AI Vendor Evaluation Scorecard
This scorecard weights the criteria that actually matter for your decision — security, accuracy on your own data, integration effort, total cost, and support — and scores each vendor against them side by side. Weighting your own criteria first, before scoring anyone, is what keeps the comparison from drifting toward whichever vendor pitched best.
Download the CSV, set a weight (out of 100) for each criterion based on what matters most to your decision, then score each vendor 1-5 per criterion. The weighted score is score times weight divided by 100, summed per vendor — compute it in your own spreadsheet after scoring, or add a formula once you open the file.
On this page
At a glance
- Weight your own criteria first, before scoring any vendor — deciding what matters after seeing a demo tends to favor whichever vendor demoed best.
- Includes 'accuracy on your representative sample,' not vendor-supplied demo accuracy: test on your own data before scoring that row.
- A blank editable CSV: the five example criteria and their weights are a reasonable starting point, not a universal standard.
Who this is for
This is for anyone comparing more than one AI vendor for the same workflow and who needs the comparison to survive a challenge from whoever didn't get picked.
A vendor's demo accuracy is not your accuracy. Once you've narrowed to a shortlist with this scorecard, the questions worth asking before you sign matter more.
What's inside
Weighted criteria, scored per vendor:
| Criterion | Weight (%) | Vendor A Score (1-5) | Vendor B Score (1-5) |
|---|---|---|---|
| EXAMPLE: Accuracy on your representative sample | 25 | (blank — yours to score) | (blank — yours to score) |
The five example criteria (security, accuracy, integration effort, total cost of ownership, support) sum to 100% weight — adjust the weights, add or remove criteria, and add a column per additional vendor as needed.
Download the vendor evaluation scorecard (CSV)Should I use the example weights as-is?
Only if they genuinely reflect your priorities. A team more sensitive to integration risk than cost should weight accordingly — the defaults are a starting point, not a recommendation.
How do I score accuracy fairly across vendors?
Test each vendor against the same representative sample of your own data, not their own demo data, and score what you actually observed.
Can I compare more than two vendors?
Yes — add a Score/Weighted column pair per additional vendor.
Next step
A scored, weighted comparison is what turns a procurement decision into something you can defend after the fact, not just explain.
Related: read questions to ask an AI consulting firm before you sign with whichever vendor scores highest.