A Shadow-AI Policy Your Engineers Will Actually Read

AMost AI use policies are unenforceable wallpaper. Here's the one-page version we ship to engineering orgs. Across regulated engineering programs, teams are working through the same set of questions: where does AI actually fit into the system of record, how do you keep traceability intact, and who signs off on the model's output when an auditor walks in?

The short answer is that AI is landing fastest in the places where engineers already burn hours on low-leverage work — drafting requirements, cross-referencing change orders, summarizing review comments — and slowest in the places where a wrong answer becomes a certification problem.

"The teams getting value out of AI right now are the ones who treated it like any other piece of engineering software: scoped, reviewed, and wired into the workflow they already trust."

What's actually shipping

Most of the vendor announcements over the last two quarters fall into three buckets: drafting assistants tied to a requirements tool, review copilots tied to CAD or PLM, and search/Q&A layers sitting on top of program documentation. The first two are where the real productivity gains are showing up; the third is mostly still demo-grade.

The engineering orgs that have moved fastest share a few traits: leadership treated AI as an integration problem rather than a procurement one, security got pulled in during the pilot rather than at the end, and the workflows were rebuilt around AI output instead of bolting it onto the existing process.

Where this is going

Expect the next wave to be deeper integrations into PLM and traceability tools, more on-prem and air-gapped deployment options for defense and aerospace programs, and the first serious push from regulators on how AI-generated artifacts should be documented and signed off. Teams that have already built the muscle for AI-assisted review will be in a much better position when that guidance lands.