AI’s next phase in professional services is less about speeding up isolated tasks and more about deciding how a firm will deliver value: redesigning workflows, preparing people and data, setting clear oversight, and measuring outcomes clients care about. Adoption is rising, but use alone does not show that a firm has integrated AI into its core work or gained consistent benefits.
AI adoption is rising, but use is not the same as transformation
The Thomson Reuters Institute’s 2026 AI in Professional Services Report, based on a survey of more than 1,500 professionals, found that 40% said their organizations used generative AI, up from 22% in 2025. More than 80% of current users said they used it weekly, and more than 90% expected it to become central to workflow within five years. Those last figures describe current-user behavior and expectations, not completed firm-wide integration.
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Agentic AI—systems designed to carry out multistep work with some autonomy—was less established: 15% of surveyed organizations reported using it, while a further 53% said they were planning or considering it. Intention is not deployment, and deployment is not evidence that a system can safely own professional decisions.
The survey figures describe respondents, not every firm. They should also be read alongside the UK Department for Science, Innovation and Technology’s country-specific 2026 AI Adoption Plan: drawing on ONS data, it reports that 43.4% of UK professional and business services firms used AI in December 2025, compared with 31.4% in December 2024. The survey populations, geographies, and measures differ, so these rates are not directly interchangeable.
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Why faster tasks do not automatically make a better firm
A professional may use AI to draft, summarize, classify, or search more quickly, yet the surrounding process can remain slow: information may arrive in inconsistent formats, systems may not connect, review may be unclear, or staff may need to redo output that does not meet client or regulatory requirements. If an organization automates one step without rethinking the handoffs around it, it can accelerate a task without improving the end-to-end service.
The UK plan identifies expertise, safety and transparency concerns, implementation cost, data readiness, monitoring, and limited process redesign as adoption barriers. It also notes gaps between larger and smaller firms. These constraints help explain why individual experimentation may not translate into organization-wide gains: firms need to make the work, systems, and responsibilities fit together.
Measurement is another weak link. In a Thomson Reuters Institute analysis of its 2026 survey, only 18% of respondents said their organization tracked AI return on investment. Where firms did measure, the focus tended toward operational metrics rather than broader outcomes such as client satisfaction or revenue. Counting prompts, licenses, or time saved can help diagnose usage, but those measures do not by themselves establish that work is more accurate, useful, or valuable to clients.
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The Thomson Reuters Future of Professionals Report 2026 frames a possible “AI to Elevate” future: AI handles groundwork while human expertise remains central to judgment, relationships, and strategic thinking. In practice, firms can lean toward higher throughput, expert-led service, or a deliberate mix. These are strategic directions, not mutually exclusive prescriptions; the right balance depends on the client promise and the work involved.
| Decision area | Throughput and efficiency emphasis | Expert-led service emphasis |
|---|---|---|
| Client value | Quicker delivery, greater capacity, or lower-cost routine output | More responsive advice, deeper judgment, or tailored recommendations |
| Workflow design | Use AI to accelerate recurring steps, while redesigning the process where handoffs or rework limit gains | Use AI for preparation and analysis so professionals can focus on interpretation and client interaction |
| Accountability | Define who checks outputs and handles exceptions before increasing volume | Keep a qualified professional responsible for advice and consequential decisions |
| Readiness | Prioritize repeatable processes, reliable data, integration, monitoring, and staff capability | Prioritize dependable information, context, review, and the skills to turn analysis into advice |
| Commercial model | Consider how greater capacity or faster delivery changes fees and value capture | Consider how clients recognize and pay for judgment, responsiveness, and strategic counsel |
| Measures | Track cycle time and capacity alongside quality, risk, and client experience | Track advice quality, client satisfaction, risk, revenue, and professional development |
An anonymous VAT Audit Specialist at a UK tax and audit firm, quoted in the Thomson Reuters Future of Professionals Report 2026, put the choice this way: “You have to pick a lane because running a high-volume efficiency machine takes a completely different setup than a premium consulting boutique.” The point is not that a firm must choose one model for every service. It is that the operating design, skills, controls, and economics should match the value it intends to offer.
Governance and client communication are part of the service
AI use can create inconsistent expectations between a firm and its clients. The Thomson Reuters Institute’s 2026 analysis reports that 40% of firm respondents had received conflicting client instructions about using AI on matters. A firm may need to clarify what uses are allowed, what information can be entered into approved systems, when AI assistance is disclosed, and how a client can raise a concern. These policies are most useful when translated into matter-level guidance rather than left as abstract principles.
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For each workflow, leaders should determine which outputs require review, who can approve or reject them, how exceptions are escalated, and what evidence is retained. Human oversight should be meaningful: reviewers need enough expertise, time, and access to underlying information to evaluate an AI-assisted result. The professional who signs off on advice or a consequential decision remains accountable for it; a model’s output does not transfer that responsibility.
Evaluation should also fit the work. A pilot can compare a defined process before and after AI assistance, recording not just speed but correction rates, completeness, client impact, risks, and staff effort. Firms can then decide whether to expand, adjust, or stop the workflow. A gain in one metric is not automatically a gain overall—for example, faster first drafts may be offset by greater review time or more errors.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Work will change, but exposure estimates are not layoff forecasts
The UK government’s 2026 plan estimates that 13.7% of professional and business services roles are at risk of substitution and a further 52.8% are likely to be significantly augmented. These are estimates of exposure to AI-related change, not predictions that those shares of jobs will disappear. The plan also points to emerging categories such as lawtech, accountancytech, HRtech, proptech, and regtech.
For firms, the practical workforce question is which tasks and capabilities will shift. Staff may need training to validate outputs, manage AI-enabled processes, interpret evidence, protect confidential information, and devote more time to client judgment. Workforce planning should therefore consider redesigned roles and skill development as well as tasks that could be automated. Human expertise remains important wherever context, relationships, professional standards, or accountability shape the outcome.
Sector productivity figures also warrant care. PwC’s 2026 Global AI Jobs Barometer reports 21% productivity growth in professional services over 2018–2025, measuring productivity as turnover per employee from ORBIS company data; it uses 2025 data where available and 2024 otherwise, then aggregates company-level results. That statistic does not isolate AI as the cause of the growth.
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- Specify the client outcome. Name what should improve—such as turnaround, access to advice, consistency, or the depth of analysis—before selecting a tool or workflow.
- Map the whole process. Identify inputs, handoffs, review points, exceptions, and current sources of delay or rework. Decide whether AI should assist an existing step or whether the process itself needs redesign.
- Check readiness and risk. Assess data quality and permissions, system integration, information handling, staff capability, monitoring, and the consequences of an incorrect result. Set a boundary for human review and escalation.
- Run a bounded pilot. Select a suitable workflow, define a baseline and success measures, and make clear who owns the pilot and its outputs. Include quality, client impact, risk, and workforce effects in the evaluation, not only speed or usage.
- Decide whether to scale. Expand only when results support the intended client value and the firm can sustain the controls, training, and process changes involved. If not, revise the workflow or stop it.
- Review the service model. Revisit how the firm prices, communicates, and differentiates the service as AI changes the work. The available evidence raises these as strategic questions but does not establish one commercial model as the winner.
In a Thomson Reuters survey of 1,816 professionals across 62 countries, conducted in March and April 2026, respondents described a future in which AI supports work while human expertise stays central. The strategic task for a firm is to make that relationship concrete: decide what clients should gain, redesign the work to deliver it, and build accountability and measurement into the process.
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