A board that gets started with AI usually asks one question: which work can a system take over from us. That is a legitimate question, but it is the second question. The first question is whether the organization around it can carry that system. Whoever reverses that order ends up with pilots that look promising on paper and get stuck in practice, without it becoming clear why.
AI carrying means that the organization, the IT infrastructure and the data management are in order before anything is taken over. These are the fundamental dimensions: they determine whether an AI application can function on the basis of reliable data, in an infrastructure that is set up for it, within an organization that knows who decides on what. After that come the dimensions that depend on these foundations. That order is not a preference of ours, but the way it works: a dependent dimension cannot grow further than the foundation beneath it allows. If you want to know what that looks like for your organization at the infrastructure level, you will find more about that specific layer on the page how mature is your IT infrastructure when it comes to AI.
AI takeover is a different question: which part of a specific task can be done by a system, and what remains human work. That is the question the work scan of FTE TO AI answers, per task, with an outcome that indicates which part can be taken over. That question is pointless as long as it is not established whether the organization can carry the result. A system that takes over a task produces output. If nobody can assess that output, if the data on which the system runs is not reliable, or if it is not clear who is responsible for the outcome, then takeover changes nothing about the problem. It only relocates it.
The maturity measurement of hybridresourcing looks at the carrying side. Five levels, from baseline to intelligence, across seven dimensions. Not a score that says whether you are allowed to use AI, but a picture of where the organization stands now and which dimension is currently the most limiting. In a plot round, multiple people from the organization score separately from each other. The spread that results from this is often more informative than the average: a large spread on a dimension shows that there is no shared picture within the board of where the organization stands. If you are wondering why your own team does not agree internally on the pace of AI adoption, that connects to what we describe on why your board disagrees about the AI pace.
The measurement does not say what you should do. It does not provide tailored advice, no recommendation, no step-by-step plan with a timeline. It provides a structure: a score per dimension, a picture of the spread between assessors, and an indication of where the organization is likely to get stuck if it proceeds now. What you do with that is up to you. The measurement also does not guarantee an outcome. A high score at the organizational level says something about readiness, not about results. Moreover, two organizations with a low score on the same dimension can face very different next steps; what plays a role in that, you can read on which two scenarios go with a low score.
The measurement is also not a snapshot that remains valid forever. Organizations change, infrastructure gets replaced, data management improves or deteriorates. How often a new measurement is needed depends on what has changed in the meantime and on how close the organization was to a tipping point; you can read about that on how often does a maturity measurement need to be redone.
Many boards form their picture of AI readiness based on what a few employees do in a chat window. That picture is narrow. It says something about individual use, but nothing about the organization, the infrastructure or the data management behind it. What gets overlooked in the process is described on what you do not see if you only know the chat window. For those who want to start with a broader picture of the organization itself, there is the page how mature is your organization when it comes to AI.
The maturity measurement of hybridresourcing is currently being built. There is not yet an instrument you can fill in today. Those who want to use the measurement as soon as it becomes available can join the waiting list. That is not an offer for something that already exists, but a way to stay informed of when it does.
Once it is clear whether the organization can carry AI, the question changes. Then it becomes relevant to know which part of the work itself can be taken over: not as a general picture, but per task, with an outcome that indicates which part can be done by AI and which part remains human work. That is what the work scan of FTE TO AI was built for. The carrying side and the takeover side answer different questions, and both are needed for a complete picture — but the order in which you ask them determines whether the second question yields anything.
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.