Parse notes
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Product teams are drowning in AI complexity: building models, managing inference, and scaling production. None of the off-the-shelf solutions understand your unique business context, and piecing together different tools kills productivity.
Speak in money, not features. Rough revenue guesstimates win executive attention and drive better product decisions.
AI accelerates product development, but without disciplined critical thinking, it risks enabling distraction over value. Prioritize fast learning, thoughtful experimentation, and outcome-focused decisions to ensure impact over noise.
Pricing grows with proof. Start small, validate your value, and raise rates as you earn credibility and demand.
Building effective AI products requires starting with real customer needs, testing with safe, relevant users, and embracing architecture beyond chat interfaces. Consistent quality and ethical data use require evals, trace analysis, and continuous iteration
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You're already collecting behavioral signals to measure product activity: completion times, hesitation, copy/paste usage, autofill... Can you use the same data to stop bad actors? This guide by Persona [9] includes a 50-signal checklist, case studies of weak signals detecting sophisticated attacks, and frameworks for building fraud
Evals help PMs validate cross-functional workflows by testing prioritized customer scenarios, not just individual components. This lightweight, repeatable process surfaces integration risks early and aligns teams around product readiness.
Scrappy feedback gathering fuels sharper insights by prioritizing proactive, hands-on customer engagement over passive data collection. Whether at a startup or large company, consistent, unfiltered input leads to faster learning, better decisions, and more resonant products.
Great leaders serve, adapt, and stay transparent so their teams can thrive.
AI breaks old business rules and stirs new moral dilemmas, yet companies keep racing ahead to commercialize it.
Discovery ends with informed conviction - confidence built on evidence - not a number of interviews.
Discovery, not features, drives real value.