Most failed AI adoptions don't fail because of the technology: they fail because of the order. The tool gets bought before the flow is understood, training happens in the abstract before cases are defined, and three months in, the license is paid but unused. The audit exists to invert that order.
What gets mapped
Three layers. Flows: how work comes in, whose hands it passes through, where it gets stuck. Tools: which systems exist (ERP, CRM, spreadsheets, email) and how — or whether — they talk to each other. Data: where it lives, who touches it, how reliable it is. With those three layers visible, AI opportunities stop being a brainstorm and become a prioritized list.
How to prioritize
Each opportunity is weighed by impact (hours freed, errors avoided, revenue enabled) and risk (data sensitivity, cost of error, need for oversight). The first thing you implement is not the most spectacular: it's the one with high impact, low risk and a clear owner inside the team.
The deliverable
A serious audit ends in an actionable plan: prioritized cases, tools chosen on top of the environment you already have, a governance and human-oversight scheme, and metrics to know — at 90 days — whether it worked. If the plan can only be executed by whoever wrote it, it's not a plan: it's a dependency.