AI Workflows Need Evidence Before They Earn Trust
Key takeaway: The risk is not that an AI system makes one imperfect answer. The risk is that nobody has defined what “good enough” means before it touches a live business process.
Start with the task, not the model.
NIST’s AI Risk Management Framework says the tasks an AI system supports should be defined. For a business, that means naming the exact job: classify a document, extract fields, draft a reply, flag an exception or route a request. “Use AI everywhere” is not a testable requirement.
Build a test set from the work that already causes pain.
Use representative documents, requests and edge cases; decide what a correct output looks like; and record where a person must step in. NIST’s guidance calls for repeatable testing, evaluation, verification and validation processes, with metrics and methods documented.
Keep a human at the decision boundary.
The most useful first automation is often the unglamorous one: prepare, classify, extract, check and route. It reduces repetitive handling while leaving high-impact pricing, approval and exception decisions with the person accountable for them.
A simple acceptance test
Before rollout, ask: what inputs will the workflow receive, what output is acceptable, what failure looks like, and who resolves it? If those answers are vague, the workflow is not ready to automate.
What should be measured after launch
Track the share of cases completed without rework, exception volume, review time and the quality of outputs on current data. Monitoring is what turns a demo into an operational system.
Applied example from my work
The Smart Dietary Assistant research shows how a defined recognition task and evaluation process shape an applied AI system. My AI workflow service brings that evidence-led approach into production processes.
If you have a process that is ready to be tested, contact me; we can define the acceptance test before building it.