What if the real barrier to AI success has nothing to do with algorithms?
A global 2025 survey found that 72% of organizations use generative AI, yet only one-third manage to scale it beyond isolated pilots. (McKinsey, 2025). Another study shows that just 12% of enterprises have embedded AI deeply enough to achieve real transformation. (Infosys, 2024)
These numbers contradict the public narrative of “AI everywhere.”
If so many companies use AI – why do so few benefit from it?
The answer is simple: AI technology is progressing faster than organizations are ready to absorb it. This is the readiness gap – and understanding it is the foundation of AI-maturity.
What an AI‑maturity model actually is
An AI‑maturity model is not a theoretical framework. In practice, it is a structured diagnostic tool that helps an organization understand:
- how prepared it is to use AI in a systematic way, and
- what organizational capabilities need to change to move from experiments to real, business‑impacting implementations.
Most established models evaluate organizations across dimensions such as strategy, talent, operating model, technology, data and adoption & scaling.
At PAS, the AI‑maturity model is one of the central elements of AI‑strategy development. It is used together with:
- a baseline SWOT analysis (how staff see strengths, weaknesses, opportunities and threats of AI),
- an assessment of AI‑readiness stage across key dimensions, and
- a short, personalized or detailed AI‑roadmap with concrete tasks, deadlines and responsible project leaders.
This combination is not abstract. It becomes a reference model for the organization’s ongoing evolution towards Digital Excellence.
The six dimensions that shape AI success
Although different providers label them differently, the underlying structure of AI‑maturity models around the world is surprisingly consistent. We see the same in our projects.
1. Strategy and leadership
This dimension looks at how clearly AI is connected to business goals and whether there is a leadership mandate.
2. Data and infrastructure
This dimension assesses data accessibility, quality, and integration with core systems like ERP/SAP.
3. Technology and engineering capability
Can the organization build, deploy and maintain AI systems reliably? Are there standards for development, testing and monitoring?
4. Governance and risk management
Especially relevant with generative AI: Who controls model deployment, compliance, data protection, and auditability?
5. People, skills and culture
Research highlights that staff AI‑literacy, change readiness and internal communication are key determinants of maturity.
6. Operating model and workflows
How embedded is AI into actual business workflows? How are decisions re‑wired? Redesigning workflows is strongly correlated with higher bottom‑line impact from AI.
These dimensions illustrate where companies stand today and what blocks progress.
From awareness to transformation: a practical view on maturity stages
Different researchers use different labels, but they describe a similar evolution. You can think of it in five conceptual stages:
- Awareness – AI is on the agenda but little is happening.
- Experimentation – Pilots and proofs‑of‑concept in various departments.
- Operational – Some AI systems embedded in workflows; benefits are local, not enterprise-wide.
- Systemic – AI integrated across key value streams, supported by governance, data platforms and skills.
- Transformational – AI reshapes business models, services and even industries.
Research shows that higher maturity is associated with better organizational capabilities – not just more technology.
In our view, the exact labels are less important. What matters is that every organization knows where it stands and what the next step is.
Why so many companies stay stuck
If AI‑maturity models are so useful, why do so many organizations remain in the early stages? Several patterns emerge from both market research and our project experience:
- Too many initiatives start with “we need AI tools” rather than a business‑problem‑first orientation.
- Data is often scattered across ERP, CRM, emails, production systems and external platforms; integration is expensive and slow.
- Leadership has a vision but mid‑level teams and operational staff lack clarity or skills.
- Governance, responsibilities, model lifecycles and risk frameworks are missing.
- Skills and cultural adoption are underestimated.
These patterns align with McKinsey’s findings that although AI use is increasing, only a minority of companies manage to convert it into measurable enterprise‑wide impact.
This is exactly the situation an AI‑maturity assessment brings clarity to.
How an AI‑maturity assessment changes the conversation
A serious maturity assessment is not a survey with a score. It is a structured process that changes how decision makers see their organization.
In our PAS AI‑strategy packages, this process includes:
- Workshop with key decision makers.
- 10-30 expert interviews across functions.
- SWOT analysis of current AI state.
- Mapping to an AI‑maturity / readiness model across the six dimensions.
- Roadmap creation with concrete tasks, deadlines and responsibilities.
- Training and education to prepare staff for change.
The result is not just a report; it is a shared picture of where the company stands and what needs to happen next.
From diagnosis to implementation: bridging strategy and real projects
One of the typical frustrations with strategy work is that it often stops at PowerPoint. At PAS, we have both consulting and engineering resources, so we help bridge strategy into execution.
Our project track record includes:
- AI‑based classification systems for customer communication and document logs integrated with SAP in banking.
- AI‑based tender automation engines for construction and energy companies.
- AI‑based prediction tools for scrap‑metal pricing.
In many cases, the work starts with strategic questions and a maturity assessment – and ends with operational systems used every day.
What companies can do next
Whether you are at the very beginning or already running several AI projects, the next steps remain:
- Clarify your business priorities for AI.
- Map your data and systems landscape.
- Assess your AI‑maturity honestly.
- Identify 2-3 high‑value, realistic use cases.
- Build a 6-18‑month roadmap with measurable KPIs.
- Invest in people, not only tools.
- Repeat the maturity assessment regularly.
These steps are consistent with leading market practice and our PAS methodology.
AI is here, and organizations are using it extensively – but many are still waiting for measurable business impact. The missing piece isn’t the tech – it’s the organizational readiness.
Organizations that jump directly into tools and pilots often end up with expensive experiments and little structural change. On the other hand, organizations that first understand their AI‑maturity – and then act on it – are far more likely to build AI into a systematic capability.
In our view, the most successful companies in the coming years will not be those who simply spend the most on AI. They will be those who understand themselves best, make informed decisions, and build AI step by step on a solid foundation.
That is what AI‑maturity is about. And that is where a structured, realistic AI‑strategy – supported by a concrete AI‑maturity model – becomes the difference between hype and real impact.