SAP Labs Ruling on Standard Sets and Its Implications for AI-Assisted Benchmarking

Transfer pricing practitioners frequently encounter this pattern: a taxpayer builds a comparable set, the TPO rejects most of it, and the replacement set resembles the set the TPO has used in assessments of similar taxpayers. The Karnataka High Court's ruling in the SAP Labs India appeals addressed this practice directly. The Court held that a TPO cannot reject a taxpayer's comparables merely to substitute a standard set of comparables routinely used by the Income Tax Department, and that the comparability exercise must be tied to the particular international transaction and conform strictly to Rule 10B. The Court also closed off another common approach, holding that the plus or minus 5% tolerance range under Section 92C is a threshold for determining when no adjustment is required, not an automatic deduction from the arithmetic mean once a transaction falls outside it.

Considered solely as a case about TPO conduct, this ruling restates a familiar principle: tribunals have long required that FAR analysis be conducted properly. Its significance is heightened by timing. A small but growing set of commercial platforms, including Tessera, ArmsLength AI, TPGenie and others, now offer a workflow similar to the one the Court found impermissible, without the government's involvement. These vendors market consistency: a fixed, repeatable accept or reject logic applied to a comparable-set export, uniformly across each row, with human review limited to exceptions. Vendors advertise measurable gains, including claims of freeing up preparation time substantially and achieving high accuracy rates on automated accept or reject decisions. The feature underlying this pitch, a single decision logic applied consistently across every candidate company, closely resembles the feature the Karnataka High Court held a TPO cannot rely on when substituting a standard set for taxpayer-specific analysis.

This ruling does not hold that AI-driven benchmarking is impermissible. Nothing in it addresses AI, and no Indian tribunal has yet evaluated an AI-generated comparable set on its own terms. Its relevance lies in the doctrinal language now available for testing a benchmarking study whose selection logic was a repeatable template rather than transaction-specific FAR judgment. A taxpayer whose TP study relied substantially on an automated accept or reject pass, and whose audit trail records only which template rule fired for which company, may find that this traceability does not, on its own, demonstrate that the comparable search was tailored to the taxpayer's controlled transaction. The same risk applies to the Department: if a TPO's office uses AI-assisted searches that effectively reconstitute the Department's familiar standard set under a different label, taxpayer's counsel could cite SAP Labs against that approach.

Practitioners advising on AI-assisted benchmarking need to consider what a defensible workflow looks like under this standard. One possibility is that a traceable, overridable accept or reject log is sufficient, provided a human reviewer documents transaction-specific reasoning for the final set. Another possibility is that the underlying decision logic itself must be shown to respond to the specific FAR profile of the tested party, rather than being applied uniformly across an unrelated population of candidate comparables. No case has tested this question yet, and vendors are unlikely to raise it themselves. Firms advising clients on adopting AI benchmarking tools, or defending a study built using one, would be well advised to build in an explicit, documented step where a reviewer records why the FAR profile of the tested party justified each inclusion or exclusion, independent of what the algorithm flagged, and to treat AI output as a first-pass screen rather than as the analysis itself.

Although the SAP Labs ruling does not mention AI, its reasoning on standard sets and transaction-specific comparability is likely to inform how AI-assisted benchmarking studies are tested going forward. Practitioners should document the human judgment behind each comparable decision accordingly, rather than relying on the traceability of an automated log as a substitute for that judgment.

Agentic AI in Transfer Pricing: The Practical Problem Is the Handoff Between Agents

Discussion of AI and transfer pricing has largely centred on whether a single autonomous agent could take a set of intercompany agreements, run a functional analysis, select comparables and produce a defensible benchmarking range with minimal human involvement. Vendors market toward that capability, and practitioner panels debate whether such an agent could meet the reliability standards implicit in Section 92C or Section 482. A hackathon held in Vienna earlier this year, organised with the WU Tax Law Technology Center, Microsoft and TPA Global and reported only this week, points to a different pattern taking shape in practice. Rather than building one model to perform the entire task, participating teams chained together several narrower agents, each handling a bounded function.

The case studies covered intra-group financing and intercompany services: arm's length interest rates, creditworthiness assessment, the benefit test, cost allocation, method selection and documentation. Teams built separate agents for data extraction, service classification, benefit testing, cost allocation, compliance monitoring, documentation and audit readiness, and linked them into a workflow. The organisers were explicit that the intent is not to replace professional judgment: outputs are meant to remain traceable to source, reviewed before use, and subject to human oversight at each step. This combination of decomposition and human-in-the-loop review appears to be the practical model emerging from the exercise, even as vendor marketing continues to emphasise single-agent capability.

