Definition
An AI maturity model is a staged framework that describes how far an organization has progressed in adopting artificial intelligence. It scores the organization against a set of dimensions, such as strategy, data and governance, and places it on a scale that usually runs from early experimentation to AI built into daily operations.
There isn’t one AI maturity model. The term covers a family of frameworks published by analyst firms, universities, nonprofits and consultancies. Three are cited most often: the Gartner AI Maturity Model, the MIT CISR Enterprise AI Maturity Model, and the MITRE AI Maturity Model. They differ in the number of stages and in what they measure, but they share a purpose. Each one gives leaders a way to answer “where are we now?” and “what do we need to build next?”
Most of these models descend from the Capability Maturity Model, which Carnegie Mellon University’s Software Engineering Institute developed to rate software development processes. MITRE states that its own model draws on the Capability Maturity Model Integration (CMMI) appraisal process and on a review of commercial AI maturity models.
See also: Technology Adoption Model (TAM), Diffusion of Innovations (DOI), AI Governance Board (AIGB), AI Capability Framework
Why it matters for marketing
Marketing teams are often among the first in a company to use generative AI, usually for copy drafts, image variations and campaign summaries. Early use like that is uneven. One content team may have a governed workflow with brand guidelines loaded into its tools, while the paid media team down the hall is pasting customer data into a free chatbot.
An AI maturity model gives a marketing leader a way to describe that unevenness in terms the rest of the business recognizes. It also separates activity from capability. A team that has run 15 pilots but has no AI use case in production, no owner for any of them and no measurement plan is still at an early stage, whatever the pilot count suggests.
The models matter for budget conversations too. Enterprise frameworks from Gartner and MIT CISR are the ones a CIO or CFO is likely to be using. When marketing can place itself on the same scale, requests for data infrastructure, training or headcount are easier to connect to the company’s broader AI plan.
How it works
Every AI maturity model has two parts: a set of stages and a set of dimensions.
Stages describe the overall position. The Gartner AI Maturity Model uses five levels:
- Awareness. AI is being discussed, but there are no pilots or experiments.
- Active. AI appears in proofs of concept and pilot projects.
- Operational. At least one AI project is in production, with defined ownership.
- Systemic. AI is considered in every new digital project and is embedded in products and services.
- Transformational. AI shapes decision making, the operating model and competitive position.
The MIT CISR Enterprise AI Maturity Model uses four stages, based on a 2022 survey of 721 companies: Experiment and Prepare, Build Pilots and Capabilities, Develop AI Ways of Working, and Become AI Future Ready. Its distinguishing claim is a link to financial results. Enterprises in the first two stages performed below their industry average, and those in stages 3 and 4 performed well above it.
The MITRE AI Maturity Model uses five assessment levels named Initial, Adopted, Defined, Managed and Optimized. MITRE notes that not every organization needs to reach level 5 on every pillar.
Dimensions are the things being scored. Gartner’s toolkit assesses seven pillars: strategy, data, governance, engineering, operating model, culture, and AI product and value. MITRE uses six pillars broken into 20 dimensions, covering ethical and responsible use, strategy and resources, organization, technology enablers, data, and performance and application.
How to calculate an AI maturity score
There’s no standard formula. Most assessments follow the same basic arithmetic, though:
- Rate each dimension on the model’s scale, typically 1 to 5.
- Average the dimension ratings to get an overall score.
- Map the score to a stage.
Overall maturity score = sum of dimension ratings ÷ number of dimensions
An illustrative example for a marketing department, using seven dimensions on a 5-point scale:
| Dimension | Rating |
|---|---|
| Strategy | 3 |
| Data | 2 |
| Governance | 1 |
| Engineering and tooling | 2 |
| Operating model | 2 |
| Culture and skills | 3 |
| Value measurement | 1 |
| Average | 2.0 |
A 2.0 places this department around the Active level in Gartner’s terms. The individual ratings are more useful than the average. Governance and value measurement, both rated 1, are the gaps to close before any pilot moves into production.
Gartner used a similar approach in a survey of 432 respondents conducted in the fourth quarter of 2024. It rated organizations on seven questions, each scored from 1 to 5. High-maturity organizations averaged 4.2 to 4.5, and low-maturity ones averaged 1.6 to 2.2.
