI tool built for AI do more than slap a chatbot on a dashboard?

I talk with François Ajenstat, former Tableau CPO and founder of Golden Analytics, about what the next generation of business intelligence should actually look like. We get into where chat falls short, what AI agents can take off an analyst’s plate, and why keeping humans involved still matters.

We also look back at what earlier generations of BI got right—and what’s worth carrying forward.

Super Data Show is live every Thursday. Follow me on LinkedIn to catch the next episode and bring your questions.

More from me: ryandolley.com

Disclosure: Golden Analytics is a client of my consultancy.

If AI builds the dashboards, what happens to the BI team? John K. Thompson joins Ryan Dolley to argue that the work shifts toward managing the data, business definitions, and context that make AI useful.

We discuss why John thinks GPT-6 Astra falls short of AGI, why faster emails don’t automatically translate into business results, and when to pull the plug on an AI project. Plus: how he gets his University of Michigan students to recognize the value of their own writing.