The CIO thinks it's going too slowly. The CHRO disagrees. The COO stays silent, because operationally things are just gaining momentum. Nearly every board has this conversation, and it usually remains undecided, because no one has a shared picture of where the organization actually stands.
If four board members were to independently describe how mature their organization is in the field of AI, four different answers often emerge. Not because one of them is wrong, but because each sees a different part of the organization. The CIO looks at infrastructure and systems. The CHRO looks at what people on the work floor do. The COO looks at processes that are running. Those pictures don't automatically overlap.
The maturity measurement from hybridresourcing is built on that fact. Instead of asking for one joint score, the plot round has multiple people score separately, across seven dimensions, at five levels: baseline, foundation, activation, insight, intelligence. Only afterward are the scores placed side by side. The result is not an average, but a spread. And that spread is often the most interesting part of the outcome.
A large spread between board members is not a flaw in the measurement. It is a signal. It means there is no shared picture of the starting situation, and that every conversation about pace is therefore actually a conversation about different organizations. Whoever knows that has a different conversation than whoever doesn't.
The seven dimensions are not equal in order. Some are fundamental: how mature your organization is when it comes to AI and how mature your IT infrastructure is when it comes to AI determine what the rest of the organization can support. Other dimensions depend on them. An explanation of why the fundamental dimensions must come first is separate from that, and is needed to properly read the outcome of the plot round. A high score on a dependent dimension, while a fundamental dimension scores low, usually means that high score won't hold up for long.
The measurement does not produce a figure that tells how well or poorly an organization is doing compared to other organizations. There is no benchmark, no ranking, no percentage indicating how far along you are. The five levels describe a sequence of building, not a competition.
The measurement also does not say what needs to happen. It provides a structure with which a board can have the conversation, not a recommendation that replaces that conversation. Two organizations with the same low score on the same dimension can be there for very different reasons, so a low score on its own is not enough to draw a conclusion. What can be said in such a case is worked out in which two scenarios belong to a low score; often these are not technical problems but organizational ones.
Furthermore, the measurement is not a snapshot that guarantees anything about the future. An organization that scores at activation today is not automatically ready for what will be asked a year from now. AI applications change, and what counts as mature today may count as basic after a while.
A large part of the confusion in boards does not arise during scoring, but before it: because everyone has a different picture of what AI means in practice for their own organization. Whoever knows AI mainly from a chat window on their own laptop misses a large part of what is happening in data management, in decision-making, in processes that are not visible from an individual workstation. An overview of what you don't see if you only know the chat window shows where that creates blind spots, precisely among people who work with AI daily and therefore think they have the overview.
That blind-spot problem also touches on another point: many organizations don't even know which decisions are made daily, let alone which of those involve AI. A decision inventory maps that out, and what should minimally be included in it is described in what belongs in a decision inventory. Without that overview, every discussion about AI pace is a discussion without shared facts.
The maturity measurement answers the question of whether an organization can support AI: whether the foundations are in place, whether organization, infrastructure, and data management are sufficiently developed to build on. It does not answer the question of which part of the current work can be taken over by AI. That is a different question, with a different kind of answer: not a level per dimension, but a percentage per task. Whoever wants to know, besides readiness, where the room lies within the work itself, will find that in the work scan from FTE TO AI, which calculates per task which part of it can be taken over by AI.
The maturity measurement from hybridresourcing is currently being built. Whoever wants to use the plot round 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.