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Companies with advanced artificial intelligence capabilities — those most effectively using AI to improve operations and customer experience, and to support and develop their ecosystems — outperform their industry peers financially, according to research from the MIT Center for Information Systems Research.
These organizations are more “AI mature,” according to CISR researchers and who mapped four stages of AI enterprise maturity in a new research briefing. The researchers found that organizations in the first two stages had financial performance below their industry’s average, and organizations in the last two stages performed above their industry’s average.
“Enterprises can use the MIT CISR Enterprise AI Maturity Model to assess their current capabilities, identify gaps, and create a road map for improvement across various dimensions, such as processes, technology, and organizational culture,” said Woerner, who is the director of MIT CISR. “It’s a valuable tool for guiding business growth, improving operational efficiency, and achieving strategic objectives through a clear, step-by-step approach.”
Identifying four stages of AI maturity
The findings are based on a 2022 MIT CISR survey of 721 companies, followed by interviews in 2024 with executives at nine enterprises about traditional and generative AI and their early thoughts on agentic and robotic AI.
Instead of considering edge cases, organizations should think about which of the four stages describes most of their AI activity, Weill said at the EmTech MIT conference last fall.
Organizations need to build cumulative capabilities and lessons from AI as they move toward a future-ready state of AI use. Here’s a look at the four stages of enterprise AI maturity and the capabilities organizations need as they navigate them.
Stage 1: Experiment and prepare
Organizations in the first stage work to educate their workforce, formulate AI policies, become more evidence-based, and experiment with AI technologies to become more comfortable with automated decision-making. According to the CISR survey, 28% of enterprises were in this stage.
During Stage 1, organizations begin to discuss where humans need to be in the loop for oversight and what they consider to be acceptable and ethical uses of AI technology.
Companies at this stage focus on AI literacy initiatives for the board and top management teams, and skill-building for the rest of the enterprise. They also begin to identify value-creation opportunities from AI and the capabilities and competencies required to realize them.
“This first stage is all about experimenting, preparing, and education,” Weill said.
Stage 2: Build pilots and capabilities
Companies at this stage focus on AI pilots that create value for both the enterprise and its workers. In the survey, 34% of organizations were in this stage.
Important parts of Stage 2 include defining important metrics, beginning to simplify and automate business processes, and developing the needed enterprise capabilities identified during Stage 1. Here, organizations 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 lessons learned from the pilots.
“The hardest part of Stage 2 is changing,” Weill said. “How do we move from a command-and-control culture to a coach-and-communicate culture? AI has a lot to do with not only automating but enabling our front line to make decisions for us and enabling our customers to self-serve. You can’t do that if you command and control.”
Determining how to consolidate organizational data silos and safely and securely prepare data for use with AI is critical at this stage. This process can require significant investment in or refinement of application performance interfaces that link data and technologies, according to the researchers.
Stage 3: Industrialize AI throughout the enterprise
Moving to this stage is a significant step in an organization’s AI journey, both for the potential impact on its enterprise growth and profitability and the platform capabilities needed to achieve the scale required, the research said. Thirty-one percent of organizations reported being at this stage.
Stage 3 includes building a scalable enterprise architecture, making data and outcomes transparent via business dashboards, developing a pervasive test-and-learn culture, and expanding business process automation efforts.
“You have to simplify and automate your processes. If you try to use AI on an incredibly complicated … process, it’ll be much harder,” Weill said.
Additionally, organizations at this stage make significant use of foundation models and small language models that are trained to perform certain tasks, the researchers write. Organizations then take these models and apply them to their own data to create and capture new value on secure platforms.
“Companies in the third stage are developing proprietary models, and that leads you to the holy trinity of AI — architecture, reuse, and agents,” Weill said. “Those are the really hard parts of Stage 3.”
Stage 4: Become “AI future-ready”
In this final stage, organizations are considered AI future-ready, which was true of 7% of enterprises in the study. Here, AI is embedded in all decision-making, and organizations are using proprietary AI internally. They then sell new business services based on that capability, the AI capability as a service, or both to other enterprises.
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“This is where you’re all in for AI-enabled decision-making [and] deciding when you need people in the loop and when you don’t,” Weill said. “You’ll develop proprietary AI, and you’ll sell new services around it.”
The executives who were interviewed said they expect that the most value from AI will be created from combining people and platforms with four types of AI: analytical, generative, agentic, and robotic.
Determining your next steps
Organizations that want to benefit from CISR’s Enterprise AI Maturity Model can start by identifying which stage of AI maturity they’ve reached, the researchers said.
“We recommend bringing a team of senior technical and data leaders together to assess which of the four stages your enterprise is in today, and your aspirations and time frames regarding your enterprise’s use of AI,” Woerner said. “Then, discuss which enterprise capabilities and skill sets need more work. No matter where you are [with AI maturity], be bold.”
Read the research brief
