Many people think businesses fall into two camps when it comes to AI: those just starting out, and those going all in with an AI-first model.
But this binary view misses the bigger picture. The real difference isn’t about using trendy tools. It’s about whether a business is truly ready to turn AI into meaningful results.
The AI maturity curve helps organisations understand not just where they are, but what it takes to grow their AI capabilities in a strategic way.
What is AI maturity?
AI maturity measures how far a company has come in using AI effectively, not just by adopting tools, but by building the right mindset, workflows, and culture to support them.
It’s bigger than just experimenting with artificial intelligence. It’s about using it to make better decisions, improve outcomes, and create long-term value.
In reality, most companies aren’t beginners or AI-first leaders. They’re in the middle: testing ideas, learning from pilots, and adapting their operations.
Knowing where your business sits on the AI maturity curve helps you set realistic goals and plan the next steps toward scalable impact.
The importance of the AI maturity model
AI adoption doesn’t happen instantly. It’s a progression, and the AI maturity curve captures it.
The AI maturity model helps make sense of that journey, mapping how organisations move from early experimentation to integrated, value-driven use of AI.
Some companies stall at the pilot stage, unsure how to scale. Others leap into complex AI projects without building the right foundations, such as high-quality data, cross-functional collaboration, and strong executive alignment.
The maturity model helps avoid pitfalls by clarifying what needs to shift at each level: tools, skills, and leadership commitment.
See also: What AI can and can’t do: Setting realistic expectations for your business
Stages of AI maturity
Think of AI maturity as a five-stage journey.
Each stage builds on the previous one, bringing new priorities, risks, and operational challenges. Understanding where your business stands helps you focus your efforts and avoid common traps.
Stage 1: Ad Hoc
At this stage, AI is more of a buzzword than a real business tool.
Teams may discuss AI in meetings or play with pilot ideas, but there’s no clear plan or alignment on what success looks like.
Typical signs at this stage include:
No formal AI initiatives
Projects driven by hype or vendor pitches
No clear success metrics
Stage 2: Experimental
Your business is trying things out without a long-term plan.
There’s curiosity and early investment, but not much coordination. Projects may show promise, but they’re often disconnected from broader business goals.
Key signs for this stage are:
Pilots in marketing, operations, or IT
Mixed results and unclear ROI
Limited executive oversight
Stage 3: Systematic
AI is no longer isolated in side projects, it’s becoming part of how the business operates.
Efforts are better coordinated, reusable components are emerging, and teams are setting foundations for long-term scalability.
Companies at this stage typically:
Integrate AI into multiple workflows
Invest in internal expertise and knowledge sharing
Begin formal discussions around governance, ethics, and long-term infrastructure
Stage 4: Strategic
AI is a core part of your business decision-making.
It’s not just a tool anymore, but a part of how your company operates. Leadership sees it as central to competitiveness. There’s alignment between technical capabilities and business strategy. Investments in talent, platforms, and data are intentional and sustained.
Signs that your business is at this stage are:
AI tied to KPIs and business outcomes
You have dedicated teams or centres of excellence
You promote cross-functional collaboration on AI initiatives
Stage 5: Pioneering
Your business is shaping new markets, not just improving old ones.
In the final stage, AI is shaping what the business is and can be. Companies here are pushing boundaries, building proprietary models, and contributing to research or regulation. They’ve internalised not just how to use AI, but how to evolve with it.
Key signs of the stage:
Custom AI innovations driving new revenue streams
Deep integration of AI with product development
Active role in setting industry standards
Why knowing your stage matters
The stages of AI maturity exist for a reason: each one demands a different approach.
Strategies that drive results for companies with mature AI foundations can easily misfire when applied too early. What works at Stage 4 may fall flat at Stage 2.
Each phase calls for a different mix of tools, leadership, and mindset.
Understanding where you stand helps you:
Avoid unrealistic goals or overhyped solutions
Prioritise investments that match your current capabilities
Build the right foundations before scaling too fast
That’s why AI maturity isn’t about ambition alone, but also about timing, alignment, and pacing.
As Eric Schmidt (former Google CEO) put it in his TED interview, “AI breakthroughs are happening faster than most people realise, but progress happens in layers. It’s not a switch you flip.”
Conclusion
The companies seeing the real impact from AI aren’t always the ones with the biggest budgets or headcounts.
They’re the ones who understand their current capabilities and build from there.
That’s exactly what the AI maturity curve helps you do: take the next step that fits your business, your goals, and your level of readiness.
How Altamira can help
AI adoption is about making decisions that match your company’s readiness and goals. At Altamira, we help you avoid wasted investment by meeting you where you are on the AI maturity curve.
Whether you’re just exploring pilot projects or looking to scale proven AI systems, our role is to translate complexity into clarity. We work with you to define realistic outcomes, align technical execution with business strategy, and build capabilities that last.
We focus on what actually moves the needle: from early experimentation to strategic transformation.
Contact us to start your AI journey today.
FAQ
Think of it as a way to track how effectively an organisation is using AI.
The AI maturity model breaks down the journey into stages, from early exploration (where companies are just experimenting) to full integration (where AI is embedded in everyday operations). It’s not just about how much AI you’re using, but how well it supports your goals.
While the exact names vary by framework, most models include these five general stages:
Awareness: AI is a topic of interest, but there’s little to no actual use.
Experimentation: Small-scale pilots or isolated use cases are being tested.
Operational: AI is integrated into some business processes with measurable results.
Strategic: AI is aligned with business goals and used across departments.
Transformational: AI is a core capability, influencing decisions, operations, and innovation at scale.
These levels help teams understand their current position and what it would take to move forward.
Gartner’s version is a specific take on the general concept. It outlines five stages:
Awareness
Active
Operational
Systemic
Transformational
Each stage reflects a deeper integration of AI into the business. Gartner highlights not just tech adoption, but also culture, leadership, and governance.
Enterprises often use their model to benchmark progress and identify gaps in AI readiness.
The maturity index is a method for measuring your current position on the maturity curve.
It’s usually based on a set of criteria, like strategy, data readiness, tech capabilities, and team skills.
Some companies use internal assessments, while others rely on third-party tools or benchmarks (like those from McKinsey or Gartner) to calculate their index score.
Start by evaluating how AI is currently used across the business: Are there isolated pilots or enterprise-wide integrations? Is leadership invested in long-term AI capabilities? Are outcomes tied to business metrics?
Assessing your tooling, talent, governance, and strategy alignment can help clarify your current stage and guide next steps.
Many companies either jump into advanced AI initiatives too early—without the necessary data infrastructure or leadership buy-in—or stay stuck in perpetual experimentation.
Common pitfalls include chasing hype-driven projects, misaligning AI efforts with business goals, and underinvesting in governance, ethics, or internal expertise.