At the WU-TA Advanced Transfer Pricing Programme in Singapore this week, PwC's transfer pricing partners presented AI-based comparable screening as a significant efficiency gain for transfer pricing teams, and also set out its limits. One partner compared AI to a GPS: given the wrong address, it will still get you there efficiently, but a human has to recognise that the destination itself is wrong. A tax head from P&G compared AI's output to that of a junior associate: useful for a first draft, but requiring supervision, risk assessment and quality control before anyone relies on it.
Comparable screening once meant examining databases or company websites one company at a time. AI tools can now gather that information simultaneously, cutting the search stage substantially. Participants at the conference did not dispute that this is a genuine efficiency gain. The question from an Indian perspective is what that gain addresses within the broader TP dispute process.
Indian transfer pricing litigation in 2026 continues to turn on characterization issues rather than comparable selection. The Delhi ITAT's ruling this week in Discovery Communication India illustrates this. The taxpayer had adopted TNMM, its margin came in above the comparables' average, and the TPO did not challenge the comparable set.
The dispute concerned whether the TPO could carve out AMP expenditure already included within the accepted operating cost base, treat it as a separate international transaction, and re-price it using the Bright Line Test. The Tribunal rejected this approach, following the line of Delhi High Court and ITAT precedent in Sony India, Casio, and the Special Bench ruling in LG Electronics. The reasoning turned on the terms of the intercompany agreement, whether a genuine cost-sharing arrangement existed, and whether the expenditure benefited the taxpayer or the foreign AE. Comparable benchmarking played no part in this analysis.
Much of Indian TP litigation that consumes years and significant tax amounts follows this pattern: captive power CUP arguments, GCC or FAR mischaracterization, receivables treated as separate transactions, and tested-party selection disputes. These are disputes about judgment on facts, not about identifying additional comparable companies.
AI-based comparable screening does not reach this category of dispute. If the contested, revenue-significant disputes in India are predominantly characterization disputes, concerning whether a transaction exists at all, whether an entity is genuinely a captive, or whether expenditure qualifies as AMP, a tool that shortens the comparable search by some weeks does not touch the stage of the process where litigation risk is concentrated.
Such tools make the uncontested, mechanical part of TP documentation cheaper and faster, which has value. That is a different claim from suggesting AI will reduce TP disputes or TP risk, a claim made in vendor marketing and at conference panels, including at the Singapore event. The P&G comparison of AI to a junior associate acknowledges this limitation.
The GPS comparison makes the same point more directly. It concedes that AI's efficiency is irrelevant, and potentially unhelpful, if the underlying functional characterization (the "address") is wrong, and that getting the characterization right remains a human judgment task.
The implication for Indian tax administration concerns where AI-based risk scoring may next be deployed. Tax administrations have tended to adopt AI tools some time after industry does, and CBDT or TPOs could deploy similar AI benchmarking tools to accelerate comparable searches and sharpen risk-based case selection.
If AI's comparative advantage stops where functional characterization begins, as the Singapore discussion suggested, AI-assisted TPOs may build adjustment cases faster on the same mechanical grounds that are already failing in the Tribunals, without improving their ability to predict which cases will survive judicial scrutiny on characterization. Whether any firm or tax administration is developing an AI model trained specifically on characterization outcomes, such as AMP treatment, captive recharacterization, or DEMPE allocation, rather than on comparable company financials, was not evident from the Singapore discussion.
For TP practitioners, the practical implication is to assess AI tools by the stage of the process they improve. Faster comparable searches do not reduce exposure on characterization issues, which continue to require substantive factual and legal analysis, and documentation strategy should treat that distinction as material.
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