Technology transformation in tax: from compliance burden to strategic advantage

The corporate tax function is undergoing a profound transformation. Once viewed largely as a compliance-driven, back-office activity, tax is increasingly at the forefront of strategic decision-making, driven by rapid advances in technology and a continually evolving regulatory landscape. The convergence of automation, data analytics, and, more recently, generative AI (GenAI) is redefining how multinational enterprises (MNEs) manage tax risk, optimise performance, and deliver value.

This article explores how tax technology transformation is reshaping the tax function, the opportunities it presents, and the challenges that must be addressed to unlock its full potential. It considers both foundational technology investments and the emerging impact of GenAI, offering a forward-looking perspective for tax professionals navigating this new era.

Over recent years, tax legislation has grown significantly in both volume and complexity, particularly for MNEs operating across multiple jurisdictions. Initiatives such as pillar two require detailed analysis of tax systems at the entity level and aggregation at the ultimate parent entity, demanding sophisticated data extraction and modelling capabilities. At the same time, local tax compliance has become increasingly intricate, driven by the successive introduction of regimes such as controlled foreign company rules, anti-hybrid measures, interest limitation provisions, and expanded reporting obligations, including those under DAC6, all of which add layers of technical complexity and compliance burden.

Tax functions must now process vast and disparate datasets, apply cross-border rules consistently, adapt rapidly to legislative changes, and meet heightened reporting and transparency requirements. This rising complexity has made reliance on technology not simply advantageous, but essential.

The adoption of tax technology has accelerated markedly in recent years, driven by both regulatory pressure and technological innovation.

Key drivers include:

• Digitalisation of tax authorities, enabling more sophisticated audits;

• Increased compliance obligations, including real-time reporting;

• Stakeholder expectations, particularly around tax transparency; and

• Advances in analytics, allowing actionable insights from tax data.

As a result, tax is transitioning from a reactive function to a proactive, data-driven discipline.

The shift towards tax technology is enduring and likely to intensify:

• Irreversible regulatory complexity – the volume and sophistication of tax legislation, particularly cross-border initiatives such as global minimum taxation frameworks, are not expected to decrease. On the contrary, they will continue to expand in both scope and detail, requiring sustained technological support to manage compliance and interpretation.

• Permanent digitalisation of tax authorities – tax authorities worldwide are investing heavily in digital infrastructure and increasingly leveraging data analytics and artificial intelligence to conduct audits and identify risks. This creates a structural asymmetry: taxpayers must match this level of sophistication simply to remain compliant, making technology adoption a necessity rather than a choice.

• Data as a long-term strategic asset – organisations are recognising that tax data is not merely a by-product of compliance but a strategic resource. The ability to aggregate, analyse, and deploy tax data for decision-making, forecasting, and governance purposes embeds technology permanently within the tax function.

• Integration with enterprise-wide systems – tax technology is increasingly integrated into broader corporate systems, including ERP platforms and financial reporting tools. Once embedded within core business processes, such systems are not easily reversed, reinforcing the permanence of the transformation.

In combination, these factors demonstrate that tax technology transformation is not merely a response to current challenges but a long-term reconfiguration of the tax function.

Historically, tax compliance has been viewed as a low-value, resource-intensive process. However, automation is transforming compliance into a strategic asset.

By automating routine processes, organisations can:

• Reduce manual errors and inconsistencies;

• Free up resources for higher-value activities;

• Generate structured datasets that inform business decisions;

• Manage their tax risk strategy; and

• Enhance consistency across jurisdictions.

Importantly, data generated through compliance processes can now be leveraged beyond their original purpose to inform strategic decision-making, including identifying tax risks, optimising group structures, and modelling potential outcomes for future transactions.

More fundamentally, the tax function itself is evolving from a compliance-driven role to a strategic advisory role. Technology, and particularly automation and GenAI, enables tax professionals to focus on higher-value activities such as scenario modelling, risk management, and strategic planning. This evolution fundamentally changes how tax departments operate and positions them as key contributors to organisational performance.

