Many organizations are stuck in delivering impressive AI pilot demos in a few teams with no safe path to scale, and growing shadow AI. Others rush to broad rollout and wake up months later with fragmented standards and more incidents more frequently.

In this session, we'll walk through a phased rollout framework grounded in real failure modes and hard exit criteria. We'll cover how to stage adoption by repository criticality across codebases, "context eligibility" before higher-tier usage, and use champions and feedback loops to ensure compliant pathways are easier and more optimal than shadow AI workarounds. You'll walk away with checklists, stage gates, and anti-patterns you can apply to your own AI rollout plans.