Data Storytelling 101: Build an Exec Readout with AI

Part of Build Your AI Product Analyst

Hosted by Shane Butler, Sravya Madipalli, and Hai Guan

Wed, Sep 16, 2026

7:00 PM UTC (30 minutes)

Virtual (Zoom)

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Automate AI Evals with Claude Code
Shane Butler
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What you'll learn

Lead with the insight, not the chart dump

Open with the one finding that matters, and cut the slides that only prove you did the work.

Structure a problem, insight, ask

Shape any analysis into a 3-slide arc: the problem, the insight, and the decision you want.

Have an opinion and back it

Make a clear recommendation and let AI pull the evidence that supports it, so you are not just presenting numbers.

Anticipate the pushback

Draft the questions your exec will ask, and have the answer ready before they raise it.

Why this topic matters

Great analysis gets ignored when the story is buried. Getting the answer stopped being the hard part once AI could crunch the data. The hard part is landing it: turning a pile of charts into a readout that makes a busy exec decide. This session is the workflow: lead with the insight, structure the problem, insight, and ask, take a position, and anticipate the pushback before it lands.

You'll learn from

Shane Butler

Co-founder, AI Analyst Lab

Shane Butler is a Co-Founder of the AI Analyst Lab. Previously he led evaluation strategy for AI product development in the legal tech domain. He has more than ten years of experience in product data science and causal inference, with prior roles at Stripe, Nextdoor, and PwC. Shane is also the co-host of the AI podcast Data Neighbor, where he interviews product, data, and engineering leaders who are pioneering the next generation of data science and analytics in an AI-driven landscape.

Sravya Madipalli

Senior DS Leader (Ex-Microsoft)

Sravya Madipalli is a Senior Manager of Data Science with 14+ years of experience helping teams make better decisions with data. She has built and led data science and product analytics teams at Microsoft, eBay, Nextdoor, and Superhuman (prev. Grammarly), working closely with product, engineering, marketing, and leadership. Her expertise spans experimentation, metrics design, modeling, analytics, and translating complex user behavior into clear, actionable insights.

Hai Guan

Head of Data at Ontra, Ex-LinkedIn

Hai Guan leads the data organization at Ontra, the leading legal tech AI solutions for private markets. He previously led Data Science & Analytics at LinkedIn, Nextdoor, Pinterest, and Meta. He's spent a decade teaching product development teams how to ask questions that actually drive decisions—and now teaches how to combine that judgment with AI to move 10x faster.

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