Your organization adopted AI. Congratulations — so did everyone else. But here's what the data actually shows: in a pre-registered experiment with 758 consultants, Harvard and BCG found that AI made good work 40% better and bad work 19 percentage points *worse* than working without AI at all. The tool that amplifies expertise also amplifies poor judgment — and the boundary between the two is invisible without training.

This isn't a theoretical risk. In our own survey of 571 professionals, 93.9% use AI frequently — but 69.4% spend zero time on advanced capabilities. Microsoft's 300,000-person Copilot rollout found the same pattern: broad adoption clustered at the simplest features, with a measurable productivity dip from weeks 3 through 10 as initial excitement collided with real-world complexity.

Adoption isn't the hard part anymore. Judgment is.

In this session, you'll learn:
- How to identify whether a task falls inside or outside AI's reliability boundary — and why getting this wrong is catastrophic
- A practical verification framework that catches AI failures *before* they reach your clients or production systems
- Why the "adoption valley" kills most AI initiatives, and the specific practices that get teams through it
- How to build team-level habits that make AI output trustworthy by default, not by accident

You'll leave with concrete processes you can implement Monday morning to close the gap between "we use AI" and "we use AI well."

Opening / The Landscape (10 min): Survey data, industry benchmarks, the adoption-vs-depth gap
- Core / Verification & Judgment (30 min): The reliability boundary, practical verification framework, the adoption valley and how to survive it. Interactive — includes audience discussion.
- Closing / Building Trust in AI Output (20 min): Team-level habits, self-assessment, concrete next steps