Summary
- Evaluate AI against completed work, representative errors and recovery, not an impressive demonstration.
- Permissions should expand only after evidence, review and rollback work under real conditions.
Artificial intelligence covers systems that infer, classify, generate or recommend from data. The label says little about whether a particular deployment is safe or economical. Teams should define the decision being assisted, the cost of false output, the human checkpoint and the systems the tool may change. A small controlled trial should record accepted results, corrections and incidents. The useful question is not whether the system appears intelligent, but whether it improves an outcome without creating an unowned failure path.
