The Magnifying Effect of AI on Transfer Pricing Disputes

Transfer Pricing Disputes Are Already Hard-Wired Into Multinational Business

Benjamin Franklin's old observation about death and taxes feels particularly relevant in 2026. For multinational companies, there is another recurring reality: the risk that more than one jurisdiction will seek to tax the same profit.

That risk sits at the heart of transfer pricing. Multinational profits arise across many territories, while tax authorities naturally seek to protect their own tax base. The result is an environment in which disputes—and the possibility of double taxation—are not exceptional events. They are an inherent part of operating internationally.

Yet many multinational groups still approach transfer pricing disputes reactively. The audit arrives, and only then does the scramble begin: evidence is gathered, narratives are developed and positions are reconstructed.

The better question is whether defence readiness can be built into the transfer pricing ecosystem before the dispute begins.

The Cost of Reacting After the Dispute Starts

Mutual Agreement Procedure (MAP) statistics illustrate why preparedness matters: the inventory of transfer pricing cases in MAP has increased by more than 70% since 2019 to just over 6,000 cases, with an average resolution time of around 30 months, and 13% of MAP cases fail to resolve double taxation.

Those figures reinforce a practical point for tax leaders: a transfer pricing dispute can remain unresolved for years after the underlying audit has already consumed significant management time and resources.

Historically, maintaining a continuously defence-ready transfer pricing environment was often associated with the largest and most sophisticated MNCs because it required significant advisory budgets and manual effort.

AI changes that equation.

AI Is Removing the Resource Constraint for Tax Authorities

AI gives tax authorities the ability to screen taxpayers and triangulate data at a speed and scale that was previously difficult to achieve.

Payroll information, VAT returns, customs declarations, public filings, financial reporting, Country-by-Country Reporting, Global Minimum Tax filings, investor presentations, news releases and other sources can be analysed together to form an initial view of where profit should sit.

That can change the starting point of an audit. Rather than beginning with a blank page, a tax authority may enter the discussion with an existing risk profile and a preliminary view of profit attribution.

Once an audit begins, AI can also help authorities interrogate larger datasets more quickly. The historic assumption that a multinational will always have a resource advantage over the authority is becoming less reliable. The likely result is more frequent, more data-intensive and more penetrating scrutiny.

AI Also Complicates One of Transfer Pricing’s Hardest Questions: Who Controls Risk?

The challenge does not stop with tax authority technology. AI is also changing the business itself. Under the OECD transfer pricing framework, the allocation of risk and return is not determined by contracts alone. Actual conduct matters, including which people perform the decision-making functions associated with control over economically significant risks.

For years, tax authorities have often looked to management roles, responsibilities and locations when analysing where risk is controlled and where entrepreneurial profit should therefore be allocated.

But what happens when economically significant decisions are increasingly supported—or in some cases substantially performed—by generative and agentic AI?

Sourcing strategy, sales planning, inventory decisions, R&D pipeline management, marketing strategy, pricing and promotion are all areas where intelligent systems can increasingly influence decision-making.

The result is a difficult new question: if risk is managed through algorithms, models and data centres spread across multiple locations, while a smaller and geographically dispersed management team sets parameters and reviews outcomes, where exactly is control over risk anchored?

That uncertainty has the potential to magnify disputes rather than simplify them.

Never Has a Live Value Chain Map Been More Important

As the relationship between people, technology, decision-making and value creation becomes more complex, a static functional analysis becomes less useful. A live and dynamic Value Chain Analysis (VCA) can provide a more objective map of what drives profit and where those profit drivers are geographically anchored.

This matters for two reasons.

First, it helps the business understand whether its transfer pricing model continues to reflect operational reality as roles, systems and decision-making evolve.

Second, it gives the organisation a coherent fact base for discussions with tax authorities when debates around value creation and control over risk become subjective.

In an AI-enabled business, the map itself must evolve. It should capture changes in people, processes, technology, decision rights and the commercial drivers of profit—not simply describe the organisation as it existed when the last policy document was written.

From Faster Compliance to a Built-to-Defend Ecosystem

AI is often discussed in transfer pricing as a way to automate routine work such as benchmarking or documentation. That is useful, but it is not the most important opportunity. The more strategic use of AI is to create a transfer pricing ecosystem that is built to defend. That means connecting a live VCA with financial and operational data, identifying anomalies as they arise, testing whether margins remain aligned with policy, spotting inconsistencies in intercompany agreements and surfacing contradictory evidence around risk control.

It also means maintaining the supporting evidence while events are current rather than trying to reconstruct the story years later.

This is the principle behind infer360's approach. The platform is designed to connect value chain analysis, operational transfer pricing and a dynamic data repository so that risks can be identified and managed earlier. The purpose is not to replace tax judgement with technology. It is to give tax teams a stronger factual foundation on which to exercise that judgement.

When the underlying facts, analysis and evidence are connected, high-quality transfer pricing documentation becomes an output of a stronger governance process—not the process itself.

What Does Defence Readiness Look Like in Practice?

A defence-ready transfer pricing environment should make it easier to answer four questions at any point in the year:

• Where is value being created today?
• Who—or what—is making and controlling economically significant decisions?
• Are actual financial outcomes consistent with the transfer pricing policy and intercompany arrangements?
• Can the organisation quickly retrieve the evidence supporting its position?

The goal is a constant state of audit readiness: emerging exposures are identified before they become major issues, the narrative is grounded in a current fact base, and tax professionals spend less time scrambling for evidence after an audit begins.

Conclusion: AI Magnifies the Risk—and the Opportunity

AI is likely to make transfer pricing disputes more complex. It strengthens the analytical capabilities of tax authorities while simultaneously changing the functions, decisions and risk-control mechanisms inside multinational businesses.

That combination will put greater pressure on traditional, backward-looking transfer pricing processes.

But the same technology can also help companies respond. A live value chain map, connected data, real-time risk identification and evidence-led documentation can turn transfer pricing from a reactive compliance exercise into a built-to-defend ecosystem.

That is the shift infer360 is designed to support: not simply faster transfer pricing documentation, but stronger preparedness before the dispute begins. Because in an AI-enabled tax environment, the strongest defence is not the argument assembled after the audit starts. It is the evidence and governance already in place before the first question is asked.