There is a growing recognition that tax governance is becoming a core component of ESG considerations. Stakeholders increasingly expect transparency and accountability in how companies manage their tax affairs.

Technology plays a critical role in enabling this shift by providing reliable, auditable data, supporting country-by-country reporting, enabling real-time disclosure readiness, and strengthening governance frameworks. In this context, tax technology is not merely operational infrastructure; it is a cornerstone of corporate reputation and stakeholder trust.

At the heart of tax technology transformation lies data. The ability to collect, structure, analyse, and deploy tax data effectively is the key determinant of success.

Tax data serves a dual purpose:

• Internally, it supports strategic decision-making and operational efficiency; and

• Externally, it underpins risk management and regulatory compliance.

A recurring challenge for organisations is the fragmentation of data across systems. Disparate datasets limit analytical capabilities and undermine the effectiveness of advanced technologies such as GenAI.

Leading organisations are therefore moving towards unified data repositories, standardised data structures, elimination of duplication, and real-time data accessibility that is stored in a knowledge database in such a way that it allows the data to compound in value rather than being merely stored. The objective is to create a ‘single source of truth’ that can support both compliance and strategic initiatives.

The effectiveness of any technology solution is inherently dependent on the quality of the underlying data on which it relies. Where data is poorly structured, inconsistent, or incomplete, the benefits of automation and AI can be significantly constrained, limiting efficiency gains and the reliability of outputs.

In this context, organisations must place strong emphasis on ensuring that data is accurate, complete, and consistent across all jurisdictions in which they operate. This requires a high degree of standardisation in data formats and definitions, supported by robust governance frameworks that clearly allocate responsibility for data management and oversight. Equally important is the implementation of secure storage solutions that are non-static and allow the data to compound in value over time, and controlled access protocols, reflecting the highly sensitive nature of tax data and the need to safeguard its confidentiality.

In the absence of these foundational elements, even the most sophisticated technological tools will struggle to deliver meaningful value. Instead of enabling insight and efficiency, weak data infrastructure risks generating unreliable outputs, increasing exposure to compliance errors, and undermining confidence in the tax function overall.

Automation has become one of the most immediate and transformative elements of tax technology, fundamentally reshaping how tax functions operate. By replacing manual intervention with system-driven processes (such as automated data extraction, return preparation, and workflow standardisation across jurisdictions), it directly addresses long-standing inefficiencies in traditional tax processes. As a result, organisations experience significantly faster reporting cycles, enabling them to meet tighter deadlines and respond more effectively to real-time reporting obligations, while allowing them to manage their tax strategy appropriately.

Beyond speed, automation materially improves the reliability of tax processes. The reduction in manual handling lowers the risk of human error, leading to greater accuracy and consistency across filings in multiple jurisdictions. This enhanced reliability is particularly important in an environment of increasing regulatory scrutiny, where inaccuracies may result in audits, penalties, or reputational exposure.

The impact of automation extends beyond operational improvements. By reducing dependence on labour-intensive processes, organisations can achieve meaningful cost efficiencies and redeploy resources towards higher-value activities such as tax planning, risk management, and strategic advisory. Over time, this contributes to a broader repositioning of the tax function from a cost centre to a value-generating contributor, while also establishing a scalable foundation for the integration of more advanced technologies, including data analytics and GenAI.

Automation also allows tax functions to respond more effectively to regulatory changes. Modern systems can track applicable laws across jurisdictions, update rules dynamically, and minimise disruption when legislation changes. This represents a significant shift from legacy systems, which often required substantial reconfiguration in response to new regulations.

As tax authorities increasingly deploy digital tools and reallocate resources towards more sophisticated audit activity, organisations are under growing pressure to ensure that relevant data can be accessed and delivered both quickly and reliably. In this environment, automation becomes a key enabler of audit readiness, reinforcing the availability and reliability of data while also supporting the integrity of audit processes and enabling timely access to information when needed.

GenAI represents the next phase of tax technology evolution. It has the potential to reshape all aspects of the tax function, including:

• Compliance – automated data classification and return preparation;

• Documentation – real-time generation of tax reports and audit materials; and

• Planning – advanced scenario modelling and forecasting.

