IT infrastructure is one of the fundamental dimensions in hybridresourcing's maturity measurement. Fundamental here means something specific: this dimension must be in order before dependent dimensions such as performance management or ethics can grow meaningfully. An organization can very well have ambitions in the area of AI-driven decision-making while the infrastructure underneath it is still at baseline level. That is not a contradiction, it is a sequence that is not skipped.
The measurement does not look at whether you have a cloud contract or have replaced a number of servers. It looks at whether your technical environment is set up to support AI applications: can systems talk to each other, is computing capacity available when an application requires it, and is the architecture built in such a way that a new application does not immediately run up against the limits of an outdated system. It is about the question of whether the substrate can bear a load, not about the question of whether something has already been built on it.
At baseline level, infrastructure runs on what was once put in place, often without anyone still knowing exactly why. Systems are connected to each other via workarounds, integrations are built by hand, and every expansion feels like a risk.
At foundation level, there is visibility into what exists. The organization knows which systems exist, which data flows through them, and where the bottlenecks are. There is not yet a structural approach, but there is a map.
At activation level, the first steps have been taken to make infrastructure suitable for AI applications: integrations that previously ran manually have been automated, and capacity has been reserved for experiments.
At insight level, the infrastructure is set up to scale. New applications can be added without the entire system being overhauled, and there is monitoring that shows where the infrastructure comes under pressure.
At intelligence level, the infrastructure itself is part of the organization's adaptive capacity. It scales along with demand, flags bottlenecks before they become acute, and no longer forms an obstacle to what the organization wants to build.
A number of signals are more reliable than a self-assessment. Anyone who asks how long it takes to connect a new system to the existing environment, and finds the answer varies strongly per team, immediately sees that there is no shared picture of the architecture. Anyone who asks whether an AI pilot has ever gotten stuck on a technical limitation that no one had foreseen often hears a hesitation that is more telling than the answer itself. And anyone who asks who in the organization can oversee the entire technical landscape usually gets, at baseline and foundation level, the name of one person, not a system.
This is precisely why the plot round in the measurement is valuable. When a CIO, an IT manager, and a process owner score infrastructure maturity separately, the spread between those scores often turns out larger than expected. That spread is not an error in the measurement, it is itself information: it shows whether the organization has a shared picture of its own foundation, or whether each team is sailing on its own assumptions.
The step from baseline to foundation mainly requires an overview: mapping out what exists, before anything is added. The step from foundation to activation requires targeted investment in integrations and capacity, at the points where the map from the previous step has exposed bottlenecks. The step to insight requires an architecture that is built on scalability rather than on separate solutions for separate questions. And the step to intelligence requires that infrastructure be treated as something that continuously moves along, not as something that is replaced once every few years.
What that step precisely costs in time and resources depends on the organization, the existing systems, and the ambition behind it. An indication of what that depends on can be found on the page that calculates what it costs to raise an organization a level.
Infrastructure is the layer on which other dimensions rest, but it does not work in isolation. An organization with strong infrastructure but weak data management maturity does have the pipes laid, but not the flow that needs to run through them. And infrastructure that is technically sound but not embedded in attention to privacy and security carries risks that only become visible when it is too late. The seven dimensions of the measurement can therefore never be read in isolation; infrastructure is the beginning of a chain, not the whole story.
This measurement shows whether your organization can support AI: whether the foundation is in place to let applications work structurally. It says nothing about which part of the work itself is suitable for AI. That question is answered by the work scan of FTE TO AI, which calculates per task which part of the work can be taken over by AI. The two questions belong together, but it is this sequence that works: first insight into what the organization can bear, then insight into what there is to bear.
The maturity measurement, including the plot round for infrastructure, is under construction. Anyone who wants to use the measurement 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.