Baseline is the first of the five levels in hybridresourcing's maturity measurement. It is not a negative judgment. It describes an organization where the seven dimensions — including organization, IT infrastructure and data management — do not yet have a structure that can carry AI. Perhaps experiments with AI have already taken place. Pilots are running, licenses have been purchased, someone on the team uses a language model for reports. That changes nothing about the level. Baseline is about what is in place, not about what has already been tried.
At the baseline level, a shared picture of who is responsible for what when it comes to AI is missing. Decisions about tools are made at the department level, without anyone having an overview of everything that is running. The IT infrastructure is set up for the work processes of before AI: systems that do not talk to each other, no central place where data comes together, no vision of what is needed to get models working with the organization's data. Data management exists in name — there is an archive, a CRM, an intranet — but no one can say with certainty where which data is located, who has access to it, and whether that data is accurate.
The fundamental dimensions take precedence over the dependent ones, and you feel that hardest at this level. A pilot that works well on its own gets stuck as soon as it needs to scale, because the infrastructure underneath cannot handle it. A team that enthusiastically tests an AI application discovers that the data it needs is not available anywhere in a structured way. These are not incidents. They are the predictable consequences of a foundation that is not yet complete.
Baseline does not mean that the organization is standing still or that people lack the will. Often the opposite is true: there is movement, there are initiatives, there is budget set aside. What is missing is not motivation but coherence. Separate pilots without shared infrastructure, separate data use without central management, separate decisions without shared ownership. The energy is there. The structure to capture that energy is not yet.
It also does not mean that this level has to last long, or that something is wrong with an organization that starts here. Most organizations that take a serious look at AI start at baseline or just above it. The level is a snapshot, not a verdict.
In the plotting round of the maturity measurement, several people score separately, and with baseline organizations something specific often stands out: the spread between respondents is large. Where an IT manager sees that the infrastructure still has large gaps, a department head may think things are fine, because the tools they use work adequately. That spread is itself a signal. It shows that there is not yet a shared picture of where the organization stands — and that lack of a shared picture is precisely what characterizes baseline.
The transition from baseline to the next level does not call for more AI projects. It calls for letting something take precedence over the rest: clear responsibilities, an infrastructure that can collect and unlock data, and a data management that is reliable enough to build on. Without that foundation, every subsequent step remains temporary, because it stands on sand.
What that transition means concretely for your organization differs per situation — that depends on sector, size and which of the seven dimensions lags behind the most. How you move from baseline to the next level describes which movements belong to that. For comparison, it is also useful to see where the step leads: what the foundation level means in practice shows what a foundation looks like once the basic structure is in place. For organizations that want to look beyond the next step alone, what the insight level means in practice describes how maturity develops further once data and infrastructure are in order.
hybridresourcing's maturity measurement looks at the question of whether an organization can carry AI: is the foundation there, is the structure in place, is there a shared picture of responsibilities and data. That is a different question from which part of the work itself is suited for AI. An organization at baseline level may well have work processes in which many tasks lend themselves to being taken over by AI — it is just that the structure to do so in a sustainable way is still missing. Anyone who wants to know how much of the work itself, task by task, can be taken over by AI will not find that answer in the maturity measurement but in the work scan of FTE TO AI. That calculates per task which part is suitable for AI, independent of the question of whether the organization is already ready for it. Both answers together — is the organization ready, and is the work ready — give the complete picture before you invest in anything.
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.