This distinction matters for Indian TP practice because the architecture of contemporaneous documentation, under Rule 10D, the erstwhile Form 3CEB and now Form 48 under the 2025 Act, assumes a single preparer's judgment trail. A TPO can ask why a particular comparable was included and expect an answer from one analyst or one firm. A pipeline of five narrow agents does not fit that assumption. If a data-extraction agent misclassifies a transaction, a downstream benefit-test agent may inherit that error and proceed regardless, since it is not designed to question upstream inputs, only to execute its own task. The more likely failure mode in a multi-agent TP workflow is not a single agent producing a wrong answer, but an error propagating silently across a handoff that no one is specifically assigned to audit. India's TP documentation requirements, safe harbour disclosures and APA application forms do not currently address this scenario; they assume one preparer whose competence and good faith can be tested under cross-examination or TPO scrutiny.

Practitioners, and possibly CBDT, will need to consider what a chain-of-custody requirement for multi-agent TP work product should look like before a dispute forces the issue. Knowing that a benchmarking output traces back to a database source, as most current vendor claims are framed, is not sufficient. It would also require knowing which agent touched the data at each stage, what it changed or flagged, and whether a human actually reviewed the boundary between two agents' work rather than only the final output. India is already working through related questions, such as how DEMPE functions performed by AI systems fit within the intangibles framework, and whether GCC functional segmentation holds up against agentic AI restructuring inside captive centres. The handoff-audit issue sits a level below those debates: it concerns not whether AI can perform a TP function defensibly, but whether anyone can reconstruct, after the fact, which of several AI agents was responsible when something went wrong. Given how current documentation standards are framed, that question is more likely to surface first in a TPO's show-cause notice than in a policy paper, and practitioners relying on multi-agent tools would do well to build their own audit trail across agent handoffs before that happens.

India's New GCC Benchmarking Advice Meets an Agentic AI Problem It Has Not Addressed

For several years, the standard defensive approach for a captive Global Capability Centre (GCC) facing an Indian Transfer Pricing Officer (TPO) has been fairly mechanical: apply TNMM on an operating cost base, benchmark against routine service-provider comparables, and settle within the accepted cost-plus range. A jurisdiction briefing circulated this week for International Tax Review describes FY2025-26 as a year in which TPO scrutiny of GCC margins and intra-group services intensified, with officers moving toward fewer but deeper adjustments. The advisory response is to segment the GCC into three separately tested activities, namely support, delivery, and decision-rights, with each priced on its own terms rather than blended into a single entity-level margin.

This approach aligns with the OECD's current work at the multilateral level. The OECD has published the full set of public comments on its proposed rewrite of Chapter VII, which governs intra-group services, ahead of a consultation meeting scheduled for November in Paris. Practitioner submissions describe the draft as a substantive rewrite rather than a tidy-up: it requires accurate delineation of what was actually done, by whom, and under what conduct, as the necessary first step before any pricing method is chosen, and it expands the benefit test that has long been the fault line in service fee disputes. Read together, the Indian advisory and the OECD draft point in the same direction: MNEs are being asked to stop pricing services as a single blended category and instead demonstrate, activity by activity, that a real economic function occurred and that an independent party would have paid for it.

This advice may be harder to execute than it appears, not because of documentation gaps in the traditional sense but because of a shift underway in how GCCs operate. India's GCCs are moving away from task-by-task human execution toward agentic AI systems that plan and execute multi-step workflows autonomously. Industry commentary has described Google's move from single-task AI assistants to an agent platform that can be deployed, supervised, and audited across a company's systems as a development that could affect the labour-arbitrage economics on which the GCC sector was built. Separately, EY's GCC survey work finds a large majority of centres already testing agentic technology, with over half piloting agent-based systems specifically. Inside a GCC, this means a workflow once visibly split between a junior analyst performing support work and a senior lead exercising decision-rights judgment can now be executed end-to-end by a single agent, or by a human-agent pair, in a manner not observable from outside the system the way an organisation chart or job description made it observable five years ago.

The Indian TP advisory recommends segmenting the GCC into three tested activities before benchmarking. The OECD requires accurate delineation of the transaction before pricing it. Both instructions assume a documentable boundary between routine support and higher-value decision-making that a TPO or comparability analyst can observe and test. Agentic AI does not necessarily respect that boundary. If an exception-handling agent inside a GCC performs functions that combine what used to be tier-1 support and tier-2 judgment, the functional analysis section of the Local File, which describes who does what, would either need to become considerably more granular about which agent or human made a given call, or risk reverting to the blended, entity-level treatment that both the OECD and Indian practice are trying to move away from.

