The article examines how generative artificial intelligence is beginning to transform corporate tax work in the uk by focusing on the written, judgement heavy parts of the job rather than automating calculations. Generative artificial intelligence tools are described as particularly well suited to assembling evidence, extracting meaning from dense technical texts, and producing draft explanations that can withstand scrutiny, in a profession where deadlines are fixed, workloads are seasonal, and expectations around quality and traceability are rising. Instead of replacing technical judgement, the technology speeds up first drafts, simplifies the search for prior knowledge, and reduces the effort needed to turn technical analysis into clear, reviewable documentation.
Following a typical tax workflow, the article highlights use cases across intake and triage, research, drafting, review, and knowledge reuse, arguing that the technology feels most tangible in everyday tasks such as summarising source documents, extracting key passages from guidance, and creating memos or risk notes. Citing a Thomson Reuters Institute survey of tax and accounting firm professionals, it reports that 10 percent of respondents said their firms were using generative AI at an organisation wide level, while a further 40 percent said their firms were planning or considering its use, with high intent but uneven maturity. In that survey, the most common use cases were accounting and bookkeeping, tax research, tax return preparation, tax advisory services, document review, and correspondence drafting, reinforcing that generative artificial intelligence is being applied as a drafting and support engine rather than an autonomous decision maker.
The author describes four main value drivers in tax work: document summarisation and triage, drafting of written outputs, retrieval of internal knowledge, and standardisation of language across recurring issues and policies. A realistic example is provided in which a technical note is turned into a structured two page tax memo with headings, assumptions, open questions, and a plain English summary, reducing the blank page problem while leaving the professional accountable for accuracy and evidence. This shift changes the tax professional’s role by making verification, judgement, explanation, and governance more central, as effort moves away from routine drafting toward review and quality assurance. The article also notes that generative artificial intelligence can make technical positions appear more certain than they are, which increases the need for scepticism and disciplined review, and positions tax professionals as key voices in setting rules on what data can be entered, what outputs must be reviewed, and how artificial intelligence contributions are documented.
On the risk side, the discussion focuses on hallucinations, confidentiality, and auditability. In tax, the concern is that wrong answers can sound plausible and erode review discipline, particularly under time pressure, and that narrative explanations can increase alignment with artificial intelligence recommendations rather than strengthen oversight. Confidentiality risks arise because tax data often includes commercially sensitive figures, transaction details, and legally privileged communications, making data retention and model training practices central to tool selection. Auditability is also critical, as tax conclusions require evidence trails and teams may need policies on what to retain when generative artificial intelligence assists with drafting, especially where outputs influence external communications. To manage these issues, the article proposes a minimal control playbook: using only approved tools in controlled settings, excluding client confidential or privileged inputs unless explicitly protected, insisting that sources are cited for factual or technical claims, mandating human review for client facing, filing related, or position setting work, and keeping simple records of where generative artificial intelligence was used and how outputs were checked.
Placing this in the uk context, the article links adoption to a compliance environment that is increasingly digital and efficiency driven. HMRC publishes estimates of the UK tax gap, defined as the difference between the amount of tax expected to be paid and what is actually paid, and the latest published estimate for the 2023 to 2024 tax year is 5.3 percent, or £46.8 billion, and HMRC reports it collected 94.7 percent of all tax due, which supports an official focus on compliance effectiveness and operational efficiency. At the same time, the joint online filing service for company accounts and Company Tax Returns is due to close on 31 March 2026, and government guidance directs companies towards commercial software routes thereafter, further embedding software workflows into tax compliance. The article argues that this environment amplifies the relevance of generative artificial intelligence for drafting support and standardisation inside these software mediated processes, provided governance is strong.
Looking ahead, the article counsels against hype and suggests a grounded interpretation of generative artificial intelligence as a drafting and structuring engine within a high consequence function. The most immediate effect is described as a change in time and attention, as first drafts and summaries become cheaper to produce, potentially increasing throughput and consistency if review standards are maintained. Over time, tax professionals are expected to spend more effort on judgement, verification, and clear communication, meaning governance becomes a core capability and organisations with well defined rules on permitted use, data boundaries, and review expectations will both reduce risk and build trust in adoption. The article notes that professional bodies are already issuing guidance on responsible use, and that widely discussed concepts of trustworthy artificial intelligence, such as explainability, robustness, transparency, safety, and security, align closely with what tax teams require. It concludes that generative artificial intelligence accelerates the tasks that surround tax expertise rather than substituting for that expertise, leaving responsibility for accuracy, judgement, and defensibility firmly with the human professional.
