Most engineering organizations have rolled out AI assistance, but at the executive level, the initial magic has worn off. CTOs and VPs find themselves caught between two extremes: developers quietly using unvetted tools in data-siloed sandboxes, and senior engineers suffering from pull-request review burnout due to a surge in AI-generated syntax. The bottleneck has shifted from how fast we can write code to how we plan, govern, and verify it across teams.
This roundtable is a pragmatic session for engineering leaders focusing on the entry point of structural AI adoption. We will bypass the theoretical future to discuss what is genuinely delivering in the day job, what organizational guidelines actually protect the codebase, and how to successfully manage engineer reluctance.

Key Takeaways:
- The Adoption Framework: Tactical ideas to move squads from shallow autocomplete extensions to structured pipelines without creating manual copy-paste friction.
- The Governance Blueprint: How to establish clear guardrails for AI tools regarding code ownership, data privacy, and architectural alignment.
- The Talent Calibration: How the changing roles of junior and senior engineers affect team structures, upskilling strategies, and hiring over the next 12 to 18 months.