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.

It has to be coming from above. This is what McKinsey thinks and I think I agree. AI adoption required company transformation. This can't be done from the bottom - you will face severe resistance as this threatens people jobs/positions. Question is - how much you can extract from operational activity that can be automated reliably with AI. The more you can transform your company/operations in a way to maximize the AI automation - the bigger competitive advantage you get.

The problem companies are facing - is the lack of expertise. CEOs / Investors hear the rumors, AI can generate asymmetrical advantage - but no one knows HOW? And there are no AI transformation leaders on the market atm. So companies would try just to do something to be able to claim - we are on the road of AI adoption.

Using Claude Code for coding is a typical first move. Does it create a competitive advantage? It depends if company can ship features faster or cheaper. Otherwise it doesn't.

I have not heard of success stories about successful operational transformation based on AI. But I see a lot of nano-size startups popping up which build products on AI and embed AI into operations since the very early stage.

We do that for business small, medium or large to custom a process for them. It's always difficult for business to clearly state their SOP from 0 to 1, and adoption gets more difficult when it comes to people or workflow that's not AI-minded. For well-established business, I always do this:

1. We deploy a Sounding Panel agent to sit in meetings, stand-ups, gatherings, brainstorming sessions and events whenever and wherever is allowed (small talks too if that's ok)

2. A few days/weeks later, SP agent would know what's going on here with information on workflows, complains, challenges, missing processing, and prospective feedbacks.

3. We then work with one business unit to define an AI-native workflow for the unit to try out - often Dev, Marketing, Design...

4. Deploy SP agent to gather feedbacks and refine the workflow again from Step 2.

It could hardly be a one-night thing to just "adopt" AI at a business. It's a recursive, inclusive, and multi-faceted organism of adoption. To me personally, human-in-the-loop is the top success criteria for AI adoption.