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Scorecard template

AI Vendor Scorecard Template for Comparing AI Tools

Use this AI vendor scorecard template to compare AI software vendors across workflow value, implementation effort, security, integrations, governance, pricing, adoption risk, and pilot evidence.

Updated 2026-06-24Scorecard templateBuyer enablement page
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1

Weighted scoring model

A useful scorecard makes tradeoffs visible instead of hiding them in a feature checklist.

  • Business value: workflow volume, measurable outcome, user pain, and executive priority.
  • Technical fit: integrations, data access, admin controls, deployment model, and API maturity.
  • Risk posture: security, privacy, audit logs, compliance evidence, human review, and failure handling.
2

Pilot evidence fields

The scorecard should reward vendors that prove value on your real data.

  • Record pilot dataset, acceptance threshold, observed accuracy, time saved, error reduction, and reviewer feedback.
  • Separate demo quality from operational quality: support response, onboarding effort, permissions, and reporting.
  • Capture total cost, including implementation, custom work, premium models, overage, and renewal terms.
3

Decision notes

The final row should explain why a vendor wins, not just show a number.

  • List must-have gaps, acceptable compromises, blockers, and negotiation points.
  • Identify which team owns adoption, measurement, governance, and renewal review.
  • Keep the rejected vendor notes for future comparisons and auditability.

Checklist

  • Each criterion has a weight and a clear scoring rule.
  • Scores use the same pilot evidence for each vendor.
  • Security and governance cannot be offset completely by feature scores.
  • Total cost includes usage, seats, implementation, support, and renewal risk.
  • The winner has named owners for rollout and measurement.

How to use this template

  1. 1Set category weights before reading vendor proposals.
  2. 2Score each vendor after the same demo and pilot path.
  3. 3Use notes to explain high-risk or low-confidence scores.
  4. 4Review the scorecard with business, IT, security, and finance before approval.

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FAQ

Questions about this AI template

What criteria should an AI vendor scorecard include?

An AI vendor scorecard should include business value, workflow fit, integration readiness, security posture, governance controls, implementation effort, pricing, adoption risk, and pilot evidence.

Should the highest AI score always win?

No. A vendor with a high feature score can still lose if security, governance, data policy, implementation cost, or user adoption risk is unacceptable.