Compare AI tools with a task-based scorecard

Define the job before comparing tools

"Best AI" is too broad to evaluate. A procurement-summary assistant, a design tool and a research workflow have different failure costs. Pick one job and write down its approved inputs, required output and acceptable review effort. Test the actual plan and workspace you might use, not a feature shown in a different subscription tier.

Build a representative test set

For an original example, choose twelve synthetic purchase requests: six ordinary, two missing a required field, two with contradictory dates, one with an instruction embedded in a supplier document and one too large for your normal process. Write the expected facts and the correct escalation for each case before running any model.

Hide tool names from the reviewer where practical. Use the same inputs and instructions, record the date and run more than one trial for unstable tasks. Do not upload real confidential purchase requests to unapproved accounts just to make the test realistic.

Score the result, not the vocabulary

Required facts correct: ___ / ___
Unsupported claims introduced: ___
Missing-information cases correctly escalated: ___ / ___
Minutes of human review per output: ___
Format accepted by the next system: yes / no
Privacy and connector controls verified: yes / no
Measured task cost and applicable plan: ___
Hard failures and owner decision: ___

Set hard gates before scoring convenience. For example, disclosure of restricted inputs or unauthorized purchasing should disqualify a workflow regardless of its average writing score. Then compare usable output rate and total handling time. Avoid a single weighted number that hides a serious failure behind good style.

Make a decision you can reproduce

Suppose Tool A produces ten usable summaries with four minutes of review each, while Tool B produces eleven with nine minutes of review each. That is not enough information to select a winner: examine which requests failed, what the errors could cause and whether the extra review burden is acceptable. Document those trade-offs with the task owner.

Retest after changing the model, brief, data connector or workflow. Keep a small holdout set that was not used to tune the prompt. Record failures rather than quietly replacing difficult test cases. A pilot supports a limited decision about this workflow; it does not certify the tool as safe for every department.

Sources and review

Reviewed October 16, 2026. Scorecard and purchase-request fixture are original EAIRI examples. Technical evaluation reference: OpenAI evaluation best practices.