The ICT sector has a head start that is also a pitfall. Where other sectors still have to get used to the idea of AI, knowledge of models, architectures and tooling is usually already present in this sector. Developers experiment, teams build proof-of-concepts, and there is rarely resistance to the technology itself. That creates the impression that the sector as a whole is far along. But technical skill within part of the organization is something different from maturity of the organization as a whole. That is exactly where things often get stuck.
In an IT company or an IT department, the infrastructure is generally in order: there is computing power, there are development environments, there is experience with rolling out software. That is one of the fundamental dimensions scored in the maturity measurement, and on that point the sector generally scores high. But the other fundamental dimensions — how the organization is structured around AI initiatives, and how data is managed outside the systems of the developers themselves — do not always keep pace. A strong IT department does not mean the rest of the organization can handle the same tempo. Sales, finance, HR: these functions often have a different relationship with data and with change than the people building the AI pilots.
That difference explains a pattern that occurs more often in this sector than elsewhere: pilots that work technically excellently, but that do not grow into a structural place within the company. Not because the model falls short, but because the organization around it is not set up to absorb the result. Those who want to understand, regarding the fundamental dimensions, why that order is not negotiable will find more about it in why the fundamental dimensions come first.
In the ICT sector, the spread between respondents is often greater than in sectors with more uniform roles. A developer and an account manager within the same company can place the organization at nearly opposite levels — one sees advanced applications daily, the other sees a spreadsheet that is still updated manually. That spread is information in itself. It shows that AI maturity in this sector is rarely a sector-wide given, but depends heavily on which department is measured and who is asked.
That makes a plot round with multiple respondents in the ICT sector more valuable rather than less. A single score from a CIO or a CTO risks generalizing the picture of the technical core to the entire organization, while the dependent dimensions — where AI is actually incorporated into processes — may score very differently.
The relationship between technology and organization looks different in other sectors. In financial services, the emphasis is often on regulation and risk management as a slowing factor; that picture is described in how far has financial services progressed with AI maturity. In construction, it is often the data management dimension that is the bottleneck, because data about projects and materials is fragmented across parties; that is explained in how far has construction progressed with AI maturity. In the ICT sector, the problem is rarely a lack of technical knowledge or data — the problem more often lies in the organization dimension: who decides which pilot gets scaled up, and on what basis.
The maturity measurement maps this distinction across seven dimensions and five levels, from baseline to intelligence. For an ICT organization, the outcome is rarely a flat line: infrastructure and technical skill are generally higher than organization and decision-making. That inequality is exactly where the measurement is useful — not to confirm that the sector is "doing well," but to show which dimension is lagging and which, as a result, cannot move forward, regardless of how advanced the models already running may be.
A component that is often underestimated in this sector is the documentation of decisions: who made which choice about which AI initiative, and based on what information. Without that inventory, it is difficult to trace why a pilot was or was not scaled up. What belongs in it is described in what belongs in a decision inventory.
This measurement says something about the organization as a whole: is there a foundation on which AI initiatives can rest, and is that foundation equally solid throughout the company, or only among the people closest to the technology. That question precedes another question, namely which part of the work itself qualifies for AI. That is a different kind of measurement. For organizations that want to know which part of the work, task by task, can be taken over by AI, there is the work scan from FTE TO AI — a measurement that does not look at the organization as a whole, but at the work itself, role by role and task by task.
The maturity measurement is under development. Those who want to know when the measurement becomes available can sign up for the waiting list and will receive a message as soon as the tool is ready for use.
Vraag maar wat er moet staan voordat AI in uw organisatie kan landen.
Answers come from this site’s knowledge base. Not tailored advice, and not a scan of your company.