An AI audit is a structured assessment of where AI creates the most leverage in your business - which workflows are the highest-ROI candidates, what tools are already in place, and what the implementation roadmap should look like. It is the diagnostic before the prescription. Companies that audit before implementing deploy successfully at higher rates than those who start with tools.
A well-run AI audit produces a prioritized workflow map with estimated time savings, a tool recommendation specific to your stack and workflows, a sequenced 90-day implementation plan, and a measurement framework for tracking ROI. If those four outputs are not in the deliverable, it is not a real audit.
Map every major workflow in the business that involves significant writing, research, analysis, or repetitive tasks. For each workflow: who does it, how often, how long does it take, and what is the output. This is the raw material for prioritization. A simple spreadsheet: workflow name, owner, frequency, time per instance, output type.
Score each workflow on two dimensions: time impact (hours per week recovered if AI handles it) and implementation difficulty (how complex is the AI setup required). The highest-priority candidates score high on time impact and low on implementation difficulty. These are your 90-day targets.
For your priority workflows, identify which specific AI tools and configurations address them. Not generic AI recommendations - specific tools, specific configurations, specific costs. This requires knowing the tool landscape well enough to make accurate recommendations.
Sequence the priority workflows into a 90-day plan: what gets deployed in weeks 1 to 2, weeks 3 to 6, weeks 7 to 12. For each workflow, define the baseline metric (current time per workflow) and the target metric (expected time post-AI). These become the measurement framework for proving ROI.