A new research briefing from the MIT Center for Information Systems Research (CISR) at MIT Sloan School of Management identifies four stages of enterprise AI maturity and finds that financial performance improves with each stage. The findings are based on an MIT CISR survey of 721 companies and 16 interviews with executives at nine enterprises about traditional and generative AI and their early thoughts on agentic and robotic AI. 

Most enterprises researched were in the first two stages of AI maturity and had financial performance below industry average, while enterprises in stages three and four had financial performance well above industry average.

The Four Stages of Enterprise AI Maturity

Stage 1: Experiment and Prepare 

(28% of enterprises in the research)

“In this stage enterprises focus on educating their workforce, formulating AI policies, becoming more evidence-based, and experimenting with AI technologies to grow more comfortable with automated decision-making. They decide on acceptable and ethical use of AI technology and where in the process humans need to provide oversight. Funding targets AI literacy for the board and top management team and skill building on AI technologies integrated into enterprise software for the rest of the enterprise. Enterprises also begin to identify both value creation opportunities from AI and the enterprise capabilities and competencies required to realize them.”

Stage 2: Build Pilots and Capabilities 

(34% of enterprises in the research)

“Enterprises define important metrics, begin to simplify and automate business processes, and develop the enterprise capabilities they’ve learned they will need during stage. They now focus on moving from experiments to systematic innovation by piloting use cases, tracking value created in the pilots, and storytelling both internally and externally about learnings from the pilots. Fundamental to stage 2 is determining how to consolidate organizational data silos and safely and securely serve the data for use with AI; this typically requires a significant investment in, or refinement of, APIs that link the data and the technologies.”

Stage 3: Develop AI Ways of Working 

(31% of enterprises in the research)

“Enterprises focus on industrializing AI throughout the organization. This includes building a scalable enterprise architecture (a platform for AI that allows for scaling and reusing models), making data and outcomes transparent via business dashboards, developing a pervasive test-and-learn culture, and expanding business process automation efforts.”

Stage 4: Become AI Future Ready 

(7% of enterprises in the research)

AI is embedded in all decision-making throughout the enterprise. They leverage proprietary AI internally, and many sell new business services based on this capability, the AI capability as a service, or both to other enterprises.

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Source: MIT CISR