AI maturity is not a contest to deploy the most tools. It is the ability to turn useful AI capability into reliable work. A company may have widespread chat usage and still lack a safe route into its systems. Another may have strong integrations but no ownership for the agents running them.
This model separates those realities. The phases are not a ranking of companies. They are a way to ask what people can do, what systems AI can reach, what controls exist, and what the organisation must build before the next step is dependable.
ai maturity model
1. Awareness. The company is working out what AI means for its business. People are collecting examples, asking where value might exist, and trying to separate useful opportunities from noise. Advancing takes shared language, a small set of real workflows to examine, and an owner who can move one experiment forward.
2. AI Chat. People get value from ChatGPT, Claude, Gemini, or Copilot, but work still lives in prompts, pasted context, and personal habits. Advancing takes team patterns, safe handling guidance, and a workflow where AI needs company context rather than another clever prompt.
3. Integrated AI. AI starts to touch company knowledge or tools. The organisation is moving from helpful answers toward actions, but access and responsibility need definition. Advancing takes the right system connections, permission boundaries, and a first action path with a human review.
4. Read/Write Agents. Agents can act in business systems. They may read records, prepare changes, or write behind an approval step. Advancing takes a repeatable skill, clear ownership, logs, approvals, and a fallback when the agent is uncertain or a system is unavailable.
5. Cloud Agents With Skills & Integrations. Agents are becoming part of how work runs across teams. The hard problem shifts from model capability to reliability, monitoring, versioning, and adoption. Advancing takes observable runs, maintained skills, failure review, and a pattern that can move from one team to the next.
6. Agentic Products & MCP Layer. AI capability becomes something other people or other agents can use through products, APIs, MCP servers, or agentic workflows. Advancing takes decisions about external access, identity, permissions, interfaces, support, and the product the capability should become.
The free AI self-assessment uses these six phases and also scores the dimensions that can hold progress back: tools, usage, shared vocabulary, system reach, access, governance, and runtime. Read the AI readiness assessment for the company-level starting point, or use the framework to structure a deeper conversation about one workflow.
