My guess: 20% engineering-driven, 80% spray-and-pray. But that 20% produces almost all the real value.

My experience (large enterprise) is that the whole workforce is being actively trained at the same time as rolling out technology, particularly since deploying Claude to the enterprise there has been a ramp up. Whether you're using GenAI for research, assisting with work of building apps and agents there is full supported training modules. There is a clear multi-step skills roadmap everyone is following with expectations per role/department.

"Plaster it everywhere! Talk about it all the time. Put it in the product, even where it does not make sense. We are being disruptive, after all... and use ALL the tokens -- that's how they call it, right? -- but not too many please, we're not made of money."

(I guess the whole staff uses it on their job everyday, which can be inferred though the communication style changes in emails and team messages...)

Thankfully the people in charge of strategy are not technical, so if we just wear a fleur de lis we can keep things in working order with minimal interruption.

We're a startup, and every role uses AI for many tasks, at times quite efficiently. There's no organised strategy rn, just the generous limits incentivising the use.

The only organised effort, I'd say, is on the engineering side with eventual creation of shared custom skills for coding agent that go into the repo.