A recent Tax Notes article on a Turkish transfer pricing case traces the dispute through to its downstream effect on a U.S. foreign tax credit claim. In doing so, it sets out an asymmetry that deserves more attention in transfer pricing practice: Turkey regulates how professional advisors may use generative AI in giving tax advice, but places no comparable constraint on its own tax administration's use of machine analysis for risk-scoring, flagging related-party anomalies, or selecting cases for audit. The rules on the taxpayer side are specific and restrictive. The rules on the authority side are largely unwritten.
The same week the Turkey piece appeared, a Manila-based practitioner column described how the Philippines' Bureau of Internal Revenue has operationalised a system-assisted, risk-based audit selection framework under Revenue Memorandum Order 1-2026. The indicators are aimed squarely at related-party transactions: persistent losses against strong revenue, tax-to-sales ratios that look too low, and heavy reliance on a single related counterparty. Under this framework, transfer pricing risk is pre-flagged by the system before an examiner opens the file. Read together with Turkey's dual posture, restrictive on taxpayer-side AI reliance and expansive on authority-side machine analysis, the two examples suggest the asymmetry is not confined to one jurisdiction.
The same question arises for anyone advising Indian multinationals or GCCs. India's safe harbour election process is moving toward an automated, rules-based model, CBDT's compliance apparatus already uses AI-assisted risk profiling, and the APA and audit infrastructure is becoming steadily more analytics-driven. All of this sits on the authority side of the same asymmetry described in the Turkish and Philippine examples. Several Big 4 and boutique TP technology vendors have published material this year describing increased use of generative AI in preparing local files, benchmarking memoranda, and functional analyses. Whether that assistance will be treated on the same footing as a signed opinion from a human expert, when the question becomes whether a position was taken in good faith or whether a reasonable-cause defence survives scrutiny, is not yet settled by any rule or ruling in India.
The Turkish case does not resolve this question. Its value lies in naming the gap explicitly, rather than treating AI in tax administration as a single undifferentiated trend of efficiency gains for everyone. Whether the standards governing reliance, documentation, and reasonable cause will develop in step for both tax authorities and taxpayers, or whether taxpayers will end up defending AI-informed positions against AI-generated risk scores under rules drafted for one-sided use, remains open.
For Indian practitioners, the practical implication is to document how AI tools are used in preparing local files, benchmarking analyses, and functional analyses now, before the question is tested in audit or litigation, so that the basis for any position can be explained and defended independently of the tool used to generate it.
