AI and the Democratisation of Tax Risk Management

The Transfer Pricing Environment Is Changing Faster Than the Traditional Model

Transfer pricing has always involved a fundamental tension. Multinational enterprises want profits to be taxed once and fairly, while individual tax authorities naturally seek to protect their own tax base.

What is changing is the environment in which that tension plays out.

Geopolitical uncertainty, protectionism, tariffs, supply-chain restructuring and the rapid adoption of AI are changing how multinational businesses operate. Functions move between jurisdictions. Supply chains are redesigned. Workforces evolve. Technology takes on activities previously performed by people.

Yet transfer pricing policies are often designed at a particular point in time and reviewed periodically.

That creates an increasingly important question:

If the business is changing continuously, can transfer pricing risk still be managed periodically?

The answer is pushing organisations toward a different model—one built around continuous visibility rather than retrospective compliance.

Tax Authorities Are Becoming More Data-Led

The same technological changes affecting multinational businesses are also changing tax administration.

Tax authorities increasingly have access to multiple sources of information—from financial statements and VAT filings to customs data, Country-by-Country Reporting, Global Minimum Tax filings and public disclosures.

AI makes it possible to analyse and cross-reference these datasets much faster.

This means inconsistencies may become visible earlier. A tax authority may be able to compare what an organisation says in its transfer pricing documentation with what its financial and operational data appears to show.

For tax teams, this changes the nature of preparedness.

It is no longer enough for the transfer pricing position to make sense at year-end. The underlying data, business reality and policy need to remain aligned throughout the year.

Why a Static Value Chain Analysis Is No Longer Enough

At the centre of transfer pricing is a relatively simple question:

Where is value actually being created?

Answering it, however, is becoming more difficult.

A multinational's value chain may change because of a new manufacturing location, supply-chain restructuring, acquisitions, workforce changes or the adoption of AI within R&D, marketing, procurement and other functions.

A Value Chain Analysis (VCA) therefore cannot simply describe how the organisation operated when the transfer pricing policy was established. It increasingly needs to become a living map of the enterprise—showing how functions, assets, risks, people and important decision-making evolve over time. This creates a stronger foundation for assessing whether the transfer pricing model continues to reflect operational reality.

From Annual Review to Continuous Risk Management

This leads to one of the most significant changes in modern transfer pricing governance.

Traditionally, organisations might establish their transfer pricing policy, operate throughout the year and then assess the results during the year-end process. But problems can develop long before year-end.

Margins move. Costs increase. Exchange rates change. Demand shifts. Business functions evolve. Intercompany transactions may begin producing outcomes that no longer align with the original policy assumptions. If these changes are only identified during the annual review, the organisation may be left with a significant year-end adjustment.

A more proactive model is to bring transfer pricing into the regular financial close.

What does that look like in practice?

Monthly or quarterly closing numbers can be compared against transfer pricing policy guidelines. Where results begin moving outside expected parameters, the organisation can identify a red flag and investigate the reason.

The difference may be commercially justified. Or it may indicate that pricing should be adjusted. Either way, the tax and finance teams gain visibility while there is still time to act.

Operational TP: Turning Financial Closes Into Early-Warning Signals

This is the principle behind Operational Transfer Pricing (Operational TP).

Instead of waiting until year-end to discover a material pricing variance, Operational TP brings transfer pricing monitoring into monthly or quarterly financial processes.

At infer360, the Operational TP module is designed around this idea.

It compares monthly or quarterly closing results with established transfer pricing policy guidelines. When results begin moving outside expected parameters, the process surfaces red flags and provides guidance to help teams evaluate whether corrective pricing action should be considered.

The objective is not to eliminate every year-end true-up. Commercial conditions change, and some adjustments will always be necessary. The objective is to identify pricing drift earlier, so preventable differences do not continue accumulating throughout the year and eventually require significant retrospective corrections. This moves transfer pricing closer to operational governance rather than leaving it primarily as a year-end compliance activity.

Where AI Changes the Economics

There is another important dimension to this shift.

Historically, sophisticated tax risk management—combining detailed value chain analysis, scenario planning, continuous monitoring and audit-ready evidence—could require substantial advisory budgets and considerable manual effort.

That made the most comprehensive approaches easier for the world's largest multinational groups to maintain.

AI has the potential to change those economics. It can help organisations process larger amounts of information, identify anomalies, maintain changing value maps and connect policy with actual financial outcomes more efficiently.

In that sense, AI is not simply automating tax work. It has the potential to democratise sophisticated tax risk management. Capabilities that once required extensive resources can increasingly become available on demand to a broader range of multinational businesses.

The Opportunity Is Bigger Than Faster Documentation

Much of the conversation around AI in tax focuses on efficiency.

Can AI produce documentation faster? Can benchmarking be automated? Can tax teams spend less time on repetitive work?

Those are useful benefits. But they may not be the most strategically important ones.

The greater opportunity is using technology to help organisations understand risk before it becomes a problem.

That means connecting:

1. Value creation
2. Transfer pricing policy
3. Actual financial performance
4. Emerging risks
5. Corrective action
6. Supporting evidence

When these elements remain connected, documentation becomes the outcome of stronger governance rather than an exercise undertaken after the fact. This is the broader philosophy behind infer360: using technology to help tax teams maintain visibility over how business change, transfer pricing policy and financial outcomes interact. The technology supports professional judgement rather than replacing it.

What Should Tax Leaders Be Asking?

As the environment evolves, four questions become increasingly important:

• Is our value chain analysis still consistent with how the business operates today?
• Are actual financial results remaining within our transfer pricing policy guidelines?
• Can we identify emerging risks or pricing drift before year-end?
• Do we have the evidence to explain the decisions we made if those decisions are later challenged?

If those questions can only be answered during the annual documentation process, there may be an opportunity to rethink the operating model.

Conclusion: From Rear-View Compliance to Continuous Visibility

Transfer pricing will always require professional judgement. Business conditions change. Markets move. Supply chains evolve. Technology reshapes how organisations create value.

But the information supporting transfer pricing decisions no longer needs to arrive only at year-end.

A living value chain analysis can help organisations understand how value creation is changing. Operational TP can compare monthly or quarterly closing results against policy guidelines, surface red flags and provide guidance when corrective action may be required. AI can help make these capabilities more scalable and accessible.

Together, these developments point toward a different model of tax risk management—one that is more continuous, connected and proactive. The shift is ultimately straightforward:

From documenting what happened at year-end to understanding what is happening throughout the year.

And that may be one of AI's most important contributions to the future of transfer pricing.