What Is an AI Maturity Model? Stages, Examples and How to Assess Yours
An AI maturity model shows how far a business has come with AI. See the common stages, MIT CISR's four-stage model and how to assess your own.
Last Updated: September 2026
An AI maturity model is a framework that describes how far a business has progressed in using artificial intelligence. It sets out a series of stages, from early experiments to AI built into how the business works. Each stage has its own signs, from the skills in place to the systems in use.
Its purpose is practical. A maturity model helps a business see where it stands today, what the next stage looks like and which gaps are holding it back. This guide covers the common stages, a research-based example from MIT, and how to run your own AI maturity assessment.
Key Takeaways
- An AI maturity model describes stages of AI progress, from first experiments to AI embedded across the business.
- MIT CISR's Enterprise AI Maturity Model has four stages. In its research, 28% of companies were in stage 1 and only 7% had reached stage 4.
- MIT CISR found companies in the first two stages had financial performance below industry average, while those in stages three and four were well above it.
- Maturity is usually measured across several dimensions, such as strategy, data, technology, people and governance.
- AI maturity is different from AI readiness. Readiness asks whether you can start; maturity asks how far you have come.
What Is an AI Maturity Model?
An AI maturity model is a structured way to describe a company's progress with AI. Each stage has typical signs, such as how AI projects are chosen, how data is managed and who is responsible for results.
Most maturity models share the same logic. Early stages involve isolated experiments and pilots. Later stages involve AI running reliably inside everyday processes, with the data, skills and governance to support it.
A maturity model is not a scorecard to win. It gives people a shared way to describe where the business actually is, and its value is in showing the next realistic step. A business in its first stage gains little by copying what the most advanced companies do.
What Are the Stages of AI Maturity?
Maturity models differ in how many stages they use and what they call them. A useful, research-based example is the Enterprise AI Maturity Model from the MIT Center for Information Systems Research (CISR).
MIT CISR's research, released in December 2024, is based on a survey of 721 companies and interviews with executives. It places companies in four stages:
The "what it typically looks like" column is a plain-language summary, not MIT's wording. The stage names and percentages come from the research itself.
What Does the Research Say About AI Maturity and Performance?
MIT CISR's findings link maturity with financial results. According to the release, most companies researched were in the first two stages and had "financial performance below industry average." Companies in stages three and four "had financial performance well above industry average."
That is a correlation, not proof that AI caused the difference. More successful companies may also invest more in AI. Still, it suggests that value tends to show up once AI moves beyond pilots and into how work gets done.
The move from pilots to scaled use is also where many efforts stall. Gartner predicted in 2024 that at least 30% of generative AI projects would be abandoned after proof of concept by the end of 2025. It pointed to "poor data quality, inadequate risk controls, escalating costs or unclear business value."
What Dimensions Does an AI Maturity Model Measure?
Most AI maturity assessments look at several dimensions, because progress is rarely even. A business can have strong technology and weak governance, or a clear strategy but poor data.
Scoring each dimension separately shows where the real constraint is. Improving the weakest dimension usually does more than pushing the strongest one further.
How Do You Run an AI Maturity Assessment?
An AI maturity assessment measures where your business sits on a maturity model. It can be done internally, with a few structured steps.
- Choose the dimensions. Use a set like the one above, adapted to your business.
- Write stage descriptions. For each dimension, describe what each stage looks like in plain terms.
- Gather evidence, not opinions. Look at real projects, policies, data sources and results.
- Involve several functions. Include leadership, IT, operations and the teams using AI day to day.
- Score each dimension. Place each one at a stage, and note the evidence behind the score.
- Pick the weakest link. Choose one or two dimensions to improve before the next review.
Write the score and the evidence in one short document. It becomes the record you compare against at the next review.
Repeat the assessment on a schedule, such as once a year, using the same descriptions. That turns a one-off snapshot into a record of progress.
If your business has not yet started with AI, a maturity assessment may be premature. In that case, it is better to score your own readiness first.
What Is the Difference Between AI Maturity and AI Readiness?
AI readiness and AI maturity are related, but they answer different questions. Readiness asks whether a business has the conditions to start using AI well. Maturity asks how far it has progressed once it has started.
A readiness review looks at foundations such as data quality, systems, skills and leadership support. Our guide on how to assess your company's AI readiness and the AI readiness checklist cover that stage.
A maturity model picks up where readiness leaves off. It tracks how AI moves from first pilots to scaled, governed use across the business.
Some businesses use an AI readiness framework for the first question and a maturity model for the second. Used together, they show both whether you can start and how far you have come.
How Do Governance Frameworks Fit Into AI Maturity?
Governance is one of the dimensions that separates early and mature businesses. Two widely used frameworks can serve as benchmarks for it, although neither is a maturity model itself.
The US National Institute of Standards and Technology published the AI Risk Management Framework in January 2023, for voluntary use. It organizes AI risk management into four functions: Govern, Map, Measure and Manage.
