The most common misunderstanding in enterprise AI adoption is wanting to jump from one level straight to the top. The purpose of this framework is to provide a common language for where an organization currently stands and what the next realistic step is.
Level 1 — individual chat usage. Employees use public AI tools at their own discretion. Quick benefits, but unregulated: this is where it's decided whether confidential data leaks out.
Level 2 — company-wide framework. Corporate subscription, data handling policy, basic training. Same usage, but in a transparent and accountable way.
Level 3 — assistant built on proprietary knowledge. The AI works from the company's own documents: policies, product descriptions, past projects. From this point on, the answer isn't generic but company-specific.
Level 4 — integration into processes. The AI doesn't live in a separate window but where the work actually happens: in the customer service system, document management tool, or internal portal.
Level 5 — multi-step workflows. The AI doesn't just answer a single question but carries out a complex task: gathering data, processing it, producing a document — with human approval points along the way.
Level 6 — autonomously working agents. The system itself decides what steps are needed to reach the goal and uses tools to do so. Here, the key question is no longer capability but constraint and controllability.
+1 — the product built with AI. This separate level belongs to organizations that don't just use AI but build with it: prototypes, internal tools, faster development of customer-facing solutions. This level doesn't come after the sixth — it can be reached from any level, and it's typically what brings the most dramatic acceleration.