Here is an interview question for you: tell us about a time you knew exactly how to do something, and then completely failed to explain it to three strangers in a conference room. If an example sprang to mind immediately, this episode is for you. It was prompted by a note from a listener who is confident in his ability to do the work and less confident in his ability to talk about it under pressure. So, we talked about it (under very little pressure). Topics covered: thinking out loud, saying "I haven't come across that, but I'd love to understand it" instead of "I don't know," having your behavioral examples ready before someone asks for them, building a sprawling document of everything you've ever done so an LLM can grill you on it, and asking questions at the end that you actually want answered. Consensus: it's good to demonstrate that you can use AI effectively. No consensus: whether or not you should bring an iced coffee with you. He did not ask about the iced coffee. We covered it anyway.

This episode is brought to you, in part, by our sponsors, Prism from Ask-Y and Stape.

For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page: https://analyticshour.io/2026/10/27/309-interviewing-in-a-dynamically-evolving-job-market/

How do you know whether AI is actually making your organization better? Counting prompts, sessions, and token spend is easy. Figuring out whether any of that activity is driving business outcomes is...harder. Daniela Matinho, Marketing Science Senior Strategist at LinkedIn, wrote a Medium post on that very question, and it helped shape this discussion about what AI is (and isn't) changing about the work of the analyst. As Daniela pointed out, if you tell a client you did something in two days instead of a week because of AI, the client doesn't care. They care about the result! From the smart-but-junior agent that needs constant supervision, to the analyst's shift toward being more of a storyteller, to the unglamorous-but-essential work of data quality and governance, we covered a lot of ground. Being faster is great. Staying influential is better.

This episode is brought to you, in part, by our sponsors, Stape and Prism from Ask-Y.

For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page: https://analyticshour.io/2026/10/13/308-ai-changes-everything-analytical-fundamentals-still-apply/

There's a version of this episode where AI replaces the data visualization expert entirely, and there's a version where it's useless without one. Neither is quite right, according to Cole Nussbaumer Knaflic of Storytelling with Data, who joined Tim, Moe, and Julie to sort out where AI genuinely earns its keep in data storytelling and where it's just producing a shinier first draft of the same shitty slide. Cole-admittedly a skeptic-turned-convert who once joked she'd retire before having to deal with any of this-walks through why the humans who benefit most from AI are the ones who already have the foundation to know when to ignore it, why "let me look at some options" beats "give me the final chart" as a prompt, and why the analog, pencil-and-paper parts of the process might be the most valuable friction in the whole workflow. Bring your own opinions on whether Claude should ever be handed "the rules" and told to just go.

This episode is brought to you, in part, by our sponsors, Prism from Ask-Y and Stape.

For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page: https://analyticshour.io/2026/09/29/307-ai-does-and-does-not-work-for-data-storytelling/