AI for consulting firms: where it pays back first

AI for consulting firms pays back first in the unglamorous middle of a project: research, first drafts, synthesis of interviews and the reuse of material the firm already owns. It pays back last, and least reliably, in the judgement work that clients actually buy. A firm that starts at the middle and protects the judgement will see a return within a quarter; a firm that starts with a grand strategy will see a slide deck.

That is a less exciting answer than most vendors give, but it is the one the evidence supports. The rest of this post sets out the evidence, the tasks to start with, what changes in how clients buy, and a sensible first ninety days.

What does the evidence say about AI for consulting firms?

The best study of the question so far is a pre-registered experiment run with 758 consultants at Boston Consulting Group, described by one of its authors, Ethan Mollick, with the full paper on SSRN. Consultants were randomly given GPT-4 or not, and asked to do eighteen tasks chosen to resemble real consulting work. On tasks inside what the model could do, those with AI finished 12.2% more tasks, worked 25.1% faster and produced results of 40% higher quality.

The same experiment contains the warning that matters more. On a task deliberately chosen to sit outside the model's competence, consultants without AI got the right answer 84% of the time; with AI, they got it right 60 to 70% of the time. They trusted a fluent answer that was wrong. The paper is a working paper and had not been peer reviewed when it was published, and the models have moved on since, so treat the exact numbers with care. The shape of the result has not been contradicted: large gains on the right tasks, and quiet damage on the wrong ones.

The practical lesson is that the return depends on sorting tasks, not on buying a tool. We argued something similar about adoption in general in Why adoption is the most important thing: capability is rarely the limit; the way a firm uses it is.

One more finding deserves attention. In the same experiment, the consultants who scored lowest before the test improved most with AI, by 43%, while the strongest improved by less. For a firm, that means the tools narrow the gap between a good first draft and an average one, and raise the premium on the person who can tell the difference. Review skill, not typing speed, becomes the scarce thing, and hiring and training should reflect it.

Which consulting tasks should use AI first?

Start where a wrong answer is cheap to spot and the work is repetitive. The table below sorts the common candidates by how quickly they tend to pay back and how closely a person needs to check the output.

Task	Payback	Checking needed
Desk research and market scans	Fast	Check every source and figure
Summarising interviews and workshop notes	Fast	Spot-check against recordings
First drafts of proposals and reports	Fast	Full edit by the author
Searching the firm's past work	Medium	Check permissions and confidentiality
Financial models and analysis	Slow	Rebuild key numbers independently
Recommendations and client advice	Slowest	Partner judgement throughout

The Management Consultancies Association's 2026 member survey fits this picture. Of the firms surveyed, 76% already use AI for research and information gathering, up 9% on the year before, and 77% have either built AI into their systems or let staff use AI models. Those figures come from a January 2026 release covering a Savanta survey of more than 1,000 consultants. Research is where almost everyone has started, which also means it is where a firm gains least relative to its competitors.

Why are clients spending less on consultants as AI spreads?

A consulting firm should not only ask what AI does for its own margins. It should ask what AI does to the thing clients are willing to pay for. City AM's Business Spend Pulse, built from card transactions at more than 300,000 British businesses, found that median spending on consulting fees fell 1.9% in August against a year earlier, while overall corporate spending rose 2.2%. One in seven businesses now pays an AI company, up from one in thirteen, and the share paying professional services firms fell by about a percentage point to 18.6%, the steepest fall of any category.

That data comes from one card provider's SMEs, so it is a signal and not a census. The direction is still uncomfortable. Where a client can get a competent first answer from a tool, the part of a consulting day that was research and drafting is worth less per hour. The same survey from the MCA found that 52% of firms see the rise of AI as the industry's leading challenge, with some uncertainty over whether clients will pay for it.

The sensible reading is not panic. It is that the billable value moves towards the parts AI does badly: knowing which question matters, knowing the client's politics, and being accountable for the recommendation. We made the wider case in AI agents are being trusted faster than they are being checked, and it applies to the firm as much as to its clients.

What does AI for a consulting firm look like in practice?

At AssumeAI, the pattern we see most often in professional services firms is not a shortage of tools. Staff are already using them, unevenly and without a shared view of what is allowed, so one person saves hours while another puts client material somewhere it should not go. The firms that get value first are the ones that decide, in plain terms, which tasks use AI, who checks the output, and what may never be pasted into a model. That is dull work, and it produces a return faster than any new software purchase.

Firms that skip it tend to find the gains are real but invisible: time saved on one project is absorbed by rework on another, because nobody agreed how to check the output. Making the saving visible, even crudely, by timing a handful of repeat tasks before and after, is what turns a feeling into a business case.

What should a consulting firm do in the first ninety days?

Firstly, list the ten tasks that consume the most junior time and sort them using the table above. Pick two from the fast column, and measure how long they take today. Second, write a one-page rule on what client information may go into which tools, and who signs off client-facing output. Third, run the two tasks with AI for six weeks, checking every output, and compare the time and the error rate with your baseline.

Finally, decide what you will do with the time you have bought. If it becomes more client conversation, better synthesis and faster turnaround, the firm gains. If it becomes simply cheaper junior hours handed to the client, the City AM figures suggest the client will notice, and the price will follow. A structured AI strategy review is one way to get through the sorting and the rules quickly, and it is worth doing before spending on tooling. You can read more about who we are and how we work on our about page.

The firms that do well from this will not be those with the most AI. They will be those that know, task by task, where it can be trusted, and who say so to their clients in plain language.