Enterprise AI Maturity Model

Many enterprises are eager to adopt generative AI, but they struggle to integrate AI into the core of their operations and innovation beyond simple productivity gains. In particular, regulated industries such as finance or healthcare face greater barriers to rapidly advancing AI projects due to security and data management issues.

AI adoption generally follows the 5-stage maturity model below.

• Stage 1: Experimentation - A stage where individual teams or individuals explore AI tools and conduct PoCs (proofs of concept) in isolation
• Stage 2: Tool adoption - A stage where specific AI workflows are introduced and momentum is gained within the organization
• Stage 3: Internal platforms - A Production stage where centralized AI infrastructure is built to secure governance and enable scaling
• Stage 4: Strategic integrations - A stage where AI is integrated as an essential element of core products and operational systems
• Stage 5: AI-native transformation - A stage where the enterprise's workforce and culture are redesigned around AI capabilities

Most enterprises tend to stall between Stage 2 (Tool adoption) and Stage 3 (Internal platforms). In particular, Shadow AI (uncontrolled AI use) occurring at Stage 1 can lead to security risks such as data exposure, making the transition from the experimentation stage to systematic infrastructure a key challenge.