An AI tool scorecard template gives your team a repeatable structure for evaluating vendors. Without one, every purchase decision starts from scratch and depends on whoever ran the last demo. With one, criteria, weights, and scores accumulate into an audit trail procurement and security teams can trust.
This guide explains scorecard components, how to weight criteria for different team priorities, and how to archive results for renewal reviews. Pair the template with shortlists from our AI productivity and AI chatbot categories once your workflow is defined.
Scorecard Components and Sections
A complete scorecard has six sections: context, criteria, weights, scores, evidence, and decision. Each section serves a different audience. Context helps future readers understand why the evaluation happened. Evidence prevents scores from floating without support.
| Section | Contents | Owner |
|---|---|---|
| Context | Workflow statement, date, evaluators, shortlist | Requesting team lead |
| Criteria | Scorable requirements derived from workflow | Workflow owner + security |
| Weights | Priority percentages totaling 100% | Stakeholder committee |
| Scores | 1-5 rating per criterion per vendor | Individual evaluators |
| Evidence | Test outputs, screenshots, quotes, notes | Evaluators during testing |
| Decision | Recommendation, conditions, review date | Decision authority |
Criteria Categories: Function, Cost, Risk, Fit
Group criteria into four categories to prevent blind spots. Function covers task performance. Cost covers pricing at expected volume. Risk covers security, compliance, and vendor stability. Fit covers integration, team adoption, and support quality.
| Category | Example criteria | Typical weight range |
|---|---|---|
| Function | Output accuracy, format compliance, edge case handling | 25-40% |
| Cost | Monthly spend at volume, overage risk, seat efficiency | 15-25% |
| Risk | Data retention, certifications, lock-in, uptime SLA | 15-30% |
| Fit | Integration depth, onboarding time, support responsiveness | 15-25% |
Weighting for Different Team Priorities
Weights should reflect who bears the cost of a bad decision. A marketing team may weight output quality highest. An engineering team may weight API reliability and data export highest. A finance team may weight predictable pricing highest. Align weights in a 30-minute stakeholder session before testing begins.
Example weight profiles:
- Quality-first team: Function 40%, Fit 25%, Risk 20%, Cost 15%
- Budget-constrained team: Cost 35%, Function 30%, Fit 20%, Risk 15%
- Regulated industry: Risk 35%, Function 30%, Cost 20%, Fit 15%
- Integration-heavy stack: Fit 35%, Function 30%, Risk 20%, Cost 15%
Scoring Calibration Across Evaluators
Two evaluators scoring the same output differently is normal without calibration. Before the main evaluation, run a calibration round: score three sample outputs together and discuss what a "3" vs "4" means for each criterion. Write the definitions in the scorecard header.
Calibration rules that reduce variance:
- Define each score level with a concrete example (not just "good" or "bad").
- Require evidence links for any score of 1 or 5 (extreme scores need proof).
- Resolve disagreements greater than one point through joint review of the output.
- Average scores when evaluators disagree by one point; discuss when they disagree by two or more.
Archiving Scorecards for Audit and Renewal
Scorecards are most valuable at renewal, not at purchase. Store completed scorecards in a shared repository with the decision memo, test evidence, and contract terms. At renewal, re-run the scorecard against the same criteria to measure whether the tool still earns its subscription.
Archive checklist:
- Completed scorecard with individual evaluator sheets
- Blind test results if conducted
- Contract signed date, term, and renewal notice deadline
- Scheduled re-evaluation date (typically 90 days before renewal)
- Known limitations accepted at purchase time
Frequently Asked Questions
Can we update weights mid-evaluation?
Avoid mid-evaluation weight changes unless new information fundamentally shifts priorities (for example, a security requirement discovered late). If weights change, re-score all vendors from scratch and document why the change happened. Partial re-scoring introduces bias toward the vendor already in the lead.
Should vendors see our scorecard criteria?
Sharing criteria helps vendors tailor demos to your needs. Do not share weights or scores during evaluation. After selection, sharing the criteria (not competitor scores) sets clear expectations for the partnership.
How does the scorecard integrate with procurement governance?
Attach the completed scorecard to the purchase request as evidence of structured evaluation. Security and legal reviews run in parallel, not inside the scorecard. The scorecard answers "which tool"; governance answers "are we allowed to buy it."
Can one template work for all AI tool categories?
The structure is universal; the criteria are not. A writing tool scorecard weights output quality and tone control. An API platform scorecard weights latency, rate limits, and SDK quality. Reuse the template structure and replace criteria per workflow.
The Bottom Line
An AI tool scorecard template turns evaluation from a one-time opinion into a repeatable governance practice. Define criteria from your workflow, calibrate weights with stakeholders, score with evidence, and archive for renewal. The template is reusable. The decisions it documents are not.
