Being clear-eyed about the real capabilities and limits of AI-based evaluation makes the results more useful, not less.
Pattern recognition across large numbers of real examples, consistent application of evaluation criteria, and synthesising specific, evidence-based findings quickly — tasks that benefit from consistency and scale.
Whether a business's actual service quality matches its marketing claims, what happens in a live JS-rendered browser versus a static crawl, or highly subjective aesthetic taste — these require either live verification or human judgement AI genuinely can't substitute for.
Vague AI output ("your website could be improved") is nearly worthless. Specific, evidence-backed findings ("no H1 detected in crawled HTML, which affects X") are checkable and actionable regardless of the evaluation method behind them.
The most reliable use of AI-based evaluation is as a fast, consistent first pass that surfaces real, checkable findings — with genuinely ambiguous or judgement-heavy calls still benefiting from human review where the stakes justify it.
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