AI Opportunity Audit

Most AI projects start with a tool and go looking for a problem. The Audit starts with the process instead. Define: we agree on the problem, the customer, the scope, and the number that would prove improvement. Measure: we baseline volume, cycle time, rework, and cost, using AI to turn unstructured history like emails and tickets into usable data. Analyze: we map each workflow step by step, separate value from waste, find root causes, and decide for every remaining step whether it belongs to a person, ordinary software, AI, or AI with a person reviewing. The result is an AI Opportunity Matrix with the economics written out. Then we prototype the top candidate and test it against the baseline, so the go/no-go decision rests on a measured result instead of a demo.

What You Get

• Problem definition, scope, and the metric that will prove improvement
• Measured baseline: volume, cycle time, rework and defect rates, labor cost
• Process maps with waste and root causes identified
• Step-by-step decomposition: person, software, AI, or AI with human review
• AI Opportunity Matrix ranked by feasibility, impact, risk, and effort, with economics
• A working prototype of the top candidate, tested against your baseline where feasible
• Draft AI usage policy and data-handling guardrails
• Go/no-go recommendation with a fixed-scope Improve plan