Assess AI operating model maturity across 15 criteria
Assess how you manage AI initiatives, data, model quality, security, operations, and links to business metrics.
Criteria for a Mature AI Operating Model
- Each AI initiative has a defined business objective, baseline metric and expected impact.
- An AI portfolio owner is assigned at company level with prioritization rules.
- Model quality criteria are linked to process metrics and error cost.
- Data owners, authorized use cases and retention periods are defined.
- Reproducible data preparation, testing and model deployment workflows exist.
- Quality is validated against representative examples, including rare and high-risk scenarios.
- The path from hypothesis to product or process integration is documented with control points.
- Model behavior in production is logged; quality, latency, cost and failure monitoring are configured.
- Security, rights, industry restrictions and ethics requirements are verified before launch.
- Fallback scenarios and manual escalation are defined for errors, low confidence and model unavailability.
- AI risks are assessed alongside financial, operational and legal process risks.
- Product owner, subject matter expert, data scientist, ML/AI engineer and security roles are clearly separated.
- AI is embedded in the operational workflow and process rules, not a separate demonstration.
- Before scaling, the hypothesis is validated in a limited scope with predefined acceptance criteria.
- Feedback mechanisms, model updates and retesting after data or prompt changes are in place.
Assess where AI can deliver impact in your process
Next step
Explore results by process
Select an AI scenario with a clear owner and data. We'll match your score to process risk and identify the minimum changes needed before piloting or scaling.
- metric and acceptable error
- data and access systems
- pilot criteria
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