How to utilize an AI maturity model
Baseline assessment. Run the assessment once to establish a starting point, then repeat it on a fixed schedule. MITRE’s guide recommends periodic reassessment to measure progress.
Roadmap planning. Use the lowest-scoring dimensions to set priorities. A marketing team with strong tool adoption but weak data access should fund the data work first.
Budget justification. Tie each funding request to a specific move between stages, such as getting a first use case into production.
Function-level comparison. Score marketing, sales and service separately. Differences between them show where shared resources like a Center of Excellence (CoE) would help.
Vendor and partner selection. A team at the Active level needs different things from a platform than a team at the Systemic level. Maturity stage helps filter out tools that assume capabilities the team doesn’t have yet.
Governance design. Low governance scores are a signal to set up review processes or an AI Governance Board (AIGB) before scaling.
Comparison of AI maturity models
| Model | Publisher | Stages | What it scores | Notable feature |
|---|---|---|---|---|
| Gartner AI Maturity Model | Gartner | 5: Awareness, Active, Operational, Systemic, Transformational | 7 pillars, including strategy, data, governance and engineering | Widely used in IT planning; paired with a self-assessment toolkit |
| MIT CISR Enterprise AI Maturity Model | MIT Center for Information Systems Research | 4: Experiment and Prepare through Become AI Future Ready | Enterprise capabilities needed at each stage | Links stage to financial performance against industry average |
| MITRE AI Maturity Model | MITRE | 5: Initial, Adopted, Defined, Managed, Optimized | 6 pillars, 20 dimensions | Public guide; built on CMMI; includes an ethics pillar |
AI maturity models are also confused with a few neighboring concepts.
| Concept | What it measures | Unit of analysis | When to use it |
|---|---|---|---|
| AI maturity model | Current stage of organizational AI capability | Organization or department | Benchmarking and roadmap planning |
| AI readiness assessment | Whether prerequisites are in place to begin or scale | Organization or project | Before a major investment |
| Technology Adoption Model (TAM) | Whether individuals will accept and use a technology | Individual user | Diagnosing low tool usage |
| Diffusion of Innovations (DOI) | How an innovation spreads through a population | Market or social system | Forecasting adoption over time |
| Experience Maturity | Capability to deliver customer experience | Organization | CX program planning |
The Brand Visibility for Agentic Commerce (BVAC) Maturity Stages are a separate, narrower scale. They rate how visible a brand is to AI shopping agents, not how the organization itself uses AI.
Best practices
Pick one model and stay with it. Scores from different frameworks aren’t comparable. A level 3 in Gartner’s model doesn’t mean the same thing as stage 3 in MIT CISR’s.
Score dimensions with evidence. Ask for an artifact behind each rating: a written policy, a production use case with a named owner, a dashboard. Self-ratings without evidence tend to run high.
Assess at the department level as well as the enterprise level. An enterprise score of 3 can hide a marketing team at 1 and an IT team at 4.
Set a target stage per dimension. Not every dimension needs to reach the top level. MITRE says so directly, and the target should reflect the organization’s mission and resources.
Measure value alongside maturity. In Gartner’s survey, 63% of high-maturity organizations ran financial analysis on risk factors, ROI analysis and customer impact measurement for their AI projects.
Assign leadership. The same survey found that 91% of high-maturity organizations had already appointed dedicated AI leaders.
Don’t treat the stages as a strict sequence. Teams sometimes advance in tooling well before governance catches up. The model should surface that mismatch.
Future trends
Agentic AI is changing the top of the scale. Most models were written when AI meant predictive analytics and, later, generative tools. MIT CISR noted that its 2022 survey data mostly reflects analytical AI, and it added 2024 interviews covering generative AI and early views on agentic systems. Expect revised stage definitions that account for AI agents acting with some autonomy.
The move from pilots to scale is getting more attention. An MIT CISR update found the largest financial impact comes from moving between stage 2 and stage 3. That transition, from pilots to AI ways of working, is where much current research is concentrated.
Function-specific models are multiplying. Consultancies and vendors now publish maturity models for marketing, sales and customer service. Quality varies, and many are lead-generation tools with no published methodology.
Governance is becoming a gating dimension. With regulations such as the EU AI Act taking effect in phases, more frameworks treat governance as a requirement for advancing instead of one score among several.
Trust is being measured directly. Gartner reported that business units trust and are ready to use new AI tools in 57% of high-maturity organizations. In low-maturity organizations the figure was 14%. Future models are likely to score user trust as its own dimension.
