Why Every Engineering Team Needs an AI Workflow Audit
Most teams I've talked to adopted AI tools the same way they adopted Slack. Someone tried it, liked it, told the team, and suddenly it's everywhere.
That worked fine for Slack.
For tools that touch your codebase and shape how your engineers think about problems, it's a different story.
The pattern is almost always the same. One engineer reviews AI output carefully. Another ships it with a quick scan. There's no shared bar, no shared understanding of where it helps and where it doesn't. Everyone has their own prompts, their own workflows, their own mental model of when to trust it. None of that gets written down. When someone leaves, it goes with them.
Six months in, it shows up in places you don't expect. Code reviews where the reasoning is thin. Postmortems where nobody can fully explain a decision. System design sessions where the fundamentals feel shakier than they should.
That's the hidden cost. Not the obvious risk of shipping bad code. Something subtler: the gradual erosion of the shared standards that make a team coherent.
An audit isn't about slowing things down. It's about making a decision you're already making, but making it deliberately. Which tools are approved and for what tasks. What responsible review looks like for AI-generated code on your specific team, in your specific domain. What skills you need to keep sharp, and which ones you're comfortable handing off.
The teams that sort this out now will have a real advantage over the next few years. Not because they used more AI. Because they used it with intention.
If your team has been moving fast and hasn't stopped to think about this yet, that's probably the right time to do it.
If you want to talk through what this looks like for your team, get in touch.