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AI’s next phase in professional services is not simply faster research, drafting, or analysis. It is a strategic shift: firms are deciding which client outcomes to improve, redesigning workflows around AI, and setting rules for human review, staff capability, and measurement. The technology can support that shift, but adoption figures alone do not show that a firm has integrated it well—or that it has created lasting value.
“2026 marks the strategic phase of AI — one where organizations redefine workflows, reshape value, and build AI directly into the foundation of their business strategy,” said Mike Abbott, Head of the Thomson Reuters Institute, in its 2026 AI in Professional Services Report.
How widely are professional-services organizations using AI?
Use is growing, but current use, future plans, and expectations are different measures. In the Thomson Reuters Institute’s 2026 AI in Professional Services Report, 40% of professionals surveyed said their organizations used generative AI (GenAI), up from 22% in 2025. The report covers more than 1,500 professionals in legal, tax, accounting, risk, fraud, and government sectors; its accompanying analysis describes respondents across 27 countries. More than 80% of current users said they engaged with GenAI weekly, and more than 90% expected it to become central to workflow within five years. The latter is a forecast, not an accomplished result.
Agentic AI—systems designed to carry out multi-step work with some degree of autonomy—was less established. The same report found that 15% of organizations surveyed said they used it, while another 53% said they were planning or considering it. Those figures indicate interest beyond current deployment, not that most firms already rely on agents for professional decisions.
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A UK-specific measure shows a similar upward trend, though it is not directly interchangeable with the Thomson Reuters survey. The UK Department for Science, Innovation and Technology’s 2026 AI Adoption Plan for Professional and Business Services reports that 43.4% of UK firms in the sector said they used AI in December 2025, compared with 31.4% in December 2024, citing Office for National Statistics data. Adoption varied by firm size, and the plan identifies expertise, cost, data readiness, safety and transparency, monitoring, and limited process redesign among the barriers.
Why doesn’t faster task completion automatically mean higher productivity?
An employee can use AI to complete a task sooner without changing how the rest of the work moves through a firm. If review, handoffs, approvals, client communication, or data preparation remain unchanged, time saved in one step may not translate into faster delivery or better service overall. The UK plan specifically warns that individual experimentation can speed up tasks without producing firm-wide gains when workflows and organizational design stay the same.
Scaling use therefore involves choices about the entire process, not only which model or tool to try. Firms need to consider whether information is suitable for the intended task, where outputs will be checked, how the system fits existing work, who monitors it, and what staff need to use it competently. The UK plan identifies expertise, safety and transparency, implementation cost, data readiness, and monitoring as practical adoption challenges. These are organizational requirements, not problems that model capability alone resolves.
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Usage counts can show whether people have adopted a tool, but they do not establish whether clients or the business benefit. In the Thomson Reuters Institute’s 2026 analysis, just 18% of respondents said their organization tracked AI return on investment. The analysis says measurement tended to focus on operational metrics rather than broader outcomes such as client satisfaction or revenue. A useful measurement plan can pair efficiency measures with quality, risk, client experience, revenue, and workforce-development outcomes that match the firm’s objective.
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Set expectations with clients
Technology choices can affect client trust and instructions about how work is performed. Thomson Reuters Institute reported that 40% of firm respondents had received conflicting client instructions about AI use on matters. That finding makes client communication a practical part of adoption: firms need to understand applicable instructions, explain their approach clearly, and establish who is responsible for decisions and deliverables.
What kind of service model should a firm build?
There is no evidence that one AI-enabled service model will suit every professional-services firm. Thomson Reuters’ 2026 Future of Professionals Report frames two useful strategic poles: using AI to improve throughput and efficiency, or using it to elevate expert-led service. They can coexist across a firm, but they imply different workflow, client, and commercial choices.
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| Strategic direction | What the firm prioritizes | What must be designed |
|---|---|---|
| Higher-volume efficiency | Faster or lower-cost delivery of repeatable work | Standardized processes, suitable data, clear review points, and measures of throughput alongside quality and risk |
| AI-supported expert-led service | More responsive advice, deeper judgment, and stronger client relationships | Ways for professionals to use AI-supported groundwork while retaining time and accountability for interpretation, strategy, and client needs |
A VAT audit specialist at a UK tax and audit firm, quoted anonymously as a survey respondent in the Future of Professionals Report, put the difference 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.” This is a respondent’s view, not a finding that firms must choose only one model. The practical question is which client outcome the workflow is meant to improve—and whether the operating model supports it.
What do productivity and job-exposure figures actually tell us?
Sector statistics can provide context, but they should not be treated as proof that AI caused a particular result or as precise forecasts for an individual firm or worker.
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- Productivity: PwC’s 2026 Global AI Jobs Barometer reports 21% productivity growth in professional services over 2018–2025. PwC calculates this as turnover per employee using ORBIS company data, using 2025 data where available and 2024 otherwise, and aggregating company-level results. The figure does not isolate AI as the cause of that growth.
- Role exposure: The UK Department for Science, Innovation and Technology’s 2026 plan estimates that 13.7% of UK professional and business services roles are at risk of substitution and a further 52.8% are likely to be significantly augmented. These are exposure estimates, not certain job losses or guarantees about how any particular role will change.
The UK plan also points to AI-enabled categories such as lawtech, accountancytech, HRtech, proptech, and regtech. Together, these signals make workforce capability and role design important parts of adoption. They do not settle how staffing, responsibilities, or professional identity will change in every organization.
Human expertise remains central in the future-of-work scenario described by Thomson Reuters: AI handles groundwork while professionals contribute judgment, relationships, and strategic thinking. That framing does not mean every task must remain human-led. It does mean that automating steps is different from transferring accountability for consequential professional decisions to a system.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What should leaders decide before expanding AI use?
Moving from isolated experiments to a deliberate operating approach starts with a small number of concrete decisions. Treat them as linked: the intended client outcome determines which workflow should change, which in turn determines the controls, skills, and measures required.
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A free scan shows the junk files, broken settings and background clutter dragging Windows down - then fixes them in one click.Free scan · Windows 10 & 11- Name the outcome. Decide whether the priority is faster delivery, lower cost, improved quality, more responsive advice, or another client or business result. Avoid using adoption itself as the objective.
- Choose the workflow. Map the work from intake through delivery and identify where AI could change it, including handoffs and review. Decide whether to automate an existing step or redesign a larger process.
- Assign responsibility. Define who reviews AI-supported work, who can approve a decision, and who remains accountable to the client. Make those boundaries understandable to staff and clients.
- Check readiness. Assess data suitability, process consistency, system fit, monitoring, security needs, and staff capability for the intended use. Address gaps before expanding beyond a controlled use case.
- Set measures in advance. Track the operational result, but also choose relevant indicators for quality, client satisfaction, risk, revenue, or workforce development. Compare results with a baseline rather than assuming that frequent use equals value.
- Revisit the service proposition. Consider whether the change supports a higher-volume model, expert-led advice, or a combination, and how fees and value capture might need to adapt. The available evidence identifies commercial-model change as a strategic question; it does not establish a winning pricing approach.
The Thomson Reuters Future of Professionals Report is based on 1,816 professionals surveyed in March and April 2026 across 62 countries and fields including law, tax, audit, accounting, compliance, risk, and global trade. Its “AI to Elevate” scenario is a useful way to frame the strategic question: use AI to handle groundwork while preserving space for human judgment and relationships. Whether that becomes a firm’s reality depends on the workflows, responsibilities, capabilities, and outcomes it deliberately builds around the technology.
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