The unit of value in AI automation

When assessing an AI workflow, I start with the result that someone needs to trust and use.

For invoice processing, that could mean a correctly extracted, checked and posted invoice. For support routing, it could mean a request reaching the appropriate queue within an acceptable time. The definition of an acceptable outcome determines the evaluation.

Count the surrounding work

The model call is one cost. Data preparation, integration, retries, validation, exception handling and human review may also be required. Implementation and maintenance add costs over a different time horizon.

An illustrative workflow might generate a useful draft quickly while still requiring substantial review. That can be valuable. To evaluate it, compare total handling time and outcome quality with the current process, including the cases the automation cannot handle.

Use representative work and explicit quality criteria. A model's stated confidence is not a measured probability that the result is correct. Depending on the task, the important measures may include critical errors, missed cases, review effort, completion time and cost per acceptable outcome.

Separate capacity from cash

If the workflow saves staff time, the company gains capacity. A cash saving depends on what actually changes in staffing, external spend or workload. Additional revenue depends on whether the released capacity can serve real demand.

The assessment should therefore show what can be automated, where people remain involved, which costs change and what still needs testing.

Sometimes the best first step is AI assistance. Sometimes ordinary rules or an existing integration are sufficient. The relevant comparison is the one that achieves the required outcome within the business's cost and operating constraints.