Market position was long assessed on scale: market share, brand recognition, the number of account managers maintaining relationships. A buyer still counts that, but factors in something else as well. He wonders how much of that position runs on people who repeat the same work daily — compiling quotes, comparing prices, producing content, answering customer questions — and how much rests on something that cannot simply be copied: a network, a brand, a position in a chain that does not shift the moment a competitor performs a task faster.
The reason is not complicated. Work that AI takes over, a competitor can take over too. A market position that mainly consists of speed and volume in tasks that can now be done partly or fully by AI is less defensible than a position built on trust, exclusive access, or knowledge that cannot be captured in a task. A buyer no longer just counts how many people staff the position. He counts what remains when the repeatable part of that work falls away.
The difference is visible between companies that appear to operate in the same market. One company defends its position with a sales team that mainly drafts standard quotes and follows up on prices by phone — work of which a large part can now be handled by AI with human approval. The other company defends its position with a network of relationships built over years, or with a certification that is not transferable. Both companies may have the same market share on paper. A buyer does not value them the same.
The difference between companies that already see it this way and companies that do not yet see it this way usually lies not in the sector but in how the work is organized. Where processes have been broken down into repeatable steps with a clear decision framework, it becomes quicker to see which part of the market position relies on people and which part relies on something else. Where work still sits undocumented with individuals, that distinction is vague — and the market position remains strong on paper, while a buyer, during due diligence, quickly sees that the defensibility lies somewhere other than expected.
Three questions give an initial picture.
First: what portion of revenue comes from activities that AI can take over, what portion from work in which AI collaborates with human oversight, and what portion remains human work because it runs on relationship, judgment, or trust. This is not a gut-feel estimate, but a breakdown per task — and that is precisely what the work scan from FTE TO AI answers per task.
Second: how dependent is the market position on individual people versus on the company as a whole. A position that runs on the personal relationships of one salesperson weighs differently than a position anchored in the brand or the system. This overlaps with the question of how owner dependency is measured in a company where AI collaborates, and also touches on how concentrated the customer portfolio is — covered in how customer concentration is measured in a company where AI collaborates.
Third: how recurring is the revenue that the market position generates. One-off sales to new customers weigh differently than contracts that renew automatically. That distinction is worked out in what recurring revenue is worth in a sale.
Improvement does not lie in growing market share for its own sake, but in separating what is repeatable from what distinguishes the company. That requires three steps, none of which delivers short-term results but all of which are measurable.
First: map the work behind the market position at the level of tasks, not functions. Not 'the sales department' but 'drafting quotes', 'qualifying leads', 'conducting contract negotiations'. Only at that level can you see which part is takeable over and which part is not.
Then: document precisely what the distinguishing part consists of. A network that exists only in the owner's head is, for a buyer, not a market position but a risk. Documented relationships, contracts, references, and recognizable brand value are.
Finally: factor profit quality into the same process. A market position that generates profit thanks to temporary savings on labor weighs differently than a position that generates profit thanks to a structural lead. How that distinction is substantiated is covered in how profit quality is substantiated and in how profit quality is measured in a company where AI collaborates.
If this subject touches on personnel decisions — for instance because work taken over by AI has consequences for positions — separate legal requirements apply to that, independent of this valuation.
The free value check consists of eight short questions, one per value driver, and gives an initial picture of which driver is putting the most pressure on your price today. The full value scan, with maturity score, owner dependency index, and a two-year calendar toward the exit moment, is under construction.