Strong pilot candidate
Map the workflow contract, test safe examples, and measure against the current baseline.
Free AI use-case assessment
Score one real business process before you automate it. You’ll get a transparent recommendation, the weakest factor to fix, and a six-line workflow contract for a safer pilot.
Five-factor audit
How to use the result
The audit helps compare possible starting points. It cannot prove accuracy, adoption, safety, savings, or ROI before representative real work is tested.
Map the workflow contract, test safe examples, and measure against the current baseline.
Improve the weakest factor. Reduce permissions, actions, inputs, or edge cases before building.
Find work that happens more often, creates more friction, and is easier to review and measure.
A high-impact use case needs qualified oversight, restricted authority, testing, monitoring, and a manual fallback.
Questions, answered
Start with work that happens frequently, creates meaningful friction, uses accessible and reliable inputs, allows human review, and has a measurable baseline. Avoid making a high-impact autonomous decision your first workflow.
An AI workflow audit is a structured review of one business process before implementation. It tests whether the work is frequent, painful, supported by suitable inputs, reviewable, measurable, and safe enough for a controlled pilot.
No. The score is a decision aid, not a validated benchmark or results guarantee. A high score means the workflow may deserve a controlled pilot. Representative tests, failure logs, human review, and measured results are still required.
Use deterministic automation when triggers, rules, and actions are predictable. Consider an agent when the workflow genuinely requires interpreting unstructured information, choosing among tools, or adapting the next step. Use the least complicated system that can do the work reliably.
Record the current baseline before implementation. Depending on the workflow, measure time per task, error or correction rate, response time, cost per output, qualified leads, conversion, turnaround time, adoption, or gross margin. Include human review and failure costs.
This practical scorecard is informed by risk and implementation principles in the NIST AI Risk Management Framework and OpenAI's practical guide to building agents. It is not an official NIST or OpenAI assessment.