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This content is restricted to the personal use of Andrea Parks ([email protected]).AI Maturity Assessment
8 June 2026 - ID G00854046 - 8 min read
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Written for:AI Leaders
Technology leaders can use our online tool to gauge their maturity across seven main AI capability categories: strategy, value, organization, people and culture, governance, engineering, and data. Take this assessment to establish your current maturity, find priority gaps and receive recommended next steps.
Overview
The AI maturity assessment is an essential tool for organizations looking to systematically assess and enhance their AI capabilities.
It is particularly useful when an organization wants to:
- Rationalize its AI ambition in line with its current readiness
- Establish a clear AI roadmap for development
- Measure progress as it executes against this AI roadmap
- Benchmark its progress in each of the pillars against its peers
This assessment also provides a framework for continuous improvement, enabling organizations to adapt to evolving technologies and market demands, thereby maintaining a competitive edge.
We recommend organizations conduct this AI maturity assessment annually.
About the Tool and Its Results
Gartner’s AI Maturity Assessment provides a structured framework for evaluating and enhancing an organization’s capabilities in using AI.
This framework (see Figure 1), also known as the Gartner AI maturity model, is based on enterprise AI capabilities, which are arranged across two tiers in this online tool:
- Tier 1 shows the seven primary capabilities (see Table 1).
- Tier 2 further details these at a more granular level (see Table 2).
The framework and its capabilities are relevant to all sectors, business areas and AI technologies. In other words, they apply to all public and private sectors, all business functions and to all forms of AI, such as foundational AI (including data science and machine learning), generative AI, and agentic AI.
This tiering structure helps enterprises examine the maturity of capabilities at various levels of abstraction and depth.
Together, these dimensions provide a comprehensive view of an organization’s AI maturity, which can guide strategic improvements and drive competitive advantage.
Table 1: Tier 1 AI Capabilities
Tier 1 AI capability | Description | Topics covered |
AI strategy | Develop and execute the AI strategy: The development and implementation of an AI strategy that is aligned with the organization’s business strategy and its AI ambition | Trends, ambition, strategy, roadmap, alignment, innovation, transformation |
AI value | Manage the AI portfolio and use cases: The systematic identification, prioritization and management of AI initiatives | Use cases, products, portfolio, prioritization, feasibility, business case, benefits and costs, metrics |
AI organization | Evolve the AI operating model: The integration and collaboration within the AI ecosystems, including internal teams and external partnerships | Operating model, center of excellence, teams, partners, alliances, ecosystem |
AI people and culture | Foster people and culture for AI: The organization’s commitment and ability to foster AI literacy, AI skills and a culture of innovation | Roles, workforce, staffing, skills, literacy, training, culture, change, organizational readiness |
AI governance | Govern AI and manage risks: The establishment and enforcement of governance policies and standards to ensure ethical and compliant AI practices | Risks, trust, security, safety, ethics, regulations, compliance, policies, guidelines, monitoring |
AI engineering | Engineer AI systems and platforms: The robustness and efficiency of AI development and deployment processes | Design, architecture, buy/blend/build, test, embedding, deployment, operations, infrastructure, platforms, frameworks, methodologies, collaboration |
AI data | Prepare and manage data for AI: The quality, accessibility and utilization of data for AI applications | Data acquisition, preparation, quality, integration, semantics, data platform, privacy, multimodal data |
Source: Gartner (June 2026)
Tier 2 subcategories provide more detail and a grouping of AI capabilities (see Table 2).
