AI Vendor Evaluation Scorecard
Compare AI partners objectively across Evidence, AI Safety & Governance, ROI, and Delivery Maturity. Built by Omovera practitioners who ship production AI—not slideware.
Executive Snapshot
4 pillars • 6 scoring categories • 100-point composite
Use the full infographic below in vendor briefings, board memos, and selection workshops.
Why Most AI Vendor Selections Fail
In 2026, “AI services” can mean anything from prompt engineering to full-stack, production-grade delivery. CXOs need a selection method that is objective, evidence-based, and risk-aware.
The Weighted Vendor Evaluation Scorecard (Out of 100)
Ask each vendor to self-score and submit evidence. Then validate independently.
| Category | Weight | What “Good” Looks Like | Evidence to Request | Red Flags |
|---|---|---|---|---|
| Evidence of Results | 25% | Production deployments with measurable KPIs in comparable contexts. | Case studies, KPIs, architecture overview, reference call (if possible). | Demos only, “confidential” claims with no specifics, no uptime/monitoring. |
| AI Safety & Governance | 20% | Data controls, audit logs, monitoring, approvals, human-in-loop, policy alignment. | Security architecture, monitoring plan, access controls, incident response. | No governance plan, no traceability, “model learns” without controls. |
| ROI & Business Impact | 20% | P&L-linked model: baseline → uplift, payback period, sensitivity analysis. | ROI spreadsheet, assumptions, KPI baseline, time-to-value roadmap. | Vague efficiency claims, no baseline, no tie to unit economics. |
| Delivery Maturity | 20% | Engineering depth, integration capability, CI/CD, observability, support model. | Team composition, delivery plan, DevOps approach, SLAs, runbook. | Outsourced engineering, unclear ownership post-launch, no MLOps/LLMOps. |
| Practitioner Credibility | 10% | Operators who’ve shipped AI under real constraints (risk, adoption, budgets). | Profiles, delivery artifacts, client stories, accountability examples. | Only advisors, academic-only teams, no production accountability. |
| Knowledge Transfer | 5% | Playbooks, documentation, internal enablement, reducing dependency over time. | Docs samples, training agenda, handover checklist, governance templates. | Black box delivery, “only we can operate this,” minimal documentation. |
The 4 Pillars CXOs Must Optimize
CXO Questions to Ask Every AI Partner
Infographic: AI Vendor Evaluation Scorecard (SVG)
Copy this SVG into your site (or export as PNG via any design tool). It matches the Omovera pastel/corporate theme.
FAQ (AI Vendor Evaluation)
What evidence should an AI vendor provide?
Production case studies, measurable KPIs, sanitized architecture overview, monitoring approach, and a clear post-launch support model.
How do we compare vendors if some won’t share client names?
Require anonymized metrics, sanitized diagrams, and auditable artifacts (security posture, runbooks, monitoring screenshots).
What should the board ask about AI safety?
Data access control, audit trails, monitoring, incident response, human approvals, and how failures are detected, explained, and corrected.
Request a CXO Vendor Review (Omovera)
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