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👤 Guest Bio
James Evans leads AI initiatives at Amplitude after co-founding Command AI (acquired by Amplitude about a year ago). He’s focused on rethinking analytics for the AI era, turning large customer data sets and session replay into proactive, action-oriented insights.
At Amplitude, James’s team shipped Moda, a Slack-first analytics assistant that jumped usage 10× after moving from a standalone site into people’s workflows. He’s at the intersection of product, data, and AI adoption, building patterns that help teams go from dashboards to decisions.
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🎙Episode Intro
Most analytics tools today answer “what happened.” James argues that in the age of AI, tools must tell you what to do next. In this episode, we dig into Amplitude’s vision for “anomaly detection v2” (combining classical analytics with LLMs to surface patterns and recommended actions) and why session replay could someday replace brittle taxonomies (a potential Tesla vision moment for AI-powered analytics).
We also cover the hard parts of adoption: AI personalization trade-offs, integrating assistants into existing workflows, and the cultural changes teams must make to become AI-ready. If you build AI products, lead analytics, or run data teams, this conversation is a practical playbook for turning data into direction.
⏱ What’s Covered
(00:00) James’ introduction and the Command Bar acquisition
(09:27) How to build background agents that find signals and trigger fixes
(10:21) Prototype a single onboarding agent to spot drops and prove impact
(13:49) Why you need to apply LLM filters to cut alert noises
(15:06) How to treat session replay as your causal X-ray for true issues
(20:59) Lock down eval metrics before you scale generated UIs
(28:37) Pull instrumentation bugs from sessions and push actionable fixes
(29:43) Pilot session-first pipelines to avoid brittle taxonomies
(31:41) Pair conversational queries for discovery with a GUI for deep analysis
(36:31) Seed adoption by solving internal time-suck problems with copilots
(54:10) Quick tips on how to win at your AI project
💡 Key Takeaways
Peer pressure > training. The fastest adoption for AI tools happens when peers showcase wins, creating a little healthy FOMO.
Prototypes are the new PRDs. Readiness = showing working demos and not shipping documents.
Hack sprints beat hackathons. Iteration must be continuous, not annual.
AI readiness starts at the top. Touching code is negotiable, AI Fluency is not.
Distribution matters more than features. Tools like Moda took off when embedded in Slack, not as a standalone.
Consistency risk is real. When everyone codes, governance and design guardrails become critical.
Exercises you can run this week
Prototype Friday (90 min): Instead of a PRD, ask each PM to demo one working AI prototype for team feedback.
Hack Sprint (1–2 days): Pick a sticky workflow problem. Prototype → test → ship. Repeat weekly.
Peer Demo Session (45 min): Showcase 2–3 live AI workflows inside your org. Use FOMO to drive adoption.
Fluency Check (30 min): Have your exec team use one AI tool in their daily workflow for a week. Share what sticks and what fails.
📚 References & resources
Cursor — AI-native code editor enabling faster prototyping.
Excalidraw — Whiteboarding tool for fast, hand-drawn-style diagrams (great for prototyping ideas with teams)
🔗 Where to Find Tomasz
LinkedIn: James Evans
Amplitude: https://amplitude.com
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Until next time,
Haroon