AI Product Leader ex Meta AI, Salesforce

You're expected to ship AI fast but every playbook out there was built for consumer apps with endless data, clean telemetry, and instant feedback. In B2B you have none of that. Your data is siloed, your customers are regulated and slow to adopt, your teams are skeptical, and leadership's expectations keep climbing. So how do you prove an AI feature actually works when you can barely measure it?
In two hours you'll leave with a tool-agnostic framework you can use. You'll pick AI bets that survive contact with reality, measure success and ROI when you have no instrumentation, ship into regulated and change-averse orgs, and keep your product defensible while everyone races to build.
This is built for B2B reality and taught by someone who built AI at Meta and Salesforce and funded B2B AI startups as a VC in a small cohort of B2B product managers who learn as much from each other as from the material.
You won't just learn about AI in B2B. You'll walk out knowing how to ship it.
Stop forcing consumer AI playbooks onto B2B. Leave able to pick, measure, ship, and defend AI products built for B2B's messy reality.
Map the four B2B traps siloed data, no telemetry, regulated buyers, skeptical teams against your own product.
Score features on value, feasibility, and reliability. Leave with two of your own bets ranked and ready to defend.
Define ROI, metrics and proxy metrics even when no data exists. Build a starter success metric for one live feature.
Design evals, audit trails, and feedback loops that hold up when every customer is a separate, silent silo.
Use staged rollouts, human-in-the-loop, and security-as-enablement to clear controls and win over skeptics.
Decide build vs. buy vs. partner and pinpoint your moat when everyone can build with agents.
Meet the room and name the four B2B traps: siloed data, no telemetry, regulated buyers, skeptical teams. Drop your #1 blocker in chat so the session targets what you face.
Apply a value × feasibility × reliability rubric to separate real candidates from AI-for-AI's-sake. Score two of your own features and rank them.
Define ROI, metrics and proxy metrics even when no data exists. Build a starter success metric for one live feature you can take straight back to your team.
Design evals, audit trails, and feedback loops that hold up when every customer is a separate silo. Walk a reusable template through a worked example.
Use staged rollouts, human-in-the-loop, and security-as-enablement to clear controls and skeptics. Case study on the exec-wants-it / team-resists tension.
Decide build vs. buy vs. partner and pinpoint your moat when everyone can build with agents. Pressure-test your differentiation through a VC's lens.
Share one bet you'll re-prioritize or one metric you'll add. Trade tactics with the room and leave with a single committed next step.

AI Founder | ex Venture Partner @ Storm | ex-Meta Responsible AI Product
B2B Product Manager just handed their first AI product, racing to ship and measure.
A B2B Product Manager building AI inside a regulated enterprise, stuck on siloed data, slow adoption, and skeptical teams.
A B2B product leader sharpening differentiation and defensibility as agents make building easy for everyone

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