A buyer does not read your management information to see whether you have figures. Virtually every company has figures. A buyer reads management information to see whether he can run the company without you, and whether the figures he is shown are also the figures on which the company actually runs.
That means two things at once. First: is there a fixed rhythm of reporting, or does the overview only emerge once someone asks for it. Second: does the knowledge to produce and interpret that information sit in the process, or does it sit in the head of one person who leaves after the handover. A buyer weighs the second more heavily than the first. Monthly figures produced by a system carry different weight than monthly figures a controller compiles using personal insight and a spreadsheet.
In a due diligence this usually comes to light through simple questions. Is the reporting delivered at fixed moments, or only after a request from the advisor. Are the definitions of core terms — revenue, margin, recurring revenue — fixed, or does each report explain them again and slightly differently. Can someone other than the current owner explain last month's figures without consulting anyone.
The level of detail also matters. Management information that only adds up to a result tells a buyer little about where the profit comes from and where it is vulnerable. Information broken down by customer, product, or process shows which part of the profit is stable and which part hinges on a coincidental circumstance — a single large customer, a temporary price arrangement, a supplier that happens to be delivering cheaply right now. That breakdown affects not only this driver but also profit quality: a saving embedded in the process counts differently than a saving that depends on one external party.
Producing management information is, for a large part, repetitive work: gathering data, checking it, putting it into a fixed format, flagging deviations. That is precisely the kind of work of which parts are already being taken over by AI today in some companies, but not yet in others. The difference does not lie in the sector or the size of the company, but in whether the underlying data has already been recorded in a structured and unambiguous way. Where that is the case, a system with limited oversight can compile reports and flag deviations that an employee then assesses with a reason for approval or rejection. Where figures still originate from separate files and phone-based coordination, compiling them remains manual work, with all the dependency on the person who does it.
The interpretation of the figures — what does this deviation mean, what action follows from it — remains, in virtually all situations, human work with oversight. AI can flag a pattern; the assessment of what that pattern means for the company rests with a person. For a buyer, this distinction is relevant: a reporting process in which gathering and compiling has been handed over to a system, and in which a person only assesses and decides, is less dependent on that one person than a process in which that same person also has to figure out where the figures come from.
Three questions give an initial picture. Can someone who does not work with the figures daily compile a complete monthly report within a day using the current systems and documentation. Are the definitions of the key metrics recorded somewhere outside someone's head. And is there a fixed, repeatable moment at which reporting appears, independent of who is present that week.
Where the answer to all three is no, this driver likely weighs down the price more heavily than the figures themselves show — a buyer will then factor in a risk premium for the time and uncertainty of rebuilding this process themselves.
Improvement does not lie in more expensive software but in documentation: definitions, rhythm, and a separation between compiling figures and assessing them. Once compiling follows a fixed procedure, it becomes less dependent on who carries it out — and therefore easier to hand over to a system with oversight rather than to one person.
This driver does not stand apart from the rest. How management information is organised is connected to how processes and systems are set up, to how dependent day-to-day operations are on you as the owner, and to what succession-readiness does to the final price. The underlying question — which work in your company can genuinely be taken over by AI — is answered per task in FTE TO AI's work scan.
An assessment of this one driver says little without the other seven alongside it. The free value check consists of eight short questions, one per driver, and gives a picture of which driver is weighing down your price the most today. The full value scan, with a maturity score per driver, evidence, an owner-dependency index, and a two-year calendar toward the exit moment, is under construction.