The practical question is not whether AI adoption in GCCs is occurring; the survey data and industry commentary indicate that it is. It is whether the functional-segmentation and accurate-delineation frameworks currently being refined by the OECD and recommended by Indian advisors were designed for a labour model that may be changing faster than the guidance can be finalised. If accurate delineation depends on establishing what was actually done and by whom, and 'whom' increasingly means a shifting combination of humans and autonomous agents operating across functional boundaries that were previously organisationally distinct, this raises a documentation question that neither the OECD's November consultation agenda nor the Indian safe harbour and Local File templates currently address in detail. For practitioners, the practical implication is to start building functional analyses capable of tracking agent-level activity now, rather than waiting for the guidance to catch up.

AI-Performed DEMPE Functions and the Limits of Transfer Pricing's Intangibles Framework

The OECD's DEMPE framework was designed to stop groups from parking valuable intangibles in a low-tax entity that holds legal title but performs none of the underlying work. The test traces the relevant functions, development, enhancement, maintenance, protection and exploitation, back to the people who exercise judgment over them: who decided to pursue a research direction, who approved a patent filing, who manages commercialisation risk. For two decades this test has worked reasonably well because those functions were, in fact, performed by identifiable people.

That assumption is being tested where AI systems perform DEMPE functions. Commentary in Australia has raised this issue in relation to the ATO's Practical Compliance Guideline 2024/1 on intangibles migration, which requires multinationals to self-assess and disclose where DEMPE activities for offshore-held intangibles actually occur, using a risk-zone framework that determines how closely the ATO will scrutinise a taxpayer's position. The commentary notes that the PCG's evidence requirements assume human decision-makers, leaving a gap where AI systems (training models, tuning algorithms, monitoring outputs, iterating on protection measures) perform these functions without a person who can be identified as the locus of judgment.

A companion piece extends this analysis to M&A due diligence. It argues that AI systems, including the algorithms, training data and infrastructure, are themselves valuable intangibles under transfer pricing principles, and that a target lacking DEMPE documentation for its AI footprint may carry dormant tax risk that an acquirer inherits on completion.

This is not confined to Australia, and it is not a distant issue for India. India's global capability centre sector has moved well beyond routine coding and support work. Commentary directed at India's GCC market has told clients that where a captive centre designs a core AI algorithm used across the global group, arm's-length pricing cannot be justified by comparing developer hourly rates; the pricing must reflect that creative, value-creating contribution. That is a DEMPE argument, framed in language close to the ATO's, applied to the kind of AI-first GCC that has become common in Bengaluru, Hyderabad and Pune over the last two years.

The timing adds to the difficulty. The Finance Act 2026 safe-harbour rationalisation consolidated software, ITeS, KPO and contract R&D into a single IT-services category, set a flat 15.5% margin, and raised the eligible transaction threshold to ₹2,000 crore. The change was presented as compliance simplification for routine captive units. A captive unit whose engineers are training or fine-tuning a model whose commercial exploitation happens offshore may not be 'routine' in the DEMPE sense, even if it fits comfortably within the new safe-harbour band. A group that elects into the safe harbour for five years on the strength of a routine cost-plus characterisation may be locking in a margin that a DEMPE-consistent functional analysis would not support. That exposure may surface only when the safe-harbour election lapses and a TPO examines what the unit was actually doing.

This raises a harder question about the DEMPE test itself. DEMPE and the 'significant people functions' concept exist to identify who exercises judgment over value creation. Where that judgment is distributed across a model's training run, a human supervisor who approves outputs in bulk, and an infrastructure team in a different jurisdiction, none of these resembles the risk-bearing decision-maker the framework was written for. Tribunals and tax authorities have decades of practice tracing DEMPE to people, but little developed practice for tracing it to a system.

Whether CBDT, the ITAT or the OECD develops a workable answer first, and whether India follows the ATO's disclosure-and-self-assessment approach or adopts something more prescriptive, remains unclear. For now, practitioners advising GCCs and AI-heavy captives should treat DEMPE documentation for AI-performed functions as a present compliance issue, and should examine whether a safe-harbour election understates the functional profile of a unit that is doing more than routine service delivery.

Quantifying AI's Error Rate in Transfer Pricing Benchmarking

A study in The International Tax Journal tested multi-agent AI systems against simulated data covering twenty MNE cases across three sectors, comparing deployment models across eleven countries. Agentic AI cut processing time by 98% and performed deeper functional analysis than a human team in the same window. The study also recorded a 15% error rate in complex functional characterizations, a 22% irrelevance rate in comparable selection, and a 15% hallucination rate in legal citations.

