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AI is helping junior tax practitioners work faster and take on more complex tasks. But reducing the difficult parts of tax work may also affect how professional judgement develops.
Junior practitioners can now search legislation, summarise guidance, review documents and draft technical material in a fraction of the time these tasks once required. They can also contribute to more complex work earlier, supported by tools that help them navigate unfamiliar issues.
Our February 2026 survey of 446 tax professionals from the UK and Ireland found that 84% use AI for tax research. It is also widely used for knowledge management (68%), document analysis (67%), drafting internal communications (65%) and drafting client documents (64%).
Among those using AI, 53% said they are producing work faster and 32% said the quality of their work has improved.
Junior practitioners are producing work that is value – even when they use AI – but the side effect of this transition could be far greater loss for the tax community.
Many of the tasks most suited to automation are repetitive and time-consuming.
They include tracing legislation, reviewing source material, checking calculations, reconciling balances and working through large amounts of guidance. It is understandable that tax teams would want to reduce the time spent on them.
Yet these tasks have also played an important role in professional development.
Ian Bowden, Tax Partner at BDO, explains:
“When AI handles foundational tasks, juniors naturally do less of the ‘heavy lifting’ that historically built muscle memory: tracing schedules, reconciling balances, tying out source numbers, performing manual calculations and researching in primary sources.”
That heavy lifting teaches practitioners how a calculation is constructed, how legislation fits together and where errors are likely to arise. It also exposes them to the ambiguities and practical complications that are often absent from a concise summary.
Repeated exposure to these problems helps practitioners recognise patterns. Over time, they develop an instinct for when something looks unusual, when a source needs to be challenged or when a conclusion does not sit comfortably with the facts.
Bowden warns:
“The danger is we could create a generation of professionals who can review AI output but cannot independently reconstruct the logic behind it.”
AI-generated work can appear confident, structured and complete.
For an experienced tax professional, that output may be a useful first draft. They have enough knowledge to identify missing qualifications, test assumptions and recognise when the answer needs further investigation.
A junior practitioner may not yet have that frame of reference.
Jane MacKay, Tax Partner at Crowe, says:
“There is a risk that juniors bypass foundational learning, such as navigating primary legislation, building technical structure and developing critical scepticism.”
For junior practitioners, this means the answer may arrive before the practitioner has worked through the problem.
When the structure, sources and initial conclusion are all provided by the tool, there are fewer opportunities to make mistakes, test alternatives and understand why one interpretation is stronger than another.
More than half of respondents, 54%, said AI is having a significant or severe impact on the development of reasoning and judgement among junior practitioners. Only 5% said it is having no impact.
The biggest skills gap identified was verification and source-checking, selected by 66% of respondents. Deep tax reasoning and argumentation followed at 58%.
These findings are closely connected.
A practitioner needs a reasonable understanding of the subject before they can verify an answer properly. It is difficult to challenge a conclusion if you do not understand how it should have been reached.
Paul Aplin, President of the Chartered Institute of Taxation, says:
“There is a real danger that using AI in some tasks will deprive junior staff of the very experience they will need in order to assess the accuracy and reliability of AI output when they progress to more senior roles.”
This creates a possible gap between apparent capability and genuine understanding.
A junior practitioner may be able to produce complex work earlier, but that does not necessarily mean they are ready to take responsibility for the judgement behind it.
Turn complex tax work into clearer, stronger outcomes
Tax teams do not need to preserve every manual process simply because it once formed part of the training.
The aim should be to identify which experiences remain important and make sure they are not lost.
This may mean asking junior practitioners to work through selected calculations manually, trace an AI-generated answer back to primary sources or explain how they would reconstruct the analysis without the tool.
Managers may also need to spend more time reviewing the reasoning behind a piece of work, rather than only the finished output.
Almost two-thirds of respondents, 63%, said junior practitioners should be taught to use AI as a “thinking partner” rather than a shortcut.
MacKay describes a similar approach at Crowe:
“We’ve focused on guidance, training and positioning AI as a first-pass tool rather than an answer engine.”
Used in this way, AI can support the learning process. It can suggest possible issues, compare approaches and help practitioners organise their thoughts. The junior professional still needs to assess the sources, explain the assumptions and make the final recommendation.
AI will continue to take on more of the research, drafting and analysis involved in tax work.
That should improve productivity and give junior practitioners access to more interesting work earlier in their careers. But greater exposure to complex work is not the same as greater experience.
Tax teams will need to be more deliberate about how judgement is developed.
The challenge is to retain the parts of traditional training that taught practitioners how to test an answer, recognise uncertainty and take responsibility for a conclusion, without preserving inefficient processes for their own sake.
AI can do more of the heavy lifting. Junior practitioners still need to understand what is being lifted, how it fits together and whether it can be relied upon.