Evaluating AI automation before implementation

Map the process before choosing a model

A repetitive task is not automatically a good AI use case. Begin by understanding the current process, its inputs, exceptions, decision points, owners and consequences of an incorrect result.

Some problems are better solved with ordinary workflow automation, search, validation or a system integration. AI should be used where it adds a useful capability.

Define acceptable behaviour

Before implementation, decide what the system may do autonomously, what requires human approval and what should cause the workflow to stop. Sensitive information, access controls and auditability should be considered from the beginning.

• Define success and failure criteria.
• Create representative test cases, including edge cases.
• Set rules for human review and escalation.
• Control which systems and records the workflow may access.
• Monitor error rates, cost and unintended behaviour.

Measure the actual outcome

Compare the new process against an agreed baseline. Measure the quality of results alongside time saved, exceptions, review effort and operating cost. A convincing demonstration is not a substitute for evidence from realistic use.