The first question is: can AI perform this task? The second question, and it is often skipped, is: does this organization have the technology and the capacity to actually let that work shift? A task can be technically transferable and still remain human work for years, because the systems do not talk to each other, the data is not in order, or no one has the authority to organize the oversight that is needed. Readiness therefore does not sit in the task. It sits in the organization.
Three categories of work run through every process. Part can be taken over by AI independently. Part can be partly taken over, with a human approving or rejecting and substantiating that with a reason. And part remains human work, because judgment, relationship or responsibility cannot be delegated. Which category a task gets is not fixed. That changes with the technology a company has connected and with the capacity that is free to make and maintain that connection.
Two companies with similar ambitions can score very differently on this dimension. Not because one is more progressive than the other, but because the underlying conditions have been filled in differently.
The first company has data that is structured, up to date and accessible to the systems that must take over the work. It has connections between applications that today run separately from each other. And it has people with time to test, adjust and safeguard new ways of working, alongside their existing work.
The second company has the same ambition on paper, but the data is spread across systems that are not set up to communicate with each other. Everyone who should be reviewing the process is already fully occupied with operational work. There is no freed-up capacity to carry the change, let alone to test whether it works.
The difference between the two is measurable. It concerns the state of the technology, the fte capacity available alongside daily operations, and the question of whether someone has the assignment and the mandate to make this shift succeed. Without those three, an ambition remains a statement on a strategy document.
When a task moves from fully human work to AI execution with human oversight, more changes than just who performs the task. The human's work shifts from executing to assessing. That requires different skills, a different kind of attention, and often a different place in the process. The capacity that is freed up is not automatically deployable elsewhere; it has to be organized, with a clear picture of where those hours go.
The technology also changes role. A system that previously only registered must now be sufficiently reliable to support decisions or partly make them. That places demands on data quality, on the explainability of outcomes, and on the question of who intervenes when the system deviates. What an employer subsequently does with the freed-up capacity falls outside this assessment; separate statutory requirements apply to that, and it is a choice that lies with the employer itself.
It does not show itself in an announcement. It shows itself in small signals that accumulate. A team that asks for access to data that should really have been shared long ago. A decision that is delayed because no one knows who is allowed to approve that a task now runs in an automated way. A progress report in which the same task counts as "AI-supported" for one team and still fully manual for another, without anyone being able to explain why.
These signals are connected to other dimensions of readiness. How you determine whether behavior changes depends on how your performance measurement is set up for work that AI takes over; who may make which decision when a task changes category is described in what you record about decision rights in a change; and whether external parties move along in the process depends on how your partnerships are prepared for work that AI takes over. Technology and capacity are not separate from the rest of the organization; they are the layer on which the other dimensions rest or get stuck.
A management team can use the phrase "AI-ready" and each mean something different by it. One director thinks of a pilot that went well, another of a system that still needs to be replaced. That confusion of tongues is precisely where a vague ambition ends and a concrete test begins. How you translate a vision that sounds convincing on paper into something that can be tested is worked out on the page about making a vague vision concrete.
The question of which work in this specific company can truly be taken over by AI is not answered by discussing a dimension in general terms, but per task, with the work scan from FTE TO AI.
The free readiness check consists of eight short questions, one per dimension, and gives a picture of where your organization stands furthest along and where it lags behind, including this dimension of technology and capacity. The full ambition assessment, with the four layers from vision to target state and the translation into roles and decision rights, is under construction.
Vertel wat u wilt bereiken, dan kijken we samen wat daarvoor moet staan.
Answers come from this site’s knowledge base. Not tailored advice, and not a scan of your company.