Table 2: Tier 2 AI Capabilities
| Tier 2 AI capability | Description |
|---|---|
AI strategy | |
Monitor and interpret AI trends | Run a trend-scanning program and analyze the impact of trends on the organization. |
Develop and rationalize the AI vision | Set the AI vision and ambition, combined with assessing and monitoring the organizational readiness for AI. |
Develop and refine the AI strategy | Author and align the AI strategy, involving key AI stakeholders. |
Develop and coordinate the AI roadmap | Develop and execute an AI roadmap with short-term and long-term milestones, coordinated with stakeholders. |
Promote AI-driven innovation | Foster a culture of innovation by identifying and scaling ideas and experiments. |
AI value | |
Manage the AI use-case portfolio | Assess and prioritize AI use cases, and align the AI use-case portfolio with business objectives and AI capabilities. |
Manage AI value propositions | Manage AI value propositions, develop business cases, measure value realization and monitor AI portfolio performance. |
Develop the AI product portfolio | Identify and develop AI product opportunities, manage AI product portfolio, stakeholder engagement, communication and funding. |
AI organization | |
Develop external AI partnerships | Develop, foster and monitor external partnerships and alliances for AI. |
Manage the AI ecosystem | Strategize and balance insourcing and outsourcing for AI capabilities, while managing continuity risks and dependencies across the AI ecosystem. |
Evolve the internal operating model | Create, maintain and monitor the internal AI organization. |
AI people and culture | |
Manage AI-related change and culture | Foster, communicate and promote a culture and practices that are ready for AI adoption and change. |
Evolve AI-related roles and staffing | Identify and develop AI roles and manage workforce planning, staffing and recruitment. |
Deliver AI training and literacy | Implement and monitor AI education, upskilling and knowledge sharing. |
AI governance | |
Evolve AI policies and controls | Create, maintain and monitor AI policies and controls for compliance, ethical, security, safety, sustainability and other risks. |
Develop AI governance teams and roles | Establish and evolve the AI governance operating model, including governance organization, processes, responsibilities and decision rights. |
Monitor AI and enforce policies | Perform AI governance, oversight and assurance. |
AI engineering | |
Design and architect AI | Establish, apply and optimize AI system and integration architectures, as well as practices for design and engineering. |
Develop, test and integrate AI | Select, build, ground, contextualize, train, validate and embed AI models or agents or applications. |
Deploy, operate and scale AI | Bring AI models or agents or applications to production, monitor and optimize their security, impact, risks, value and performance, and scale where needed. |
Manage AI platform and infrastructure | Develop, promote and monitor the use of an AI platform and infrastructure to facilitate, accelerate and scale AI initiatives. |
Manage AI engineering | Initiate, facilitate and coordinate staffing and activities for AI engineering across teams and delivery projects. |
AI data | |
Acquire and prepare AI data | Source, transfer, store, integrate, transform and validate data for AI engineering and operations. |
Manage and govern AI data | Enable the management and ensure the governance of data for AI. |
Analyze AI data | Explore, visualize and analyze data to enhance AI training or improve AI-driven insights. |
Source: Gartner (June 2026)
Contributors
Var Shankar
Directions for Use
To run the AI Maturity Assessment, click the “Launch” button at the top of this page and begin filling out the form. The assessment consists of a series of statements about AI capabilities. For each statement, you will need to select three things:
- Current maturity: The current maturity level ranges from one to five (see Table 3). This should reflect only where the organization is at the moment, independent of upcoming work that might increase maturity in the future.
- Target maturity: The target maturity level should range from one to five (see Table 3) and be anchored to a time horizon relevant to your AI program. We recommend setting targets for a one-year horizon and repeating this exercise annually. Higher target maturities should be reserved for the highest priority topics, and most organizations do not need to aim for a Level 5 maturity.
- Importance: The importance indicates to what extent the capability is relevant to the organization’s adoption of AI.
Table 3: Maturity Assessment Levels (see Tables 1 and 2 above for capability descriptions)
| Level number | AI capability | Guidance |
|---|---|---|
1 | Does not exist (meets description <10%) | Our organization meets the capability description for less than 10% |
2 | Partially exists (meets description >=10%, <100%) | Our organization meets the capability description for more than 10%, but not completely |
3 | Fully exists (meets description 100%) | Our organization fully meets the capability description |
4 | Fully exists and optimized (meets description >100%) | Our organization optimally meets the capability description |
5 | Fully exists and adaptive (meets description >100% continuously >1 year) | Our organization optimally meets the capability description, continuously adapting it for more than one year |
Source: Gartner (June 2026)
This assessment takes about 20 minutes to complete. After completing the form, click the “Submit and View Results” button. This will generate your tailored report. This report also contains an averaged overview of all capabilities’ maturity level, with Table 4 as guidance.
Table 4: Overall AI Maturity Levels
| Level number | Overall AI maturity (across AI capabilities) |
|---|---|
1 | Planning: The organization considers the use of AI |
2 | Experimenting: The organization conducts AI pilot and proof-of-concept projects |
3 | Stabilization: The organization has several AI solutions in production |
4 | Scaling: AI is applied across the organization |
5 | Leading: The organization fully leverages and depends on AI |
Source: Gartner (June 2026)
What You Get
Summary of Results
Receive an in-depth report that provides:
- A heat map with your current maturity and target maturity level for each capability
- A visualization of the largest gaps between current and target maturity that you should consider for improvement
- A series of recommendations and resources to address each gap
View and Download Presentation Slides
Related and Connected Content
This maturity assessment also has a partner AI roadmap document titled Toolkit: Build Your Organization’s AI Roadmap. This roadmap uses the exact same capabilities tiering and approach as the framework here, making it much easier for clients to use these resources together.
Personal Walk-Through
Save time interpreting the report with a results walk-through and action-planning guidance from a service partner, as available per your service.
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Document Revision History
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