Manufacturer avoids an estimated $7.9 million in AI rework with a 4-hour governance tabletop

01Make AI decisions defensible

The manufacturer was scaling AI across engineering, operations, quality and the back office. Each initiative showed promise. Leadership wanted assurance that, if something went wrong, it could explain who approved each AI decision, what data it relied on and why. It engaged our team to test that assurance before the initiatives went further.

02Moving from separate definitions to one shared language

In a 4-hour tabletop, leaders from each function walked through a realistic scenario. Each group showed how its AI would act, who would approve it and what evidence would prove it.

The exercise surfaced a gap no system review would have caught. Different teams used the same core term, [TERM], to mean different things. [Briefly describe how each team defined it.]

Every AI initiative had inherited its team's meaning. The same word fed different data, different rules and different decisions, and each team believed its version was the company's version. Left alone, those differences would have been built into workflows, controls and reporting, and multiplied as each initiative scaled.

03Transitioning to a governed AI model

The findings changed more than the AI roadmap. They changed how the organization governs decisions.

The affected AI initiatives were refactored around one agreed definition. Controls that depended on the term were rewritten so every team tests the same thing. Foundational governance was reset across the organization, with shared definitions, named owners and a review step before any new AI use case goes live.

Timing made the difference. The initiatives were still early in their workflows, so the fix meant realigning plans rather than tearing out systems already in production.

04Accomplishing more before the build

05Driving value today and tomorrow

That estimate reflects [how it was calculated, for example rework avoided, delayed launches avoided, audit and remediation costs avoided].

Just as important, leadership now has the evidence to sign off on AI with confidence. As new initiatives come forward, they start from one language, one set of controls and one standard of proof.