A few weeks ago I was on a video call with Daniel Kellmereit, who co-wrote The Silent Intelligence with me more than ten years ago, and we spent most of it chatting and comparing notes about genAI. How are you using it? What does it actually save you? Where does it still fall over?
We both had the same answer, more or less — at least for those of us who have gone past the chat window and built some standing setup that gives the tools real context about the work. The time savings are not subtle and they are not occasional; they show up every day — in the parts that used to take an afternoon and now take twenty minutes. And then he said something — I am paraphrasing — to the effect that the market has not caught up with AI productivity.
I have been thinking about that ever since, because it explains a strange gap I keep running into. Inside firms, the productivity is obvious to the people doing the work. Outside — not so much.
That gap has now been measured. McKinsey’s global State of AI survey, published in August 2026, found that eight in ten people using AI say it has improved their own productivity. Only 37% of organizations report that AI has contributed positively to their EBIT. And the share who attribute at least 5% of EBIT to AI and call its impact significant is 6%. Both of those organizational numbers are essentially where they sat in 2025.
That raises a question about why it takes larger firms so long to turn felt productivity into profit, and whether smaller ones are in fact doing better at it. I will come back to both in a later piece. Today I want to talk about a question I do not think our industry has answered yet: what is the business model for AI-era professional services?
Let me give an uncomfortable and somewhat exaggerated example, to make a point. Imagine a twelve-week project that, with AI doing a serious share of the production, is genuinely finished in two. The deliverables are good. The client is happy. And the firm bills twelve weeks, because that is what the proposal said, and spends the remaining ten twiddling its thumbs, figuratively speaking. And there you have it: the firm has clear savings and pockets the benefit, but the client does not see any P&L improvement.
Now imagine the client figures it out. The ones who use these tools themselves tend to develop a fairly good nose for what came out of one. Not everybody can.
The question they ask is not unreasonable. Should we still pay you the same, now that you are working with AI?
There are two versions of that question. The first is the honest one, the one I just described, and it is owed a real reply. The second arrives as an accusation — you AI’d this — and it is not really a question. It is a negotiating move, and the implication underneath it is that work which was fast to produce should be cheap to buy. Nobody ever asked for a discount because the document had been spell-checked, or because the analyst used a spreadsheet instead of a legal pad. What is different this time is that the tool can also produce a great deal of confident nonsense very quickly, and telling the good output from the bad takes real effort. A client who has spent an evening watching ChatGPT generate pages of plausible text, not all of it something you would want to rely on, may reasonably wonder whether that is what he just paid for.
How should professional services companies react to this question? There are two extreme reactions — and both of them are wrong.
The first extreme reaction is to say nothing, keep the old model, and quietly absorb the difference, which I suspect is what some firms are quietly doing, for as long as they can. This is indefensible, and in at least one profession it is explicitly against the rules. The American Bar Association’s Formal Opinion 512 tells lawyers who bill hourly that they must bill the time actually spent, including the time spent putting information into the tool and reviewing what it produced, and not the hours the task would have taken before. It goes further: a flat fee has to stay reasonable too, which means the efficiency the tool actually delivered has to be accounted for rather than quietly pocketed. That is guidance for American lawyers and it does not automatically govern anybody else’s contract. But the principle underneath it travels: if what you sold was time, you invoice the time you actually used. Phantom hours are not a pricing strategy. They are a story you are telling a client, and I think the days of the pure hourly model are numbered across a good deal of professional work.
The second extreme reaction is to hand every saved hour back as a discount, and it is wrong for a different reason. It prices the wrong thing. The real value in this work was never the hours; it was knowing which problem to solve, how to elegantly solve it, what the deliverable has to survive, and who is accountable when it does not. Understanding a client’s workflow well enough to make AI actually produce a gain for them is harder than asking a chatbot good questions — considerably harder, and in my experience a good deal rarer. The firms I think are handling this best have stopped selling hours and stopped selling AI, and started selling a named method they own — the AI is an instrument inside it, and the price attaches to knowing what to build and in what order.
So the solution is somewhere in the middle, and I want to be honest that I do not have a formula. I do not think anybody does yet. It is a process, and we are early in it.
What I do have is a way of thinking about it: a client can receive the value as a lower fee, certainly, if the service is a commodity. But they can also receive it as earlier delivery; as more scope for the same money; as better evidence behind the recommendation; as tools, skills and scripts they keep and can run themselves afterward; as capability their own team did not have before you arrived. The question is not whether to share the gain. The question is which currency the client actually values.
That suggests a test. Name what the client is buying. Capacity, a bounded deliverable, an outcome, or an ongoing capability are four different products. Make the client’s gain visible, in whichever currency it arrives. Make your own retained value legible, because expertise, method, tooling investment, quality control, and accountability are real and defensible. And choose a commercial model that matches what can actually be scoped and verified, rather than the one that sounds most modern.
That last one is where I would slow down. Outcome pricing is only credible when there’s an agreed baseline, an agreed metric, a time window, some way of verifying the result that both sides trust, and an honest rule for how much of the change you actually caused when four other things moved at the same time. If those do not exist, you have not priced an outcome. You have priced a hope, and hope, as we know, is not a strategy.
Clients want to buy outcomes. You cannot always guarantee one, though, especially when nobody ever wrote down the baseline you would be measured against. What you can sell is the process that tends to produce that kind of outcome. And phased milestones are usually the more honest instrument for it: you price the stretch of work you can actually see, you deliver it, and then you stop at a real gate and agree what the next stretch is worth.
I want to leave the contradictions standing rather than tidy them away. Efficiency should reduce what a client pays; scarce judgment and faster time-to-value may justify preserving or increasing the fee. Fixed fees reward the efficient and can still curdle into something unreasonable. All of these are true at once.
So I would rather keep asking the question: which gains belong to the client, which belong to the provider, and what evidence makes that division fair? I genuinely want to know how you are handling it.
There is an old engineering legend, usually told about Charles Proteus Steinmetz and Henry Ford, that captures the essence of the service pricing debate. Ford’s engineers could not get a generator working. So they called Steinmetz, a well known mathematician and electrical engineer of the time, and promised him ten thousand dollars — a huge sum at the time — if he fixed it. Steinmetz spent some time listening to the machine, chalked a mark on the casing, and hit it once with a hammer at the chalked spot. The generator started working immediately.
After such a quick resolution, Ford was struggling to justify the ten thousand dollars he had promised for a single blow with a hammer. So instead of pushing down on the price, he asked Steinmetz to itemize the invoice, thinking Steinmetz might not feel comfortable enough openly charging that much for such a quick and easy fix. What came back was: making chalk mark on generator, one dollar. Knowing where to make the mark, and where to hit with the hammer, nine thousand nine hundred and ninety-nine dollars.
The story is almost certainly too good to be true, and it travels under half a dozen other names. But I think about that invoice a great deal lately, because AI has made the chalk mark essentially free — and it has not taken on one ounce of the responsibility for knowing where to put it. At least not yet.