Summary
- AI should be assessed by the actions it can take, the evidence it exposes and the path for human correction.
- A pilot needs task-level accuracy, exception rates, supervision time and rollback results—not a broad claim of intelligence.
Artificial intelligence covers techniques that infer patterns, generate outputs or select actions from data. That breadth is useful for explaining the field but weak for approving a production deployment. A text assistant, fraud classifier and autonomous workflow create different failure modes even when all carry the same AI label.
The first control is decision authority. A low-risk system may propose wording that a person accepts or rejects. A higher-risk system may change a customer record, block a payment or trigger infrastructure work. The latter requires explicit permissions, durable logs, a way to see the supporting evidence and a named owner able to reverse the result.
Performance also belongs in the workflow, not in a general benchmark. Teams should measure accepted output, false action, missed exception, time spent checking, recovery after tool failure and behaviour when data changes. Automation that produces more cases for humans to investigate may shift cost rather than remove it.
Infrastructure dependencies matter as well. Availability, latency, data handling and provider changes can alter the service after a successful trial. A useful exit plan preserves essential records and keeps a manual or alternative path for critical decisions.
The next credible disclosure is a monitored pilot with a fixed task, clear decision boundary and published correction rate. Until that exists, a capability demonstration shows possibility; it does not show reliable operations.
