AI Tool Evaluation Template
R&D Operating Model — Evaluate AI tools before adoption (see Section 11.3)
1. Security & Privacy (Critical)
Where does data go? Is it used for training? Compliance certifications?
| Question | Answer |
|---|
| Where is data sent? |
|
| Is code/data used for model training? |
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| SOC2 / ISO 27001 certified? |
|
| Encryption in transit and at rest? |
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| Data residency options? |
|
2. IP & Licensing (Critical)
Code ownership, license compatibility, indemnification for IP claims.
| Question | Answer |
|---|
| Who owns AI-generated code? |
|
| License compatible with our projects? |
|
| IP indemnification provided? |
|
| Clear enterprise ToS? |
|
3. Integration (High)
IDE support, CI/CD, JIRA, SSO — how well does it fit our toolchain?
| Question | Answer |
|---|
| Supported IDEs / platforms |
|
| CI/CD integration? |
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| SSO / SAML support? |
|
| Setup complexity |
|
4. Accuracy & Quality (High)
How good are the outputs? Acceptance rate, false positives, relevance.
5. Cost (Medium)
Pricing model, per-seat cost, volume discounts, hidden costs.
6. Adoption Friction (Medium)
How easy is onboarding? Does it disrupt existing workflows?
| Question | Answer |
|---|
| Time to onboard one engineer |
|
| Learning curve |
|
| Workflow disruption |
|
7. Vendor Stability (Medium)
Company maturity, enterprise support, product roadmap.
Evaluation Results
1.0 — Reject
3.0 — Conditional
3.5 — Approve
5.0
Recommendation: Score all 7 criteria to see the evaluation result.
Pilot Plan
Complete this section if the decision is Approve or Conditional.
R&D Operating Model — AI Tool Evaluation Template v1.0 | See Section 11.3 for governance framework and Appendix G for scoring guide.