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AI Tool Evaluation Scorecard

Score any AI vendor across 7 weighted criteria — privacy, integration cost, recurring cost, output quality, vendor stability, compliance fit, switching cost. Get a 0–100 score and a verdict before you buy.

Updated May 2026

Privacy + data handling

Does it train on your data? Where's data stored? Who else can access it?

weight × 3
Acceptable

Integration cost

Estimated engineering hours to wire into your existing stack

weight × 2
Acceptable

12-month TCO

License + per-seat + per-call + ops fees over a full year

weight × 2
Acceptable

Output quality (in your tests)

Run on your real data — not vendor demos

weight × 3
Acceptable

Vendor stability

Funding stage, runway, customer count, recent layoffs (Crunchbase + LinkedIn)

weight × 2
Acceptable

Compliance fit

SOC 2, HIPAA, GDPR, sector-specific certs you actually need

weight × 2
Acceptable

Switching cost

Data export format, contract lock-in, prompt portability if vendor disappears

weight × 1
Acceptable

Score

60 / 100 (45 / 75 weighted points)

Pilot before committing

Export:

Weights reflect how often each factor surfaces in post-purchase regret on AI-buyer surveys. Adjust mentally for your context — heavily regulated industries weight compliance + privacy higher; engineering-light teams weight integration cost higher.

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What it does

Score any AI vendor across seven weighted criteria — the same factors that show up in post-purchase regret on AI-buyer surveys. The output is a 0–100 score plus a verdict band (proceed / pilot / investigate / walk away).

The weights default to a generic profile. Heavily regulated industries should mentally re-weight privacy + compliance higher; engineering-light teams should re-weight integration cost + switching cost higher. Export the scorecard to attach to a procurement memo.

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How to use it

  1. Enter the tool / vendor name.
  2. Score each of the 7 criteria 1-5 based on real evidence (not vendor claims).
  3. Add notes per criterion — sources, gotchas, follow-up questions.
  4. Read the weighted total + verdict.
  5. Export to attach to your procurement decision doc.

Frequently asked questions

Where do the weights come from?
Generic SaaS post-purchase regret studies (Gartner, G2 buyer reports) consistently surface privacy + output quality as the top two regret drivers. Integration cost, recurring cost, and vendor stability cluster next. Switching cost is rated lowest in regret terms but only because most buyers don't realize it matters until they try to leave.
Should I score against vendor demos or my own data?
Always your own data. Vendor demos are curated for the things the model does best. The single most consistent finding across AI procurement post-mortems is that 'output quality' assessed from demos overstates real-world quality by 30-50%.
How do I evaluate vendor stability for a private company?
Crunchbase for funding rounds + Trove for headcount changes + LinkedIn for layoff signal. Recent down-round, sudden senior departures, or 'restructuring' announcements are red flags. Public revenue is unavailable, but customer count growth (or lack of it) shows up in case-study cadence.
What's a 'walk away' score?
Below 45/100. At that level the tool has multiple structural problems and no single fix changes the math. Reconsider whether you need an AI tool at all for this use case, or shop alternatives.

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Show the math + sources

Formula

Weighted score = Σ (criterion_score × criterion_weight) / Σ (5 × criterion_weight) × 100. Seven criteria: privacy + data handling (×3), output quality (×3), integration cost (×2), 12-month TCO (×2), vendor stability (×2), compliance fit (×2), switching cost (×1). Each scored 1–5. Verdict bands at ≥75 (proceed), ≥60 (pilot), ≥45 (high risk), <45 (walk away).

What this assumes

Weights reflect generic SaaS post-purchase regret patterns from public buyer surveys. Heavily regulated industries should re-weight privacy + compliance higher. Engineering-light teams should re-weight integration + switching cost higher. The tool is a structured worksheet — final purchase decisions should integrate the score with budget, timeline, and stakeholder constraints not captured here.

Sources

  1. Gartner — Critical Capabilities for Generative AI Engineering Tools
  2. G2 — 2025 SaaS Buyer Behavior Report
Methodology last verified: 2026-05-03

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