In professional services, most money is earned with people's time: research, drafting, reporting, coordinating, reviewing. Part of those hours is substantive work requiring experience and judgment. Another part is preparatory or repetitive: gathering information, drafting a first version, checking a file against standard points, structuring a report. Those two kinds of work often sit in the same role, with the same person, in the same week. That makes the sector different from a production environment where tasks are physical and separable: here you have to work out, within a single function, which part of the work is tied to a task and which part is tied to the person.
What drives the outcome is not the size of the company but the extent to which work has already been captured in a process, a template or a system. A firm where files, checklists and report formats are largely built up in the same way has different work on hand than a firm where every file is reinvented depending on who handles it.
AI is already taking over work in parts of professional services. Not as an announcement, but visible in the way a portion of firms prepare files, have reports drafted or have first analyses produced. Three categories run through almost every role:
Where the difference between companies comes from is not a matter of chance. Firms where work has already been broken down into recognizable steps can more easily identify and hand off that first category. Firms where work largely resides in the heads of a few people first have to make that work visible before anything can be identified. Making it visible is itself already a step in building value, separate from what happens with AI afterward.
A buyer does not buy the number of employees, but what keeps running without them. If work that previously cost hours is now completed in less time or with less oversight, that changes the weight of every driver, not just the one for efficiency.
Owner dependency weighs most heavily. If the only person who truly knows how a file fits together is the owner, that remains a risk regardless of what AI takes over at the execution level. What succession readiness means for the price a buyer is willing to pay shows how that dependency is weighed separately from revenue.
Profit quality changes in character. A saving that arises because one specific system or one supplier takes over the work counts differently for a buyer than a saving that is embedded in the process itself and therefore transfers along in a handover. Recurring revenue, client concentration and the legal grounding of contracts and files also remain drivers independent of AI; which legal matters need to be in order before a sale process starts describes what a buyer routinely asks about.
The comparison with other sectors makes the pattern clearer. In education and the agricultural sector, the share of transferable work differs from that in services, and in retail and hospitality operational continuity weighs more heavily than knowledge transfer: see how that plays out in the analysis for businesses in education, in the analysis for retail, in the analysis for hospitality businesses and in the analysis for the agricultural sector.
This page describes which work is transferable and which work is tied to individuals; it does not describe what an employer should do with that outcome. Decisions about staff and employment contracts are subject to their own legal requirements and are not part of a value assessment. What counts here is capacity: how much fte capacity is freed up or shifted when certain tasks are relocated elsewhere, and what that means for how a buyer views the company.
The underlying question — which work in this company can truly be taken over by AI, and which part remains human work — is answered per task using the work scan from FTE TO AI.
To see which of the eight value drivers is weighing most heavily on your price today, there is a free value check: eight short questions, one per driver, giving an immediate picture of where the greatest pressure lies. The full value scan, with a maturity score per driver, an owner-dependency index and a two-year calendar toward the exit moment, is under construction.