About This Session
Most AI enablement programs in large engineering organizations fail the same way: leadership announces the tools, developers quietly resent the mandate, a few champions get loud on Slack, and six months later, nothing has changed except the line item on the IT budget. This session is an honest, field-tested account of what it took to do it differently — inside a $10 billion company's engineering organization, over a six-month engagement with measurable outcomes.
We'll walk through the five-phase rollout we ran end-to-end: a baseline AI readiness assessment, a structured Developer Fear Map workshop, a Leadership Expectations Alignment session, pattern-driven code assistance, CLAUDE.md authoring across active repositories, a special-focus pilot team, a scaled rollout informed by what the pilot taught us, and a sustainment cadence built around a monthly retrospective. You'll see the actual artifacts, the Fear Map facilitator guide, the CLAUDE.md template, the four prompting patterns we taught, and the productivity measurement framework that tracks adoption without surveilling developers.
Most importantly, you'll hear what didn't work, where leadership expectations had to be recalibrated mid-engagement. Where a metric we believed in turned out to erode trust, we changed it in Phase 4 because the pilot team taught us something we hadn't anticipated. Where "AI raises the floor, not the ceiling" stopped being a slogan and started being the actual ROI argument.
This workshop is built for engineering leaders making the investment case and the practitioners who have to make it work on Monday morning. Attendees leave with concrete artifacts, a clear-eyed view of the human and technical work involved, and a rollout sequence they can adapt to their own organization.