A workforce leader — often responsible for workforce planning, capacity management or the bridge between HR and the business — gets AI put on his plate without anyone having explained beforehand what that means for staffing. The question comes from above: less inflow, different profiles, more flexible scaling. The workforce leader needs to have an answer to that while he himself does not know whether the organization has its preconditions in order to make that answer come true.
The question is not whether AI affects roles. That question has already been answered by others, higher up in the organization. The question a workforce leader asks himself is different: on the basis of which assumptions is the workforce planning now being adjusted, and how solid are those assumptions? If capacity scenarios are being built on an AI promise that assumes good data, working infrastructure and teams that can work with the outcomes, and those preconditions are not there, then the planning is wrong. He does not notice that today. He notices it at the moment the inflow has already been slowed down and the output does not come.
A workforce leader who bases his workforce planning on an AI effect that does not materialize is on his own. The establishment has by then already been adjusted, vacancies have been put on hold or actually just opened up, and the business is counting on a capacity that turns out not to be there. Recovering costs time that a workforce plan does not have — inflow has lead time, and a gap that is spotted too late cannot be closed at the push of a button. That risk does not sit in the technology. It sits in the assumption that the technology already works in an organization that is not yet ready for it.
If the workforce leader does know how mature the organization is before he plans, his position changes. He can distinguish between a capacity scenario that is realistic in the short term and a scenario that depends on foundations still to be built. He can give the business a timeline that holds up rather than a timeline that hopes. That is no guarantee of a good outcome, but it is a plan that does not collapse the moment the first assumption fails to materialize.
What a workforce leader does not accept is an AI roadmap that speaks of future capacity without specifying what needs to be in place first. "AI will eventually replace part of the inflow" is not a planning basis if no one can indicate whether the data management, the IT infrastructure and the organizational structure are already ready for that. He also does not accept any figure that is based only on the gut feeling of an enthusiastic sponsor. What he needs is a picture of where the organization truly stands, per dimension, with the spread between what different people in the organization think about it — because that spread is itself already information about how stable a capacity assumption is.
The maturity assessment from hybridresourcing.com evaluates an organization at five levels — baseline, foundation, activation, insight, intelligence — across seven dimensions. Fundamental dimensions such as organizational structure, IT infrastructure and data management come before the dependent dimensions, not because that is a preference, but because that is the order in which it works: without a working infrastructure and reliable data, AI has little to run on, however well the organizational structure is already positioned. In a plotting round, several people score separately, so that it becomes visible where the picture of the organization aligns and where it diverges. For a workforce leader, that spread is often the most useful signal: a large spread on data management means no one is certain whether the foundation is in place, and that is precisely the foundation on which capacity scenarios rest.
This approach also touches other roles in the organization, each from their own interest: a CEO looking for the answer to stalled AI pilots, a COO who needs to assess operational readiness and a CHRO who guards the organizational side of AI maturity. The picture also differs by sector, as can be seen in how far the construction sector stands with the foundations for AI and how that relates to the state of affairs in the installation sector.
The maturity assessment says something about the carrying side: can this organization carry AI, are the foundations in place, is the data in order, is the infrastructure ready for it. It says nothing about which part of the work itself can be taken over by AI. That is a different question, with a different instrument. Once the picture of readiness is in place, the logical next step is the question that follows: which part of the work, task by task, is actually transferable. That is what the work scan from FTE TO AI calculates — per task, based on what the work itself entails, not based on an assumption about the organization as a whole.
The maturity assessment is under construction. There is currently no report to order and no advisor who comes by to go through the dimensions. Anyone who wants to use the assessment 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.