Auditing a Process for AI Transformation
Before you automate a process, audit it like an operator. This guide walks a typical cross-functional workflow, shows where the handoffs and dark data hide, and gives you the five moves that turn a process audit into an AI roadmap that compounds.
A Practical Guide for Executives on Auditing a Process for AI Transformation
The five moves of a process audit
Map the process as it actually runs
Document the real sequence, not the version in the process document. Interview the people executing the work. Undocumented workarounds are where the integration debt lives.
Instrument every system handoff
Mark each point where data changes systems, format, or owner. Note whether the transfer is an API, a file export, or a person retyping. These are your integration candidates.
Locate the dark data
Identify data the process captures but never uses. Ticket text, call logs, form fields, rejected records. This is usually the highest value asset in the workflow and nobody owns it.
Score each step for AI fit
Assess volume, rule clarity, data availability, and cost of a wrong answer. High volume with clear rules and clean data is where automation returns. Judgment under ambiguity is not.
Design the target state around the data layer
Build the shared data, evaluation, and governance foundation first. Sequence use cases to inherit it. Point solutions do not compound.
Follow Jeff Z Johnson on LinkedInThis audit is the first mile of AI Compass.
The AI Compass assessment applies this exact discipline across your highest-value workflows. In two to three weeks it ranks your AI opportunities by ROI and hands you a roadmap your leadership will approve, whether you build it with us or not.