ISO/IEC 42001, published in December 2023, is an international standard for AI management systems. It "specifies requirements for establishing, implementing, maintaining, and continually improving an Artificial Intelligence Management System." Businesses can be certified against it.
How Do You Move Up a Stage of AI Maturity?
Moving up a stage usually depends on fixing the constraint that stopped the last one. The focus changes as maturity grows.
- From experiments to pilots. Choose a few use cases with a clear, measurable business outcome, and get the basic data in order.
- From pilots to scaled use. Turn what worked into standard ways of building and running AI, with shared platforms and clear ownership.
- From scaled use to AI built into the business. Link AI to strategy, products and processes, with governance and skills spread across teams.
- From one team to several. What worked in one department rarely transfers without the same data, training and support.
Move one stage at a time. Trying to skip a stage usually means the foundations from the missed one are not there.
The hardest step for many businesses is moving from pilots to scaled use. Gartner's list of reasons projects get abandoned is a useful checklist: data quality, risk controls, cost and clear business value.
What Mistakes Do Companies Make With AI Maturity Models?
The most common mistakes come from treating the model as a label rather than a tool.
- Scoring on opinion. Self-assessment without evidence tends to flatter the result.
- Measuring tools instead of outcomes. Owning AI software is not the same as getting value from it.
- Skipping governance. Scaling AI without policies and review creates risk that slows everything later.
- Assessing once. A single score shows a position, not progress.
- Chasing the top stage. The goal is the next useful step, not the highest label.
- Copying another company's roadmap. Their constraint is rarely the same as yours.
Frequently Asked Questions
What is an AI maturity model?
It is a framework that describes stages of progress in using AI, from early experiments to AI built into how the business works. It helps a business see where it stands and what to improve next.
What are the stages of AI maturity?
It depends on the model. MIT CISR's Enterprise AI Maturity Model uses four: Experiment and Prepare, Build Pilots and Capabilities, Develop AI Ways of Working, and Become AI Future Ready.
What is an AI maturity assessment?
It is a structured review that places a business on a maturity model. It usually scores dimensions such as strategy, data, technology, people and governance, based on evidence rather than opinion.
What is the difference between AI maturity and AI readiness?
Readiness asks whether a business has the conditions to start using AI. Maturity asks how far it has progressed once it has started, from pilots to scaled use.
Is there a standard AI maturity model?
There is no single official one. MIT CISR's model is based on research with 721 companies. Standards such as ISO/IEC 42001 cover AI management systems, and NIST's AI RMF covers risk management, but neither is a maturity model.
What did MIT CISR find about AI maturity and financial performance?
Enterprises in the first two stages had financial performance below their industry average. Those in stages three and four had financial performance well above it.
Who should take part in an AI maturity assessment?
Include people from leadership, IT, data, operations and the teams that use AI each day. Each group sees a different part of the picture, and scores are more honest when no single team marks its own work.
How often should you assess AI maturity?
Once a year is a common rhythm, or after a major change in strategy or technology. Using the same descriptions each time lets you compare results.
What does a mature AI business look like?
AI is tied to business goals, runs on well-managed data and shared platforms, and is governed by clear policies. Results are measured against business outcomes, not activity.
Executive Summary
An AI maturity model describes how far a business has progressed with AI, from early experiments to AI built into the business. MIT CISR's four-stage model is a well-researched example. In its survey of 721 companies, only 7% had reached the most advanced stage.
The research links maturity to results. Enterprises in the first two stages had financial performance below industry average, while those in stages three and four were well above it. The step from pilots to scaled use is where many efforts stall.
A useful AI maturity assessment scores several dimensions on evidence, finds the weakest one and repeats on a schedule. It is different from a readiness review, which checks whether the conditions to start are in place.
What Should You Do Next?
List your current AI projects and place each on one of the four MIT CISR stages. Then score your data, governance and skills honestly, and pick the weakest one to work on first.
If you are not sure you are ready to start, begin with a readiness review before a maturity assessment.
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About the Author
Jerry Jariwalla is the founder of AiBuildrs and creator of the Growth Signal Intelligence framework. With over 22 years in digital marketing and multiple successful business exits, Jerry has spent the past decade leading AI implementation programs for mid-market businesses across professional services, recruitment, membership organizations, and traditional industries.
Expertise: AI Strategy, AI Implementation, Workflow Automation, Custom AI Development, Voice AI, Offshore Engineering, B2B Sales Intelligence, Mid-Market AI Adoption
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Disclaimer: This content is for informational purposes only and does not constitute professional business or technology advice. ROI outcomes vary based on industry, existing systems, and implementation commitment. Contact AiBuildrs for a consultation regarding your specific situation.
Jerry Jariwalla
Founder & CEO of AiBuildrs. Builds custom AI infrastructure for professional service businesses — voice AI, sales automation, bespoke systems, and Answer Engine Optimization via the company's flagship product, The AEO Engine. View Jerry Jariwalla's Full Profile →