Education organisations differ from most other sectors in that the core activity itself – teaching, guiding, assessing – is difficult to separate from the people carrying it out. Where a factory or a shop can rewrite processes without affecting the identity of the work, any change in a school or college quickly touches on the relationship between teacher and student. That doesn't make the sector slower in adopting technology, but it does make it more cautious about what AI is actually allowed to do before talk turns to 'taking over'.
On top of that, educational institutions often consist of multiple layers that don't move at the same speed: a board, a number of locations or faculties, and a large group of teachers with their own working methods. Most of the AI initiatives we encounter start in one of those layers without the other layers being taken along. A teacher experiments with a language model for feedback, while the IT department has no picture of what data leaves the building in the process. A board formulates an AI vision, while the data management of student tracking systems still relies on paper or separate spreadsheets. That mismatch is not a matter of unwillingness, but of sequence.
A board member, director of operations or head of IT who sees AI pilots getting stuck often recognises the same pattern: the pilot works on its own, but doesn't scale. That is rarely because the technology falls short. More often, one of the foundational layers needed to carry a pilot is missing: an organisational structure that can make and assign decisions about AI, an IT infrastructure that can safely accommodate new applications, and data management that is in order before a model can do anything meaningful with it. Only once those three are in place does it make sense to look at the dependent layers: how people work with it, what insight it produces, and how intelligently the organisation ultimately handles it.
In education this plays out more strongly than elsewhere, because the foundational layers have often grown historically around educational choices, not technical ones. A school administration is set up for accountability to the inspectorate, not for reusable data for AI applications. An IT department is often small and geared towards maintenance, not integration of new systems. That is not a shortcoming, it is a different priority set decades ago that now factors into how quickly AI actually lands.
The hybridresourcing.com measurement looks at seven dimensions, divided across five levels from baseline to intelligence. For educational institutions it stands out that the score on organisation and data management often lags behind the score on ambition or on individual applications. There is regularly already someone working with AI, sometimes even at an advanced level, while the organisation as a whole is still at the first level in terms of decision-making and data setup. That spread becomes visible in the plotting round, in which several people within the same institution score separately. Among education organisations that spread is often larger than average, precisely because boards, IT and teaching staff rarely work from the same picture.
That spread is not a problem in itself. It is information. An executive who thinks the organisation is ready for AI at scale, and an IT manager who knows the infrastructure cannot yet handle that, are both right from their own position. The measurement makes that difference explicit, so the conversation can focus on what needs to be in place first, rather than on who is right.
The seven dimensions are not equal in timing. Organisation, IT infrastructure and data management are the dimensions the other four rest on. A school that wants to work on insight or intelligence without having its data in order is building on quicksand. That applies to educational institutions just as much as to retail businesses struggling with similar data management backlogs or to the hospitality sector, where organisational structures are often just as fragmented across locations. The sector differs, the sequence does not.
That makes the question 'how far are we' less a question about a score and more a question about where the foundation is missing. An institution that scores ambitiously on paper for AI use, but has no view of its own IT infrastructure, will not be able to deliver on that ambition without first going back to the basics. That is also what institutions in the ICT sector and in financial services show: high ambition on the dependent dimensions does not compensate for a weak foundation.
The hybridresourcing.com maturity measurement is under construction. Anyone who wants to know how their own institution scores on the seven dimensions, and how that score compares to that of colleagues within the same organisation, can sign up for the waiting list. There is no tool to use today yet; there is, however, a clear picture of what the measurement will deliver once it becomes available.
This page is about the question of whether an education organisation can carry AI: whether the foundation is in place before anything is built on it. A different question, which only becomes meaningful afterwards, is what AI can concretely take over within education. That is what the work scan from FTE TO AI is for: it calculates, per task, which part of the work is suited to being taken over by AI, regardless of whether the organisation as a whole is already ready for that. Anyone who combines both questions gains insight not only into the possibilities, but also into the order in which they can become reality.
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.