Applying the AI Maturity Model & Framework to Teams
The AI maturity model gives teams a transparent snapshot of where they are today and where to go next. Progress involves culture, process, and governance. As such, a maturity model lets you set goals and map a path forward. Below, we’ll walk you through the key stages and share practical steps that you can use instantly.
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The Gartner AI maturity model outlines how organizations progress from exploration to full transformation. It helps teams measure current AI readiness, set goals, and plan the steps needed to scale AI responsibly.
Understanding the AI Maturity Framework
Most frameworks break maturity into clear pillars like strategy, data, technology, people, and governance, with defined dimensions at each stage. This structure gives teams a shared way to assess progress and plan next steps across the AI maturity model.
Definition and Purpose
An AI maturity model gauges a team’s current AI capability and readiness, then maps out next steps. It reduces false starts by linking goals to data practices, basic process hygiene, and governance. In short, it’s a planning framework that turns ambition into a stepwise roadmap over time. This way, you understand your current level and what needs to change.
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Overview of Maturity Stages
Most models describe levels of AI that move from exploration to enterprise scale. A practical view covers five stages:
Exploration
Experimentation
Systematic
Strategic
Transformational
Early stages are ad hoc and tool-curious. Mid stages add repeatable workflows and shared data. Leading stages tie AI to priorities and outcomes. MIT CISR’s (Massachusetts Institute of Technology Center for Information Systems Research) recent work similarly maps staged growth from experimenting to “future-ready,” demonstrating how maturity increases with broader adoption.
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The Five Stages of AI Maturity
Teams move through a predictable path as they grow in their AI usage. Let’s break down the five stages of the AI maturity curve, highlighting what each stage looks like in practice. You’ll also see where most companies stand today.
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AI maturity is measured by assessing progress across areas like strategy, data practices, technology, talent, and governance. Teams often use staged models—such as exploration, experimentation, systematic, strategic, and transformational—to see where they stand and what’s needed to advance.
Stage 1: Initial Exploration (28% of Organizations)
This early stage is mostly about curiosity. Teams might try out AI tools such as chatbots or writing assistants, but there’s no overarching plan or formal ownership. Projects are ad hoc, and there’s only a tenuous connection to goals. At this point, the focus should be on learning, especially for leadership, and finding small, low-risk areas to explore further.
Stage 2: Experimentation/Developing (34% of Organizations)
Now AI moves into pilots. These may be isolated to a department or workflow, but there’s more structure. Teams begin linking data sources, setting basic metrics, and testing automation. Governance is still light, but interest grows across the company. Coordination begins to take shape as leaders recognize the need to connect efforts.
Stage 3: Systematic/Mature (31% of Organizations)
AI starts to scale here. Teams build coordinated roadmaps and track clear outcomes. Infrastructure improves, and models are deployed with more control. Data becomes easier to access across teams. Some companies form centers of excellence (COEs) to lead the charge. Governance, ethics, and documentation become more routine.
Stage 4: Strategic/Leading (7% of Organizations)
AI is now embedded in business strategy. Instead of using only off-the-shelf tools, companies may create custom models that give them a unique edge. AI efforts are long-term and oriented with innovation planning. This level of maturity is rare, as only about 7% of companies reach it.
Stage 5: Pioneering/Transformational
At this point, AI is part of how the company thinks and operates. It’s used to improve existing work and power new services, models, and ecosystems. These teams think AI-first, using it to shape what’s next rather than just complement what’s already in place.
Critical Dimensions of AI Maturity Development
Progress in AI ultimately depends on how well your team develops across several key areas. These include strategic planning, data practices, tech infrastructure, skills, and governance. Let’s take a look at each.
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Most maturity models focus on four core dimensions: strategy, data, technology, and people. These areas show how well a team connects AI goals to business priorities, manages and uses data, builds scalable systems, and develops talent to support adoption.
Strategy and Approach
To move forward, AI work should be tied to your company’s goals. Define what success looks like, and design a roadmap to get there. Teams also need leadership that’s ready to guide adoption responsibly, not just technically.
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A maturity model works by mapping where a team is today against defined stages of growth. It highlights strengths, exposes gaps in areas like data or governance, and provides a step-by-step path to advance AI adoption.
Data Readiness
AI needs clean, connected, and accessible data to work well. That means addressing silos and making sure data governance is in place. Mature companies treat data as something to be shared and built upon.
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Technology Infrastructure
A stable AI foundation includes scalable systems, secure data pipelines, and well-coordinated tools. It also means choosing the right platforms for model deployment and monitoring. Early-stage teams often encounter trouble connecting older systems with newer AI models.
Eyeful’s Expert Advice:
“Teams should always include expert human gatekeepers for AI innovation projects. AI should be used to compliment ways to scale the creation of useful, meaningful content and workflows. But human experts should provide governance for the final output.”
Talent and Expertise
AI maturity requires growth in both technical and non-technical roles. Yes, you want to hire data scientists, but you’ll also want to get your entire team caught up. Encourage collaboration between technical experts and subject matter specialists. Internal training goes a long way to make AI more approachable for everyone.
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Governance and Ethics
Responsible AI work begins with clarity. Create early guidelines for how decisions are made, and make sure documentation and review are part of the process. Even in early pilot phases, put risk management tools in place and build with auditability in mind.
Where to Go from Here
The AI maturity curve shows that teams grow step by step, not all at once. Are you just starting to explore or already scaling across departments? Progress comes from building in the right areas: data, tech, skills, and clear direction. Use this model as a guide to track where you are and what’s next.
Key Takeaways:
The AI maturity model ensures teams understand their current AI readiness and plan future growth.
Maturity is technical, but it also includes strategy, leadership, and ethics.
Most teams today fall somewhere between exploration and systematic use.
Strong data practices and infrastructure are key to moving beyond early stages.
Governance and education play a critical role throughout the journey.
Action Items:
Identify your current stage using the five-level AI maturity curve.
Prioritize small pilots tied to business goals if you’re in early stages.
Evaluate your data quality, accessibility, and team-wide visibility.
Invest in cross-functional training to build shared AI literacy.
Document how decisions are made and introduce oversight early on.
Ready to identify your AI maturity level and take action? Let’s talk about your next step.