Frequently asked questions
What is an AI maturity model in simple terms? It’s a scale that shows how advanced an organization is in using AI. The organization rates itself on several dimensions and lands on a stage, from early experimentation to AI embedded across the business.
What are the five levels of the Gartner AI Maturity Model? Awareness, Active, Operational, Systemic and Transformational.
How many stages does the MIT CISR model have? Four. They are Experiment and Prepare, Build Pilots and Capabilities, Develop AI Ways of Working, and Become AI Future Ready.
Which AI maturity model should a marketing team use? Usually the one the wider company already uses, since a shared scale makes cross-department planning easier. If the company hasn’t chosen one, Gartner’s five levels are the most widely recognized, and MITRE’s guide is publicly available at no cost.
Is an AI maturity model the same as an AI readiness assessment? No. A readiness assessment checks whether the prerequisites for adopting or scaling AI are in place. A maturity model describes the current stage and the stages beyond it. Many organizations use a readiness assessment as the input to a maturity score.
How often should an organization reassess? There’s no fixed rule. Annual reassessment is common, with more frequent checks during periods of heavy investment.
Does higher AI maturity lead to better business results? Research points that way. MIT CISR found that enterprises in its top two stages had financial performance well above industry average. Gartner found that 45% of high-maturity organizations keep AI projects in production for three years or more, against 20% of low-maturity organizations. Both findings are correlations and don’t prove that maturity caused the results.
Can a single department have a different maturity level than the company? Yes. Maturity often varies by function. Scoring departments separately shows where capability is concentrated and where it’s missing.
What are the most common barriers to advancing? Gartner’s survey found data availability and quality near the top for both groups, cited by 34% of leaders at low-maturity organizations and 29% at high-maturity ones. Low-maturity organizations also struggled to find the right use case (37%). For high-maturity organizations, security threats were a top-three barrier for 48%.
Does every organization need to reach the top level? No. MITRE’s guidance says the target level depends on the organization’s mission, resources and business practices.
Related terms
- Technology Adoption Model (TAM)
- Diffusion of Innovations (DOI)
- Crossing the Chasm
- Unified Theory of Acceptance and Use of Technology (UTAUT)
- AI Governance Board (AIGB)
- AI Capability Framework
- Experience Maturity
- Center of Excellence (CoE)
- Proof of Concept (PoC)
- Organizational Change Management (OCM)
Sources
- Gartner. “AI Maturity Model Toolkit.” https://www.gartner.com/en/chief-information-officer/research/ai-maturity-model-toolkit.html
- Gartner. “Gartner Survey Finds 45% of Organizations With High AI Maturity Keep AI Projects Operational for at Least Three Years.” June 30, 2025. https://www.gartner.com/en/newsroom/press-releases/2025-06-30-gartner-survey-finds-forty-five-percent-of-organizations-with-high-artificial-intelligence-maturity-keep-artificial-intelligence-projects-operational-for-at-least-three-years
- Weill, Peter, Stephanie L. Woerner, and Ina M. Sebastian. “Building Enterprise AI Maturity.” MIT CISR Research Briefing, December 2024. https://cisr.mit.edu/publication/2024_1201_EnterpriseAIMaturityModel_WeillWoernerSebastian
- MIT CISR. “New MIT CISR Research Finds Companies with Advanced Enterprise AI Outpace Industry Peers in Financial Performance.” December 19, 2024. https://cisr.mit.edu/content/press-release-enterprise-ai-maturity-121924
- MIT Sloan. “How to boost your organization’s AI maturity level.” https://mitsloan.mit.edu/ideas-made-to-matter/how-to-boost-your-organizations-ai-maturity-level
- MITRE. “The MITRE AI Maturity Model and Organizational Assessment Tool Guide.” https://www.mitre.org/news-insights/publication/mitre-ai-maturity-model-and-organizational-assessment-tool-guide
- MITRE. “The MITRE AI Maturity Model and Organizational Assessment Tool Guide: A Path to Successful AI Adoption” (PDF). https://www.mitre.org/sites/default/files/2023-11/PR-22-1879-MITRE-AI-Maturity-Model-and-Organizational-Assessment-Tool-Guide.pdf
- MITRE. “The MITRE AI Maturity Model.” https://aimaturitymodel.mitre.org/