Most teams bolt AI onto how they already build. The leaders rebuild around it.

You're not alone. Most teams turn these tools on, get a burst of autocomplete or a flashy demo, and plateau. The tool isn't the lever, the workflow around it is. The builders session shows how to wrap AI in specs, agent configs, and review loops so it compounds week over week instead of stalling.

Both are partly right. AI tools are legitimately powerful for certain workflows and genuinely overhyped for others. The problem is that most teams haven't had a structured way to figure out which is which. Our leaders session gives you a measurement framework so you can evaluate AI investments with data instead of opinions.

That instinct is right, and it's why the leaders session treats governance as a first-class topic. You'll leave knowing where to put human checkpoints, which review gates matter, and what governance other engineering orgs have actually adopted. The hands-on security work, like spotting a compromised agent config before it runs, lives in the builders session.

Vendor dashboards measure usage, not impact, and lines of code accepted is not the same as engineering velocity. The leaders session gives you a measurement framework that ties AI adoption to the metrics your business actually cares about, like cycle time, defect rate, and throughput, so you can make renewal and expansion calls with real data.

Uneven adoption is the default, and it's expensive. Your fast adopters are building muscle memory the rest of the team could use, but there's no mechanism to share what's working, and your holdouts have real concerns nobody's addressing. Running both sessions closes the gap: leaders get the framework to set policy, builders get the hands-on skills, and everyone leaves with the shared vocabulary that makes AI decisions stick.