A CTO hired a star engineer to push AI adoption forward, and within months, the team had lost its momentum, the new hire, and any sense of where they actually were.

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Almost every CTO Adam speaks to right now is carrying the same thing: exhaustion beneath loud pressure. The board wants speed. Investors want an AI strategy. The voice in their own head says everyone else has worked something out that they haven't.

The pressure to go faster with AI is usually the signal that the real work is somewhere else. Adam has watched organizations try to leap from pure experimentation to mature AI practice through a single hire, and watched what breaks when they do.

The fastest engineering teams aren't tuning the engine first. They're upgrading the brakes and tires, the testing automation, the review systems, and the people around the work, before they touch raw speed.

Tune in to learn about the three-stage pattern of AI adoption, two contrasting examples that show what acceleration actually costs, and the three postures that separate the CTOs making real progress from the ones spending money to look busy.

You’ll Learn:

[00:00] Introduction

[01:08] Why CTOs feel behind even when they’re moving fast

[02:48] The three stages of AI adoption and where most teams get stuck

[05:12] Why skipping stages creates friction that slows the whole team

[08:34] How lack of standards turns fast progress into team-wide frustration

[15:06] Why slowing down in the right way is what actually moves teams forward

[23:11] How to spot your team’s real stage and decide what to do next

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