01 · Strategy & Leadership
Leadership has defined where AI should create measurable business value.
AI has a business-owned direction, funding model, and measurable value thesis.
AI maturity light
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02 · Culture & Adoption
Business teams trust the data and analytics used in AI-enabled decisions.
Teams trust data, adopt AI-enabled workflows, and learn from change.
03 · People & Capabilities
Product owners, data owners, engineers, analysts, and risk roles have clear AI responsibilities.
Roles, skills, and capacity exist across business, data, AI, and risk.
04 · Operating Model & Governance
Every critical dataset or AI use case has named business and technical owners.
Decision rights, controls, and ownership are clear from idea to operation.
05 · Delivery & DevOps
Data and AI changes are versioned, reviewed, tested, and deployed through CI/CD.
AI and data products are shipped through reliable, testable delivery pipelines.
06 · Platform & Architecture
There is a reference architecture for AI applications, data products, and integration patterns.
The architecture supports governed data access, automation, and scalable AI patterns.
07 · Data Foundation
Important data products have defined quality tests, owners, and issue handling.
Data quality, lineage, metadata, and contracts make AI inputs trustworthy.
08 · AI, Innovation & Value
The organization maintains a qualified backlog of AI use cases with value and feasibility estimates.
AI experimentation is connected to measurable outcomes and reusable capabilities.
09 · Risk & Compliance
Data and AI use cases are classified by sensitivity, regulatory exposure, and business criticality.
AI risks are managed across privacy, security, ethics, regulation, and resilience.
10 · AI-Ready Documentation & Knowledge
Key documentation (architecture, processes, decisions) is authored in AI-friendly text formats — Markdown, Mermaid, code — rather than PowerPoint, Word or Excel.
Organisational knowledge is captured in machine-readable, version-controlled formats that people and AI tools can both use.
11 · IT Landscape & Data Accessibility
We can programmatically access the data we need across core systems (APIs, exports, a warehouse/lake) without manual extraction.
The internal IT and data estate is both reachable and governable — data is accessible on the organisation's terms, not trapped in SaaS silos.
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