TL;DR
The AI vendor evaluation scorecard replaces gut-feel vendor selection with a written rubric the team agrees on before demos start. Six axes: capability fit, total cost at volume, integration depth, data and security posture, roadmap alignment, support quality. Each axis is scored 1 to 5 against anchored definitions, weighted by importance, then summed. The total is the input to the decision, not the decision itself.
- Six axes. Anchored 1-5 rubric. Weighted total.
- Bake-off, not demo. Same data, same task, same criteria.
- Five-person eval team, not a committee of twelve.
- If no vendor wins, do not force a pick.
In this article
Why scorecards beat gut feel
The most expensive AI procurement mistakes I have seen at consumer brands have one thing in common. The buying decision happened in a room. Someone said "their demo was incredible." Someone else said "their team really gets us." A check got cut. Nine months later the tool is shelfware, the integration never finished, and the cost-per-inference at production volume is three times what was modeled.
Gut feel does not scale. Vendor demos are designed by the vendor to demo well. Sales engineers are trained to make capability look turnkey. The objective input is the workflow data, the contract terms, and the integration map. Everything else is theater.
The scorecard exists to put structure around the decision before the demos start. If you write the rubric first, the demos cannot reshape it. If you anchor the score definitions, the scoring stops being a vibes exercise. If you weight the axes, the team is forced to articulate what actually matters about this purchase.
Demos are designed to demo well. The scorecard is designed to buy well. They are not the same thing.
The six evaluation axes
Six axes cover the decision for almost every AI vendor evaluation at a consumer brand. Adjust the weights, not the axes.
1. Capability fit
Does the vendor actually do the job your workflow needs done. Not the job the demo showed. The job your workflow needs done. The test is the bake-off, not the deck.
2. Total cost at volume
Not the sticker price. Not the per-seat number. The fully-loaded cost at production volume, including retries, eval loops, monitoring, rate-limit headroom, and integration engineering. For the math, see the LLM cost calculator.
3. Integration depth
How deeply the tool lives inside the workflow your team already uses. A tool that requires a new tab is a tool that gets abandoned in week three. A tool that integrates into the existing CRM, CDP, or production system has a chance at adoption.
4. Data and security posture
Where the data goes, how it is stored, who has access, what the audit trail looks like, what happens if the vendor has an incident. The consumer-brand minimum: SOC 2 Type II, clear data residency, a written data processing agreement, and a real security questionnaire response (not a marketing PDF).
5. Roadmap alignment
Are they building toward where your workflow is going. Or are they building toward someone else's use case and you are along for the ride. Ask for the next two quarters of roadmap in writing. Vendors who will not show it are telling you something.
6. Support quality
How fast they respond. Who answers. What the escalation path looks like when something breaks in production at 9pm on a Friday. The lowest-weight axis in most cases, but the one that determines what year two of the relationship feels like.
The AI vendor scorecard (copy this)
Here is the scorecard. Copy it, share it with the eval team, agree on the anchored definitions and the weights before any demos happen.
# AI Vendor Evaluation Scorecard
**Workflow:** [the workflow this vendor will support]
**Anchor metric:** [the metric the workflow is trying to move]
**Eval team:** [5 names, 1 line each: workflow owner, tech lead, procurement, data/security, sponsor]
**Bake-off date:** [date]
**Decision date:** [date]
---
## Scoring rubric (1-5, anchored)
**5 - Clearly differentiated.** Best-in-class against this axis. No meaningful gap.
**4 - Strong fit.** Solves the job. Minor friction worth accepting.
**3 - Meets baseline.** Acceptable. No competitive advantage.
**2 - Below baseline.** Meaningful gap that requires workaround or extra spend.
**1 - Deal-breaker.** Cannot proceed without resolution. Disqualifies the vendor.
---
## Weights (must sum to 100%)
| Axis | Weight | Why this weight |
|---|---|---|
| Capability fit | 25% | Does the job your workflow needs done |
| Total cost at volume | 25% | Production economics, not sticker price |
| Integration depth | 15% | Lives inside existing tools or a new tab |
| Data and security | 15% | Higher if customer-facing |
| Roadmap alignment | 15% | Next 2 quarters in writing |
| Support quality | 5% | Year-two reality |
---
## Vendor scores
| Axis | Weight | Vendor A | Vendor B | Vendor C |
|---|---|---|---|---|
| Capability fit | 25% | [1-5] | [1-5] | [1-5] |
| Total cost at volume | 25% | [1-5] | [1-5] | [1-5] |
| Integration depth | 15% | [1-5] | [1-5] | [1-5] |
| Data and security | 15% | [1-5] | [1-5] | [1-5] |
| Roadmap alignment | 15% | [1-5] | [1-5] | [1-5] |
| Support quality | 5% | [1-5] | [1-5] | [1-5] |
| **Weighted total** | **100%** | **[sum]** | **[sum]** | **[sum]** |
---
## Per-axis evidence (required for every score)
For each axis, each vendor, write one sentence of evidence.
A score without evidence does not count.
