Ometz AI

Buying AI · 5 min read · June 11, 2026

How to read AI vendor case studies (and the benchmarks that matter)

The AI market runs on suspiciously round numbers. A buyer's guide to separating evidence from theater — written by a vendor, deliberately against our own interest.

Spend ten minutes researching AI vendors and you'll meet the same claims: 10× pipeline, 300% ROI, 'enterprises save millions.' Some are true somewhere. Most are unfalsifiable. As a vendor, we have every incentive to play the same game — which is exactly why we'd rather arm you against it.

Three tests separate evidence from theater. Specificity: a real case study names the workflow, the baseline, the time period, and the measurement method — 'reduced verification time from 14 staff-hours a week to 3, measured over 90 days' beats '10× efficiency.' Attribution: did the number come from the client's systems or the vendor's marketing team? Survivorship: one great outcome proves possibility, not probability — ask what the median engagement achieves.

Why we lead with third-party benchmarks

You'll notice the numbers on this site are mostly not ours — they're from Harvard Business Review, McKinsey, Deloitte, Clio, and industry call studies, cited and labeled as market benchmarks. That's deliberate. Published research describes the size and shape of the problem honestly. Your results depend on your call volume, your funnel, and your follow-through — which is why our engagements start with a diagnosis that measures your baseline before we promise anything.

The diagnosis-first model is itself the test we'd suggest applying to anyone in this market: a vendor confident in their product will happily measure your current state first, because the baseline is what makes the after meaningful. A vendor who skips the baseline is planning to grade their own homework.

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