Healthcare operates with a different relationship between risk and pace than most sectors. Where elsewhere a pilot may fail without consequences beyond the organisation itself, an error in a care process directly affects a patient or client. That relationship not only slows down the introduction of AI, it also changes the order in which things must be put in place. An organisation in healthcare cannot experiment with a task as long as the data underlying that task is not reliably traceable, and cannot scale up as long as the IT infrastructure keeps islands separated from each other instead of forming a whole.
On top of that, healthcare consists of multiple worlds that rarely move at the same speed. A back office that processes invoices and scheduling may already be well ahead in terms of data management compared to care delivery itself, where systems are often set up per department or per specialism. That inequality within a single organisation is precisely why a maturity measurement cannot rely on a single department alone. Anyone who only measures the supporting services sees a different picture than someone who also includes care delivery, and both pictures are incomplete without each other.
The hybridresourcing.com measurement works with five levels: baseline, foundation, activation, insight and intelligence. In healthcare it stands out that many organisations already reach foundation or activation on the organisation dimension — there is policy, there are owners of digital initiatives, there is boardroom attention for AI. But on data management the picture regularly remains stuck at baseline or foundation, because patient data, electronic records and operational systems are not always built to be reused for anything other than what they were originally recorded for.
That difference between dimensions is no coincidence. The order in which dimensions develop is fixed: organisation, IT infrastructure and data management must be in place first before dimensions that depend on them — such as decision-making based on AI insights — can advance further. A healthcare institution that invests in an intelligent dashboard before the underlying data is consistent is building on a foundation that still needs to set. That explains part of the pilots that start well and then get stuck: not because the application doesn't work, but because the layer beneath it cannot bear the load.
The seven dimensions of the measurement give an organisation a position, but the plot round provides something that is particularly relevant in healthcare: insight into spread. When a board member, an IT manager and a head of a care department each score separately on the same dimensions, it often turns out that their views diverge. The board member sees an organisation that is ready, the care department sees systems that still operate separately from one another. That spread is itself a finding. It shows where within the organisation the conversation still needs to take place before an investment decision can rest on anything solid.
This spread is not unique to healthcare, but its consequences weigh differently there. In a wholesale or manufacturing company, a wrong assessment of readiness leads to a delayed project; in healthcare, it leads to a system being rolled out on a foundation that cannot support it, with consequences for people who had no say in that process. Anyone wanting to know how this trade-off plays out in other sectors can make the comparison with how manufacturing scores on AI maturity or with the state of affairs in professional services, where the risks of a misstep are generally smaller but the foundations are just as often missing.
The hybridresourcing.com measurement does not describe an ideal image or a mandatory growth path. It describes a position on seven dimensions, at a given moment, as seen through multiple eyes. For a healthcare institution this mainly means that the question is not whether it is ready for AI in general, but on which dimension it should invest first before a next step delivers anything. For one institution that is IT infrastructure that connects departments, for another it is data management that makes data usable beyond its original purpose.
Healthcare shares that question with other sectors that work with complex, fragmented systems. The situation at the transport sector and its AI maturity or at education and the state of AI readiness shows that fragmentation of systems and processes manifests differently everywhere, but that the order from fundamental to dependent dimensions always follows the same pattern.
The hybridresourcing.com maturity measurement answers the question of whether an organisation can carry AI: whether the organisation, the infrastructure and the data are ready for something to be built on them. It does not answer the question of which work can actually be taken over by AI. That question is answered by the work scan from FTE TO AI, which calculates per task which part of the work qualifies for takeover. An organisation that knows through the maturity measurement where its foundation stands can then use that work scan to see which work can be built on it, and which work is not yet ready for that.
The maturity measurement is under development. Anyone who wants to follow its development or be among the first to measure how their own organisation scores on the seven dimensions 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.