Digital maturity is the dimension on which customers judge whether a supplier fits with them in terms of systems: is there a customer portal, can quotes be generated automatically, is there an API connection with procurement systems, does invoicing work without a manual intermediate step. For years this was mainly a matter of IT budget and the staffing of an IT department. Whoever had more people and more money, built more. That assumption is under pressure.
Behind every expression of digital maturity there is work: building and maintaining connections, setting up customer portals, writing documentation for an API, testing integrations, following up on error messages when a connection stalls. That work used to fall into three kinds: specifying what needs to be built, the building itself, and maintaining it. All three required specialized hours, and those hours were scarce and expensive.
The shift is not in "AI makes companies more digitally mature" as a vague promise, but in specific tasks that shift between the three categories that recur everywhere: tasks that AI takes over completely, tasks AI does with human oversight, and tasks that remain human work.
Code for standard connections, test scripts and basic documentation can partly be generated by AI. Judging whether a connection works correctly in the specific context of a customer, with the quirks of their ERP system, continues to require oversight: someone who approves or rejects with a reason. And the decision about which integrations get strategic priority remains human work, just like the conversation with a customer about what a connection means for their process.
The effect is that the time between "we want a connection" and "the connection works" can become shorter for the part of the work that can be taken over, while the part that requires oversight or consultation takes about as long as before. Companies where that first part was a large share of the lead time notice more difference than companies where it always came down to alignment with the customer.
The difference between companies that already benefit from this shift and companies that do not rarely lies in access to the same technology. It lies in whether the work behind digital maturity has already been broken down into pieces that AI can take over, pieces that require oversight, and pieces that remain human work. Where that breakdown has not yet been made, everything stays with the specialized hours that were always needed for it, including the part that could by now go faster.
That makes digital maturity a dimension on which the comparison between competitors can shift without either of them having invested more in IT staff. A competitor with a smaller budget but a better-structured process can deliver a customer portal or API connection just as quickly as a competitor with a larger team that still works according to the old division.
A customer does not notice this from a claim on a website, but from time: how quickly a connection with their system comes about, how quickly an error message is resolved, whether a quote automatically matches what was agreed earlier. Those are the moments where digital maturity translates into something measurable, and where a claim like "we are digitally advanced" holds up or does not.
This is also why this dimension is not separate from other points of comparison. What a supplier claims about certifications that buyers set as a condition is often connected to how well their systems can also demonstrate those certifications. And the ease with which a customer does business is largely a sum of digital maturity: what AI changes about the ease of doing business describes that connection further. Geographic spread also plays a role, since a well-connected system more easily serves multiple locations or countries, as described in what AI changes about competition on geographic coverage.
To know whether a claim about digital maturity is distinctive, it must be set against what comparable companies already do. That requires a carefully composed comparison group; how a peer group is composed determines whether that comparison says anything. The next question is whether there is room that no one in that group fills, which a white space analysis brings into view.
The underlying question, which work in a specific company can genuinely be taken over by AI, is answered per task with the work scan from FTE TO AI.
This does not touch on decisions about staffing; whoever considers that is subject to their own legal requirements, which are not addressed here.
Name where you believe you are winning on digital maturity, and test whether that claim holds up against your peer group. With the free dimension check you can map that out: you name your claims and see which of them can be defended with evidence. The full benchmark is under construction.
Vraag maar waarop er in uw markt gewonnen wordt. Ik vergelijk liever dan dat ik uitleg.
Answers come from this site’s knowledge base. Not tailored advice, and not a scan of your company.