AI transformation is a forcing function, even if it fails

A few months ago I volunteered to be one of the AI Champions for our company’s Operations group — 30+ teammates across manufacturing engineering, quality, supply chain, product data, and logistics. My job is to get AI embedded in how we work.

We ran sessions on enterprise AI access, Claude Cowork, building Skills. Almost everyone signed up. Most people use AI daily now.

But adoption rate is a weak proxy for transformation. And the more I look at what it’ll actually take to get there, the more I realize the real work has nothing to do with AI itself.

Here’s what I mean:

• A lot of process knowledge lives in people’s heads, and Claude can’t read minds yet. Making that knowledge visible to AI means writing it down for the first time.
• Requirements today often start loose and get refined through iteration until the outcome looks right. That works well person-to-person. It’s a different story when you want an AI to act agentically.

So even if some of this AI transformation effort doesn’t pan out, there’s value in trying. Getting better at capturing process knowledge and specifying outcomes upfront makes us sharper operators whether we end up embracing AI or not.