A five-page fillable scorecard that turns an AI demo into a structured buying decision. Includes scoring anchors, evidence requests, practical test cases, and handover questions.
Give every provider the same problem
A fair comparison starts with a shared brief: the workflow boundary, current systems, representative inputs, expected outputs, and business outcome. Ask each provider to state assumptions, exclusions, and the people your team must supply. A polished demonstration can show promise without resolving those questions.
This original Clairvance scorecard is a buying aid, not a validated benchmark or endorsement of any vendor. It has fillable fields for your notes and ratings. Save the file locally and adapt the criteria to your procurement process. Completing it does not transmit your answers to Clairvance.
Use evidence-based scoring anchors
Rate each criterion from zero to three: zero means missing or unsupported; one means a general explanation; two means a specific, relevant artifact or demonstration; three means evidence verified against your agreed requirement. A score reflects the evidence available at the review date. Record the artifact, owner, and unresolved question beside it.
Do not confuse an unknown with a confirmed failure. Mark an unanswered requirement as open and decide what must be supplied. Use the same criteria for each vendor, and change weights only before the comparison or with a recorded reason. The total is a conversation aid; it cannot compensate for a failed critical requirement.
Examine the workflow behind the demo
Ask how the proposal uses your existing systems and handles missing data, duplicates, conflicting records, and an unavailable integration. Request a data-flow diagram and a description of what the system may read or change. Establish which actions need approval and how an unresolved item reaches a person who can act.
Use representative examples with appropriate permission and avoid sharing confidential production data during an early demonstration. Require a plan for how test data will be handled. Ask the provider to explain what evidence would make it recommend a simpler configuration or integration instead of AI.
Make acceptance and ownership concrete
Agree who defines success and how it will be tested. The scorecard includes questions about evaluation examples, error review, logging, access boundaries, failed-system behavior, and ongoing monitoring. Request evidence for the complete process, including the review effort that remains with your team.
NIST's voluntary AI Risk Management Framework provides a useful reference for risk discussions; it does not certify this scorecard or a supplier. See the NIST AI RMF resource page. The operational questions here are Clairvance's own review prompts and need to be adapted to the proposed work.
Review the handover and the exit
Clarify who owns the configuration, code, documentation, data, and accounts. Ask who can change the workflow, what training and support are included, which recurring dependencies remain, and what happens if the relationship ends. Check that your team can export its information and continue operating or return to a documented manual process.
The decision page distinguishes verified strengths, open evidence, and critical blockers. Record the next action as select, request evidence, run a bounded evaluation, or decline. Keep your rationale with the proposal. A strong partner should help make these tradeoffs clear, including the limits of what the available evidence proves.
Sources & further reading
- AI Risk Management Framework: overview and resources. National Institute of Standards and Technology, Current resource page; accessed September 2026. Accessed 2026-09-09.