**Vendor A**
- Capability fit ([score]): [evidence from bake-off]
- Total cost at volume ([score]): [evidence from cost model]
- Integration depth ([score]): [evidence from integration map]
- Data and security ([score]): [evidence from questionnaire]
- Roadmap alignment ([score]): [evidence from roadmap doc]
- Support quality ([score]): [evidence from reference calls]
[Repeat for each vendor]
---
## Deal-breakers (any 1 disqualifies, regardless of total)
- [ ] No SOC 2 Type II (or equivalent) for customer-facing use cases
- [ ] No written DPA available
- [ ] Pricing model that does not survive 5x peak volume
- [ ] No production reference at a comparable scale and use case
- [ ] No clear path to integration with our existing stack
- [ ] Single point of failure in their team for our accountThat is the scorecard. One page if you keep the formatting tight. The deal-breaker section is the safety valve. A vendor can win the weighted score and still be disqualified because one deal-breaker is tripped. That asymmetry is on purpose. Some failures are not score-able. They are veto conditions.
The bake-off protocol
The bake-off is where the scorecard gets its real data. Demos are vendor-controlled. Bake-offs are buyer-controlled. The protocol is short.
# AI Vendor Bake-off Protocol
**Step 1.** Define the task.
- One realistic task from the actual workflow
- Same task for every vendor
- Written brief, signed off by workflow owner
**Step 2.** Define the input.
- Same input data for every vendor
- Scrubbed of anything that would identify you in a vendor pitch deck
- Representative of production volume, not a curated best-case
**Step 3.** Define the success criteria.
- 3-5 measurable criteria, written before vendors see the task
- Workflow owner signs off
- Eval team agrees blind scoring
**Step 4.** Run the bake-off.
- Same 5-business-day window for every vendor
- No mid-bake-off Q&A unless every vendor gets the same answer
- Output collected, anonymized if possible
**Step 5.** Score the output.
- Each eval team member scores each vendor independently
- Scores compared, outliers discussed
- Final score is the median of the team scores per axis per vendorRun the bake-off before the contract conversation. Vendors who refuse to participate in a structured bake-off are telling you something about how their tool performs outside their demo environment. Sometimes the right call is to disqualify them on that signal alone.
If a vendor will not run a structured bake-off, they are not selling you software. They are selling you a deck.
The decision artifact
The scorecard is the input. The decision is a separate artifact. One page, signed by the sponsor.
# AI Vendor Decision: [Workflow Name]
**Date:** [decision date]
**Sponsor:** [name]
**Recommended vendor:** [name]
**Total contract value (year 1):** $[amount]
**Total contract value at 5x peak volume:** $[amount]
## Why this vendor
[Two to three sentences tying the recommendation to the weighted score
and the workflow anchor metric.]
## What we are watching
- Risk 1: [risk] | Mitigation: [action]
- Risk 2: [risk] | Mitigation: [action]
- Risk 3: [risk] | Mitigation: [action]
## What happens next
- Contract signed by: [date]
- Production go-live by: [date]
- First quarterly review: [date]
Signed: _______________ (Sponsor) | _______________ (Workflow owner)For the broader program this scorecard sits inside, see the AI Transformation Playbook for Consumer Brands and the AI Transformation Roadmap Template. Vendor selection is a node inside the program, not the program itself. For the related cost work, see the hidden cost of AI vendor sprawl.
The bottom line
The AI vendor evaluation scorecard turns vendor selection into a written decision instead of a gut call. Six axes, anchored 1-5 rubric, weighted total, deal-breaker veto, structured bake-off, signed decision artifact. The whole thing fits on two pages.
Copy the scorecard. Agree on the weights before the first demo. Run the bake-off. Score the output. Sign the decision. If no vendor wins, do not force a pick. That is the cheapest mistake to avoid.
FAQ
What is an AI vendor scorecard?
An AI vendor scorecard is a structured rubric that scores candidate AI vendors against six weighted axes: capability fit, total cost at volume, integration depth, data and security posture, roadmap alignment, and support quality. It replaces gut-feel vendor selection with a written artifact the team agrees on before the bake-off.
How do you score AI vendors?
Each axis is scored 1 to 5 with anchored definitions written before the demos start. A score of 3 means meets baseline, 5 means clearly differentiated, 1 means a deal-breaker gap. The scores are weighted by the importance of the axis to the workflow, then summed. The total is the input to the decision, not the decision itself.
Who should be on the AI vendor eval team?
Five roles minimum: the workflow owner (the person whose team will use the tool), the technical lead, the procurement or finance lead, the data and security lead, and the executive sponsor. Five is enough to cover the axes and small enough to make a decision. Avoid evaluation committees larger than seven.
How do you handle AI vendor demos?
Demos are unreliable input. Run a structured bake-off instead: give each vendor the same realistic input data, the same prompt or task, and the same evaluation criteria. Score the output against your own rubric, not against the demo script. The bake-off is where most vendors who shine in slides reveal real limitations.
What weighting works for AI vendor scoring?
For most consumer-brand use cases, weight capability fit and total cost highest (around 25 percent each), with integration depth, data and security, and roadmap alignment around 15 percent each, and support quality around 5 percent. Adjust based on the workflow. Customer-facing use cases push data and security higher.
What if no vendor wins the AI scorecard?
That is a useful outcome. It means either the requirements are not yet clear or the market is not yet mature. Both are signals to slow down. Consider building a minimum-viable internal version, narrowing the use case until one vendor clearly fits, or revisiting in two quarters. Forcing a pick when no vendor wins creates more problems than it solves.