Understanding AI Maturity in IT: What Level is Your Organization At?

AI is on a similar trajectory.

AI adoption in IT, in fact, mirrors the staged evolution of self-driving cars. Both technologies have seen phased progress, with each stage representing a marked leap in capability, confidence, and cultural acceptance.

Atomicwork conducted a comprehensive study on the State of AI in IT 2025 in collaboration with PeopleCert (the parent company of ITIL) and ITSM.tools, which involved responses from 300 global IT professionals and 700 North American end-users.

In the study, we identified six maturity levels of AI adoption within IT, resulting in a bell-curve distribution of AI adoption.

Let’s break this down and see how leadership and organizational context influence adoption trajectories.

The 6 maturity levels of AI adoption

Our analysis categorizes AI maturity into six levels based on organizational adoption and cultural affinity.

1. Level 0: Organizations in this phase are not using AI and have no plans to start. Like cars with manual control, they operate entirely on human effort, with no reliance on AI-enabled systems.
2. Level 1: These organizations aren’t using AI yet but are exploring its potential.
3. Level 2: This level involves companies using free consumer AI tools but not using enterprise AI features. These organizations parallel vehicles with basic driver-assist features. The potential is there, but its application is minimal.
4. Level 3: Here, AI capabilities are embedded in enterprise software, and free consumer tools are discouraged.
5. Level 4: Organizations actively encourage the use of both enterprise AI capabilities and consumer AI tools.
6. Level 5: These trailblazers adopted AI/ML long before its mainstream boom. They are akin to fully autonomous vehicles, blazing the trail for others to follow.

Most organizations cluster around Level 3 with diminishing numbers at either end of the spectrum. This bell curve showcases that while AI adoption is growing, full maturity is yet to be achieved for a good chunk of organizations.

Leadership shapes the AI maturity trajectory

While digging further into how AI adoption originates, the report found that it typically starts with the IT leadership, not with CXOs, as we speculated.

• IT leadership: Responsible for 34% of AI initiatives, IT leaders act as visionaries, integrating AI into operational workflows.
• C-suite: Accounting for 23% of initiatives, the C-suite’s influence is significant but tends to focus on strategic ambitions rather than operational feasibility.
• IT teams: Comprising 22%, they lead adoption efforts, particularly in mid-sized organizations (501–2000 employees), where their practical insights drive implementation.

Moreover, the likely business or IT roles responsible for AI investments differ by organizational size.

The C-suite is most likely to lead on AI in organizations with 1-100 employees. For organizations with over 500 employees, IT leadership is more likely to lead. Interestingly, for organizations with 501-2000 employees, the IT team is more likely to lead on AI.

Also, regional differences highlight how leadership influences AI adoption. Europe-based organizations are most likely to have AI requirements that originate in the C-suite. Plus, they have a more conservative approach to AI investments, which could be (not surprisingly) tied to the stringent governance laws in the region.

Meanwhile, North American-based organizations are most likely to have IT leadership taking the reins on AI investment. Asia is the only region where the “power hierarchy” is inverted, with the IT team leading AI investments, followed by IT leadership, and with the C-suite in third place.

The trajectory of AI maturity is nonlinear, shaped by organizational size, industry, and regional nuances. For instance, in the energy and utility sectors, IT teams often champion AI initiatives, whereas IT companies lean on C-suite directives.

What’s next in driving AI maturity

The 2025 State of AI in IT report signals that more organizations will adopt AI. Leadership’s role in championing AI is critical and cannot be overstated.

Whether it’s the C-suite driving strategic initiatives or IT teams executing tactical implementations, leadership directly impacts the pace and success of adoption.

As we look to the future, the journey toward further maturity will depend on:

• Leadership alignment: Collaboration between IT teams and executive leadership.
• Cultural readiness: Fostering trust in AI systems across all organizational layers.
• Strategic investment: Allocating budgets that reflect AI’s transformative potential.

The evolution of AI maturity is far from linear, but with informed leadership and strategic planning, organizations can confidently ‘navigate’ this journey—much like how autonomous vehicles are set to redefine transportation.