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The next AI skill to buy may be evaluation
A small team needs someone who can decide whether a useful-looking answer deserves action.
Zocri · 2 min read · Sources checked
More output can increase the need for judgment
OECD's 2024 research finds business and management skills prominent in occupations highly exposed to AI. It does not calculate the return on hiring a reviewer. For a small company, it raises a practical possibility: the missing capability may be evaluating the output already available, rather than generating more of it.
If staff can produce quotes, summaries or draft instructions faster than the business can approve them, another tool may enlarge the queue. The next investment could instead be clearer standards, reviewer training or a limited specialist review of the task and its failure cases.
Define what must be evaluated
Separate factual correctness, completeness, tone and authority to act. A response can be polite and factually plausible while omitting a condition that changes the customer's decision. Give each important quality requirement a concrete example so reviewers do not rely on a general feeling that the output looks good.
Identify which checks require specialist knowledge. A general employee may verify a service date but be unable to judge a technical recommendation. Keep those responsibilities distinct.
Compare internal and external help
An internal reviewer has continuing context and can improve the process as it evolves. An external specialist may be useful for an initial assessment or a domain the team does not understand well. Compare these arrangements by availability, knowledge transfer and recurring cost, rather than prestige alone.
Ask for a review method and examples of evidence the reviewer will inspect. A supplier saying the tool is safe or accurate without explaining the task and test conditions has not provided a useful evaluation service.
Make review improve the next attempt
Record recurring failure patterns and update the brief or reference accordingly. Keep the original difficult examples to check whether a later change actually resolves the problem. Review should reduce avoidable uncertainty over time, not become an endless process of silently repairing the same errors.
Judge capacity by completed decisions
Track the time from draft to accepted result, the rate of substantial correction and the important errors discovered after approval. Use those measures to decide whether more review capacity, narrower automation or a different process is needed.
This is our purchasing framework, informed by the skills research. Evaluation has value when it helps the company act on dependable information at an acceptable cost. It should earn its budget through better decisions and fewer preventable loops, rather than becoming another abstract capability the business claims to possess.