Enterprise AIMaturity Model
A five-level framework for evaluating enterprise AI readiness. Used in our Advisory Sessions to establish a capability baseline and define the highest-priority actions for advancing the organisation's AI programme.
Aware
Level 1 of 5AI is on the agenda — not yet in the plan.
The organisation understands AI has strategic value and conversations are happening at leadership level. No structured AI initiatives have been launched and no governance or capability investment has been committed.
Characteristic indicators
- No formal AI strategy or roadmap
- Sporadic proof-of-concept experiments driven by individuals, not programme
- No enterprise data governance framework
- AI discussed at board level but not resourced
- Vendor-driven AI conversations rather than business-outcome-driven
- No AI risk framework or responsible AI principles
Priority actions to advance
- Commission an AI readiness assessment to establish a capability baseline
- Appoint an executive AI sponsor with board-level mandate
- Map all AI tools currently in use across the organisation — the shadow AI inventory
- Establish minimum data quality standards for the highest-priority data domains
Experimenting
Level 2 of 5Pilots are producing results. Scaling is unclear.
The organisation has launched controlled AI pilots in specific functions with growing leadership interest. Governance is informal and the path from pilot to production is not yet defined. Individual successes have not yet translated into organisational capability.
Characteristic indicators
- One to three live AI pilots, typically in a single function
- No enterprise AI governance committee or risk framework
- Data quality varies significantly by business unit
- No unified data platform — each pilot uses its own data infrastructure
- AI skills concentrated in a small number of individuals
- No model monitoring or MLOps capability
Priority actions to advance
- Formalise AI governance structure with defined roles and risk appetite
- Establish a model risk management policy covering validation, monitoring and retirement
- Begin building a unified data platform to support more than one AI programme simultaneously
- Inventory all AI vendors and tools — understand the full risk surface
Scaling
Level 3 of 5Value is proven. Governance is catching up.
The organisation has demonstrated AI value across multiple domains and is working to scale successful pilots into production systems. Governance frameworks are being established but are not yet mature enough to manage an expanding AI portfolio with confidence.
Characteristic indicators
- Multiple production AI systems across two or more functions
- An emerging AI governance structure — committee formed but authority limited
- Some investment in data platforms, typically function-specific
- Growing MLOps capability but not yet standardised
- AI risk frameworks in draft — not yet embedded in deployment processes
- Regulatory requirements understood but compliance uncertain in some areas
Priority actions to advance
- Establish an AI Centre of Excellence or formal AI governance board with decision authority
- Formalise data ownership across all critical data domains
- Invest in MLOps infrastructure — model monitoring, drift detection, performance alerting
- Map every production AI system to its applicable regulatory requirements (DPDP, EU AI Act, HIPAA)
Operationalising
Level 4 of 5AI is embedded. Governance is working.
AI is embedded in core business processes and governance is established and functioning. The organisation has a working AI risk management framework, clear data ownership, and is systematically expanding AI capabilities with managed risk. Regulatory compliance is understood and addressed.
Characteristic indicators
- AI embedded in core workflows across multiple business units
- Functioning AI governance board with authority to pause or retire systems
- Data mesh or enterprise data platform in production
- Model monitoring active — alerts configured and responded to
- AI risk appetite defined by leadership and documented
- Regulatory obligations (DPDP, EU AI Act, HIPAA) addressed in deployment processes
Priority actions to advance
- Shift from project-by-project AI governance to enterprise AI portfolio management
- Develop AI transparency commitments — what the organisation will and will not do with AI
- Build AI upskilling programmes across the organisation, not just in technical teams
- Begin publishing responsible AI principles publicly to signal governance maturity to clients and regulators
Leading
Level 5 of 5AI is a differentiator. Governance sets the standard.
AI is a strategic differentiator and the organisation is establishing industry standards for responsible AI adoption. Governance is mature, innovative AI use cases are in continuous development, and the organisation's AI practices are externally recognised by peers, clients and regulators.
Characteristic indicators
- AI strategy integrated with corporate strategy — reviewed at board level quarterly
- Published responsible AI principles with external accountability commitments
- Industry-leading governance — cited by regulators or peers as a reference
- AI talent development programmes embedded in the people strategy
- Participation in industry AI standards bodies or regulatory consultation
- Proprietary AI assets that create durable competitive advantage
Priority actions to advance
- Develop proprietary AI capabilities that are difficult for competitors to replicate
- Publish annual AI transparency reports to build external trust
- Engage with regulatory bodies proactively — shape standards rather than comply with them
- Share governance frameworks with suppliers and clients to elevate the ecosystem
Where does your organisation sit on this model?
Our Advisory Session uses this framework as a diagnostic instrument — producing a scored assessment and a prioritised action plan specific to your context.
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