Score two AI tools across ten criteria and walk out of the decision meeting with something defensible instead of a preference.
Comparing two AI tools or vendors? Score them across 10 criteria and get a data-driven recommendation. Built for consultant-client decision meetings — print or share the results.
Software decisions get made on demo quality and whoever argued hardest, then justified afterwards. A scorecard does not remove judgement — it makes the judgement visible, which is what lets a group disagree productively instead of circularly. Its real value is that it surfaces where two people differ, which is usually one or two criteria rather than the overall verdict.
All ten matter, but in practice three do most of the work for small and mid-sized buyers:
Score independently, then compare. Have each person score alone before anyone shares numbers. Scoring as a group produces consensus around whoever spoke first, which is the exact failure the scorecard exists to prevent.
If the totals land within a few points, the tools are genuinely comparable and the scorecard has done its job — it has told you this is not where your attention should go. Decide on the criterion you care about most, or on price, and move on. Spending another three weeks on a decision the scorecard says is close is a more expensive mistake than picking the marginally worse tool.
If one wins decisively but you feel uneasy, the scorecard has surfaced something worth examining. Usually it means a criterion that matters to you is missing from the list, or one is worth far more than the others in your situation. Both are useful discoveries and neither is a reason to distrust the exercise.
Take the winner through the risk assessment, paying particular attention to vendor dependency and cost overrun — the two risks a scorecard comparison tends to under-weight because they only materialise after you have committed.
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