What it means
AI maturity describes how advanced an organisation's capabilities are for using AI effectively and responsibly, across areas such as strategy, governance, data, technology, skills and culture. Maturity models describe typical stages, from early experiments to AI embedded in core processes.
Real-world example
A federal agency assesses its AI maturity and finds an uneven picture: strong data protection and IT security, several successful pilots, but no AI strategy, no inventory of AI tools in use, and data spread across incompatible systems. It decides to focus the next year on governance and data foundations rather than new pilots.
Why it matters for AI adoption
Maturity assessments help organisations prioritise investments and set realistic ambitions. They are most useful as a structured conversation about gaps and next steps, not as a score to compare against other organisations.
Questions to ask
- Where are our strongest and weakest areas for AI adoption, and what evidence supports that?
- Which gaps most limit our next steps?