These figures matter for Indian practice because the comparable set is the most contested element in most disputes reaching the ITAT, on turnover filters, FAR mismatches, and functional dissimilarity. A 22% irrelevance rate applied to a twelve-company set implies two or three comparables may not withstand scrutiny. A 15% citation hallucination rate is more serious, since courts elsewhere have sanctioned practitioners for AI-fabricated citations. An ABA Tax Section panel on Section 482 similarly concluded that practitioners remain responsible for method selection and defensibility despite AI assistance.

Until verification protocols match these error rates, AI-assisted local files and benchmarking studies warrant the same partner-level scrutiny as any junior draft, if not more.pemesan

Ingram Micro: TPO's PE Finding Falls Outside a Section 92CA Reference

A Section 92CA reference is meant to test the arm's length price of a specifically identified international transaction. The Assessing Officer's reference defines that scope, and a TPO cannot expand it.

In Ingram Micro (India) Exports Pte. Ltd. v. DCIT, the AO referred the matter to the TPO based on search statements that the Indian affiliate was "carrying on the actual business" of the Singapore entity, a general assertion rather than an identified transaction. The TPO went further, holding that the assessee had a Permanent Establishment in India and raising an adjustment of about Rs. 61.32 crore, feeding a proposed assessment of roughly Rs. 72.39 crore.

The Mumbai ITAT set aside the order. It held that a TPO's jurisdiction under Section 92CA is confined to the arm's length price of a specifically identified transaction. PE existence under Article 5 and taxability of profits under Article 7 remain matters for the AO, not the TPO.

Practitioners reviewing TPO orders built on broadly worded references should examine whether the reference itself identifies an actual transaction before accepting findings made on the strength of it.

AI Washing and Transfer Pricing Benchmarking: Closing the Due Diligence Gap

Vendors pitching transfer pricing documentation software routinely describe an "AI-powered" comparability search or an "AI copilot" that drafts a local file in a fraction of the usual time. Few buyers ask what is actually doing the work: whether a large language model is screening the database for functional comparability, or whether a rules-based screen performs the filtering while the model only writes the narrative afterward. A recent ICAI article names this gap "AI washing," the practice of exaggerating or mislabelling how much genuine AI capability sits behind a product, and sets out red flags that map closely onto a TP due-diligence checklist: vague "AI-driven" claims with no named model class or training data, cherry-picked success anecdotes without a baseline or error analysis, and no documented model validation, drift monitoring, or bias testing. The article also cites the Australian Taxation Office's practice of publicly documenting its own AI use, including subjecting itself to performance audits of AI governance, as a benchmark against which both tax administrations and software vendors could be judged. Current market activity gives practitioners something concrete to test against that yardstick. TP documentation platforms are marketed with features such as "TP Copilot" and "Benchmark AI," using large language models to synthesise functional analysis data into Master and Local Files, and commentary on these rankings notes that independent "AI-native" SaaS platforms have been gaining ground against legacy Big 4 proprietary tools. At least one documentation vendor discloses where the AI stops: it states that AI drafts narrative prose while tables, statutory citations, and the arm's length range are computed in code, with the taxpayer's team required to review and sign off before filing. That kind of disclosure is what distinguishes a defensible use of AI from a washed one, but most buyers have no framework for asking the underlying question, and most vendors face no obligation to answer it unprompted.

Two regulatory developments this month sharpen the stakes. On 11 September 2026, the OECD released a Pillar Two package that establishes a formal peer-review framework to check whether a country's domestic minimum-tax legislation matches what it claims to be: a "qualified" IIR or QDMTT must survive a documented consistency check rather than rely on a self-declared label. Separately, India's transition to the Income-tax Act, 2025 replaces Form 3CEB with a new Form No. 48, which carries additional disclosures on how the arm's length price was determined. Taken together, these developments suggest regulators are moving toward requiring auditable, peer-reviewable evidence behind compliance claims, at a time when TP practice is increasingly relying on AI tools whose contribution to comparability judgments is not documented. It is plausible, though not yet confirmed, that CBDT could eventually require disclosure on a form such as the new Form 48 of whether and how AI tools contributed to the benchmarking methodology behind a reported arm's length price; taxpayers unable to answer with specifics because their vendor cannot either would be in a weak position if that requirement materialises. Until CBDT or ICAI issues more prescriptive guidance, practitioners should press vendors, and press themselves, on specifics: which model class and version was used, whether the comparable set was screened by the model or only narrated by it, whether a human reviewer overrode any AI-suggested comparable and recorded why, and whether that record is retained with the same rigour as the benchmarking study itself. Any "AI-powered" benchmarking claim, whether made by a vendor to a practitioner or by a practitioner to a TPO, is only as reliable as the audit trail behind it.