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Artificial Intelligence

AI Exposed a Bigger Pricing Problem Than the Billable Hour

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AI makes the billable hour easier to question, but it does not answer the more important question: What exactly am I paying for? In professional services, a fee can cover not just production, but also judgment, advice, client alignment, accountability and trust. AI can make some production faster without making the whole engagement less valuable. The pricing challenge is to separate those parts, show what the client received and choose a fee model that fits the work.

Why faster work puts pricing under scrutiny

When a task takes fewer hours, an hourly invoice can make the efficiency look like a smaller result. That is the tension AI exposes: a firm may deliver the same or better work in less time, while the client sees fewer billable hours and asks whether the fee should fall.

But professional-services work is not a single production task. It can include drafting or analysis alongside deciding what matters, interpreting uncertain evidence, advising on trade-offs, coordinating stakeholders and standing behind a recommendation. The time saved on repeatable production is not, by itself, a measure of the value or responsibility in the engagement.

The practical implication is not that firms should simply preserve every fee, or that clients should accept a price without explanation. Firms need to be clearer about which activities AI changes, which expertise remains essential, what outcome the work is meant to support and what evidence can be shared with the client.

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AI adoption is growing faster than proof of return

Thomson Reuters Institute’s 2026 AI in Professional Services Report, based on more than 1,500 professionals across legal, tax, accounting, risk, fraud and government, found that 40% said their organizations used generative AI, up from 22% the prior year. More than 80% of current users said they used it weekly. Yet only 18% said their organizations tracked AI return on investment, while 40% did not know whether it was measured.

That gap matters for fees. A firm that knows AI is being used but cannot estimate its effects on delivery cost, turnaround or quality has a weak basis for explaining how its pricing should change. Thomson Reuters Institute head Mike Abbott described 2026 as “the strategic phase of AI — one where organizations redefine workflows, reshape value, and build AI directly into the foundation of their business strategy.” That is a view of the shift, not proof that any particular pricing model will work.

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Client expectations are also more nuanced than a simple demand for mandatory AI use. In the same 2026 report, two-thirds of corporate respondents wanted outside firms to use AI, but fewer than 20% said they mandated it. Clients may welcome responsible use while still wanting transparency about where it is applied and how efficiencies affect the bill.

What the pricing surveys do—and do not—show

There is evidence of pressure and experimentation, but no defensible cross-industry figure in the available data for the share of professional-services revenue already shifted from hourly fees to outcome pricing. The surveys below cover different populations and measure different things; they should not be combined into a single forecast.

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Source and population Finding How to read it
Deloitte UK, 121 senior legal leaders worldwide surveyed in April and May 2026, with RSGI support 85% believed AI would change law-firm pricing. The share expecting hourly-rate work to fall was projected to move from 72% to 44% over the next two to three years. These are leaders’ expectations about a future change, not measured outcomes across law firms or professional services as a whole.
Thomson Reuters Institute, 2025 Generative AI in Professional Services Report 40% of respondents expected alternative fee arrangements to increase because of generative AI. The report also said many law-firm practitioners expected the status quo to continue, so expectations were not uniform.
Promethean Research, 2026 report on digital agencies Reported use of value-based pricing fell from 31% in 2024 to 18% in 2025. Promethean cautions that the comparison comes from a single survey wave. It is a sector-specific counterpoint, not proof of a general decline or a causal effect of AI.

Deloitte partner Tom Brunt said: “This however will increase pressure on law firms to demonstrate how AI is being used and how efficiencies are reflected in pricing, with the billable hour model facing greater scrutiny as clients demand more transparent, outcome-based approaches.” The statement captures expected client pressure; it should not be mistaken for a measured result.

Other findings illustrate why adoption and payoff need separate measurement. Grant Thornton’s 2026 AI Impact Survey reported that 57% of professional-services firms were scaling AI across functions, compared with 49% of its full sample; 50% of the professional-services firms reported measurable efficiency gains, compared with 63% of the full sample. Those top-line comparisons illustrate a gap between scaling and reported gains, not a direct finding about fees. The landing page does not provide full sampling and methodology detail.

