There is a temptation to approach AI-readiness as a collection of separate themes that you can combine as you please. Pick what feels urgent, measure that, move on. The maturity measurement of hybridresourcing does not work that way, and that is not a matter of style.
The seven dimensions are not equal in timing. Organization, IT infrastructure and data management are fundamental: without a working base there, everything that follows has no ground to stand on. The remaining dimensions are dependent. They assume that the foundation can already carry something. This is a technical order, comparable to a foundation that must be in place before a floor makes sense. You can build the floor without the foundation, but what you are then measuring is not what you think you are measuring.
Organizations that start with the dependent dimensions — say, with how teams interpret AI outcomes, or how quickly decisions are adjusted — get scores that feel like progress but that have no meaning without the underlying layer. A team can be excellent at interpreting AI output while the data on which that output relies is unreliable. That score on the dependent dimension then says something about skill, but nothing about whether the result can be trusted.
The fundamental dimensions are therefore not more important in the sense of prestige. They are earlier in the sense of logic. Data management determines what is substantively possible. IT infrastructure determines what is technically scalable. Organization determines who is responsible for what when things go wrong. Without these three, any score on the remaining dimensions is a score without foundation.
The method works with a plot round: several people within the same organization score all seven dimensions independently of one another. What becomes visible afterwards is often more informative than the score itself. Spread between respondents is a signal. If the CIO places IT infrastructure at a higher level than the teams that work with it daily, that says something about who holds which picture of reality — and that is precisely the conversation that precedes every other step.
The spread is emphatically not an error in the measurement. It is the measurement. An organization in which everyone scores exactly the same level either has an exceptionally shared picture, or nobody has properly thought through the question. Both are possible; the plot round makes the difference visible rather than averaging it away into a single figure.
The maturity measurement says nothing about which tasks AI can take over. It says nothing about how much time an implementation takes, and nothing about what an AI project yields financially. It produces no percentage, no timeline, no amount — because those figures cannot come from a readiness measurement. What the method does produce is a placement on five levels — baseline, foundation, activation, insight, intelligence — per dimension, and a picture of where the organization diverges most in its own self-image.
This is also why the method gives no advice about what you should do next. There is no "you should" in this outcome. There is an outcome, and the organization itself determines what to do with it. That distinction is not meant to be non-committal; it is a boundary that belongs to an instrument that measures, not advises.
The maturity measurement answers the question of whether an organization can carry AI. It does not answer the question of which work AI could take over — that is a different issue, with a different scale and a different logic. The difference between these two questions is worked out in the distinction between carrying AI and AI taking over work, and it is worth having that distinction sharp before you interpret a measurement as a statement about something other than what it measures.
The order between fundamental and dependent also touches on what an organization may automate and what it may not. Anyone wondering what needs to be mapped out before automation can seriously be considered will find starting points in what belongs in a decision inventory, and anyone wondering where the line lies will find that worked out in which decisions may never be automated. Both questions assume an organization that already knows where it stands — which is exactly what this measurement produces.
Because readiness is not a fixed state, the question of how often this measurement needs to be repeated is also relevant; that is addressed in how often a maturity measurement needs to be repeated.
Once the fundamental dimensions are in order, or at least mapped out, a different question arises: which part of the actual work in the organization can be taken over by AI. That is not what this measurement answers. That is what the work scan of FTE TO AI is for: a calculation per task, which shows which part of the work can be taken over and which part remains with people. The maturity measurement determines whether the ground is solid enough; the work scan calculates what can be built on that ground.
The maturity measurement of hybridresourcing is under development. Anyone who wants to use the outcome of this measurement as soon as it becomes available can sign up for the waiting list.
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.