Designing comparison criteria for AI vendors

Evaluate vendors against the intended workload

Define representative tasks and operating constraints before comparing providers. Include data handling, integration requirements, evaluation results, support, cost at expected volume and exit options. Marketing labels rarely establish whether a product fits a particular workflow. Distinguish mandatory requirements from preferences so a polished demo cannot outweigh a critical incompatibility.

Worked example

Illustrative scenario; not a claim of measured client results.

For a knowledge assistant, prepare a small set of ordinary, difficult and unsupported questions using permitted material. Compare whether each candidate finds the right sources, respects access boundaries and handles missing evidence. Record the configuration and review effort. A fluent answer should not receive a high score if it cites the wrong policy or discloses material to the wrong user.

Checks, limitations and next decisions

Where current features or contractual terms matter, verify them from the provider's official documentation and agreement rather than a generic comparison chart. Mark unanswered questions for follow-up. Avoid choosing from a single aggregate score that hides a failed requirement. The final recommendation should explain the conditions under which the selected option fits and what must still be tested before production use.

A practical starting point

Work through these checks in order. Use the results to decide what needs to change in your own workflow.

1. Define representative tasks and failure consequences before comparing vendors.
2. Test candidates using the same examples and acceptance criteria.
3. Review integration, support, portability and commercial terms independently.

A mistake to avoid

Do not infer production suitability from a promotional demonstration.

How to check progress

Record task success, review effort, latency and total operating cost under comparable conditions.