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BILL’s fourth accounting-firm AI ambition survey volume says it drew on more than 200 accounting-firm leaders and focused on business-model and pricing innovation. Its landing page does not provide detailed results, so it cannot support a more specific claim about what those firms are doing.

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Choose the fee model to fit the work

There is no universal progression from hourly billing to fixed fees to outcome pricing, with each step automatically better than the last. Stanford Digital Economy Lab’s pricing framework centers on two questions: how observable the client outcome is, and how observable the provider’s inputs and costs are. Santiago & Company adds practical tests: can the provider influence the result, will the buyer accept the metric, and can the provider carry the downside risk?

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Fee model When it can fit Main trade-off to manage
Time-based When effort and inputs need to be tracked, or the scope is hard to predict. AI may reduce hours and therefore the fee even when the delivered result remains valuable. Clients may also have limited budget certainty.
Project or fixed fee When the scope and deliverables can be bounded clearly enough to price in advance. The firm needs a reliable cost floor and scope controls; otherwise unexpected work can erode margin or trigger disputes.
Hybrid When a predictable base fee can cover defined work while a variable component reflects a measurable result or additional scope. The parties need to agree what the variable component measures and how it is calculated. Stanford’s framework suggests hybrid structures may suit AI-enabled consulting as observability changes.
Subscription or asset-based When work is recurring, repeatable or provides continuing access to an embedded capability. The agreement should define what is included and how variable costs or changing demand are handled.
Outcome-based When the result is measurable, the provider can materially influence it and both sides accept the metric. Attribution and risk allocation can be difficult; the provider may not be able to control client decisions, market conditions or other causes of the result.

Outcome fees are especially risky when the provider is paid for a result it cannot substantially influence, or when the parties cannot distinguish its contribution from outside factors. A firm should not promise a result-based price simply because AI shortened production time. The result, causal contribution and allocation of downside all need to be workable.

Make the value visible before changing the fee

A more useful client conversation starts by unbundling the engagement. The firm can explain which tasks AI accelerates, which work still requires expert judgment, what decisions or risks it is accountable for and what artifacts or evidence the client can inspect. This shifts the discussion from “How many hours did it take?” to “What work was done, what changed, and what responsibility did the firm take on?”

For firms considering a new fee structure, a bounded pilot is a practical way to learn without assuming that a model will generalize across every service. Set the baseline and the outcome metric before delivery; track cost-to-serve and quality as well as speed; then compare the proposed price and margin with client acceptance. This is a decision method, not a claim that a particular pilot has been proven to succeed.

Contract design can matter alongside the fee. Santiago & Company argues that data rights, model governance, provenance, disclosure and liability may need attention as professional-services work changes. These are considerations in that firm’s analysis, not a settled universal checklist. Their relevance depends on the engagement, the data involved and the responsibilities the provider is taking on.

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What buyers should ask—and firms should be ready to answer

  • What is AI doing in this engagement? Identify the tasks where it changes production, rather than treating “AI-enabled” as a complete description of the service.
  • What remains the provider’s responsibility? Clarify who reviews outputs, exercises judgment, advises on decisions and stands behind the work.
  • How is the fee connected to scope or results? For a fixed or hybrid fee, define included work and change controls. For outcome pricing, agree on an observable metric, attribution and risk allocation.
  • What evidence will be available? Discuss what the client can inspect about process, quality and delivered work, subject to appropriate confidentiality and data protections.

AI is making the billable hour more contestable, but the deeper issue is whether the value inside a professional-services fee is visible and defensible. Firms need enough evidence about cost and quality to price responsibly; clients need enough clarity to understand what the fee buys. Until those conditions are met, replacing the hourly rate with an outcome fee may change the invoice without solving the pricing problem.

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