Section 7
AI4EA Readiness Model
Assess Data, Technology & Platform, Governance & Risk and People & Culture across five levels.
Before scaling AI, the enterprise should determine whether the foundational capabilities required by its targeted use cases exist. AI4EA evaluates readiness across Data, Technology & Platform, Governance & Risk, and People & Culture.
| Dimension | L1 Initial | L2 Emerging | L3 Defined | L4 Scaled | L5 Adaptive |
|---|---|---|---|---|---|
| Data | Fragmented | Locally accessible | Governed enterprise data | Reusable knowledge services | Continuously optimized |
| Technology & Platform | Ad hoc tools | Initial platforms | Standard AI platform | Shared and scalable services | Dynamic optimization |
| Governance & Risk | Reactive | Basic policies | Defined controls | Integrated / automated governance | Continuous assurance |
| People & Culture | Limited awareness | Initial skills | Defined roles & training | Broad adoption | AI-native culture |
| Dimension | Assessment scope |
|---|---|
| Data | Accessibility, quality, ownership, metadata, lineage, classification, privacy, knowledge availability and access control. |
| Technology & Platform | Cloud and infrastructure, AI platforms, compute, model access, integration, deployment automation, observability, scalability, resilience and cost management. |
| Governance & Risk | AI governance, cybersecurity, privacy, responsible AI, model risk, architecture governance, regulatory requirements, auditability and accountability. |
| People & Culture | AI literacy, architecture and engineering skills, leadership awareness, change readiness, defined roles and organizational adoption. |
7.5 Readiness Levels
AI4EA defines five levels: Initial, Emerging, Defined, Scaled, Adaptive. The required target is use-case dependent; Level 5 is not a prerequisite for adoption. The assessment identifies the gap between current capability and the level required to execute the prioritized portfolio safely and sustainably.
Gap disposition (Appendix A)
For each readiness dimension, determine the current level, determine the level required by priority use cases, identify the capability gap, assess the impact, define remediation actions, and assign ownership and target timing.
| Gap Disposition | Meaning |
|---|---|
| Resolve before pilot | The gap prevents safe or meaningful experimentation. |
| Resolve during pilot | The gap can be addressed as part of controlled validation. |
| Resolve before scale | The pilot may proceed, but production scale requires remediation. |
| Accept | The residual gap is accepted according to enterprise risk appetite and governance. |
Primary outcome
Prioritized AI capability gaps and a readiness improvement roadmap.
Core artifacts