Baseline is the level at which most organizations start. There is AI use, but it runs on individuals: someone in the IT department experimenting with a language model, a marketer generating text, a single manager asking whether a process can be sped up. There is no shared approach, no shared picture of what works, and no structure that retains what is being learned. If that person leaves or takes on a different project, the knowledge disappears with them.
At baseline, what is missing is not the will but the foundation. The seven dimensions distinguished by the maturity measurement — including organization, IT infrastructure, and data management — are still disconnected from each other at this level, or barely developed. That is not a shortcoming; it is the state an organization is in before it has started building deliberately.
A few signals recur consistently at baseline. There is no overview of where AI is already being used within the organization, let alone a shared picture of what that use delivers. Decisions about AI are made wherever the question happens to arise, not on the basis of a consideration that affects the entire organization. Data is scattered across systems that do not communicate with each other, which slows down every step toward something structural. And there is no common language: what one department calls AI is something else to another.
The plotting round within the maturity measurement often makes this visible before there are words for it. When multiple people within the same organization score separately, a wide spread often stands out at baseline: one respondent sees progress that another does not recognize. That spread is itself a signal. It shows that there is not yet a shared picture of where the organization stands, and that is precisely what characterizes baseline.
The path from baseline to the next level does not begin with a new application, but with the foundational dimensions. Organization, IT infrastructure, and data management come before the dimensions that depend on them. This is not a preference; it is the order in which it works: an organization that wants to build something on AI without its data being in order, or without clarity on who decides what, is building on quicksand.
Concretely, this means an overview needs to come first. Who within the organization is already doing something with AI, and on the basis of which data? Where is data relevant to those applications located, and is it accessible and reliable enough to build on? Is there someone or a group that bears responsibility for choices about AI, or does that still happen by chance? These questions may seem modest, but they are exactly what the foundational dimensions zoom in on.
The dependent dimensions — those that concern how AI affects the work itself — come after. At baseline it is still too early to say anything meaningful about that, because the foundation on which those dimensions could rest is missing.
The transition to foundation does not require everything to fall into place at once. It requires the organization to stop treating AI use as something that arises by chance among individuals, and to start documenting what exists: what data exists, who decides what, which systems need to be able to communicate with each other. This is less a technical journey than an organizational one. The technology usually follows once the structure is in place.
It is also a journey that takes time, and its duration depends on how much is already present informally. An organization where one department has already been working deliberately with data for a while has a shorter path to travel than an organization where that is nowhere the case. What exactly is needed to move further from foundation, and which dimensions require attention first, is described on the page explaining how you move from foundation to the next level. Further along in the series, for organizations that are already further along, it is also described how activation relates to the level after it and what insight requires to grow toward intelligence.
Baseline says nothing about an organization's ambition and nothing about the quality of the people who work there. It says something about the state of the foundation at a given moment. Organizations that seriously map out this level often discover that more is already present than they thought — loose initiatives, data that is being kept somewhere after all, people who are already thinking further ahead than their role requires. The maturity measurement brings that together into a picture on which a next step can be built, without filling in that picture with advice that does not fit the organization.
This page is about readiness: about what needs to be in place before AI can gain a structural role. That is a different question from which part of the work AI can actually carry out. Once the foundation takes shape, that second question becomes relevant, and that is exactly where FTE TO AI's work scan connects: it calculates, per task, which part of the work can be taken over by AI, based on the tasks as they are currently carried out. Anyone who wants to know what AI can take over once the organization is ready for it will find that there — including how that differs when it comes to agents that carry out work independently or chains in which different steps follow one another.
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.