GenAI enables tax professionals to analyse significantly larger datasets and incorporate probabilistic modelling into their analyses. This allows organisations to evaluate multiple strategic scenarios and identify tax-saving opportunities dynamically as well as quantify uncertainty in tax outcomes. As a result, tax becomes a forward-looking, strategic function rather than a purely historical one.

One of the most compelling applications of GenAI is in risk identification. By analysing historical data and identifying patterns, GenAI can, under the supervision of a tax expert, flag potential compliance issues, predict audit triggers, and suggest remedial actions. This shifts the tax function from reactive to proactive risk management.

By automating labour-intensive, low-value tasks, GenAI allows tax professionals to focus on higher-value activities such as advisory and strategy. This not only improves efficiency but also enhances the overall contribution of the tax function to the organisation.

Despite its benefits, tax technology transformation is not without challenges.

In the context of GenAI deployment, no one should forget the idiom of ‘garbage in; garbage out’. Many organisations struggle with disparate data systems, unstructured data, and inconsistent formats. These issues can significantly hinder the effectiveness of both automation and GenAI.

A key barrier to adoption lies in the difficulty of integrating new technologies with existing infrastructure, particularly ERP systems and other legacy platforms. To address this challenge effectively, solutions must be designed to ensure interoperability. This is often achieved through the use of application programming interfaces while limiting disruption to established processes and workflows. In addition, a phased or incremental implementation approach is essential, allowing organisations to progressively embed new technologies without undermining operational continuity.

The use of AI, and particularly GenAI, amplifies data security and confidentiality concerns for several structural reasons linked to how these technologies operate and are deployed within organisations.

Unlike more deterministic systems, AI tools may process data in ways that are not always fully transparent to users, particularly where large language models are involved. This raises concerns about:

• How data is used internally by the system;

• Whether it is retained after processing; and

• Whether it could be inadvertently incorporated into training datasets or outputs.

These issues are particularly acute where organisations rely on third-party or cloud-based AI solutions, which may involve data transfers outside the organisation’s direct control.

Finally, the use of AI must be reconciled with stringent data protection and privacy regulations, which often impose strict limitations on how personal and financial data can be processed, stored, and transferred. AI does not remove these obligations; rather, it complicates compliance by introducing new data flows and processing mechanisms that must be carefully governed.

There are solutions to mitigate and even fully address the two concerns around data confidentiality and privacy.

Taken together, these factors explain why AI adoption heightens data security and confidentiality concerns rather than merely extending existing ones. It fundamentally changes how data is aggregated, processed, and shared, requiring organisations to adopt more sophisticated and proactive security frameworks to mitigate the associated risks.

Tax professionals may be cautious about adopting new technologies, particularly where there are concerns about job displacement. Organisational structure and governance can also contribute to resistance.

The implementation of tax technology often requires cross-functional collaboration between tax, IT, finance, and data teams. Misalignment between these functions, or unclear ownership of technology initiatives, can delay decision-making and create friction in implementation. In addition, insufficient support from senior management may limit the resources and strategic focus required to drive meaningful transformation.

Successful tax technology transformation therefore requires not only investment in systems but also a parallel investment in people, training, and change management to ensure that the tax function can fully embrace and leverage new technologies.

The increasing use of automation and GenAI raises important questions around professional responsibility within the tax function. Despite the sophistication of these tools, ultimate accountability for the accuracy of tax filings and compliance with applicable laws remains with the taxpayer and its advisers. Technology may assist decision-making, but it cannot replace legal responsibility, and, as such, the authors are of the opinion that the increasing use of GenAI in tax will systemically require a human in the loop.

This creates an inherent tension: while organisations seek efficiency gains through technology, tax professionals must retain sufficient oversight to validate the outputs produced. Over-reliance on automated or AI-generated results, without proper review, may lead to errors, misinterpretation of legislation, or inappropriate tax positions.

The challenge is heightened by the probabilistic and sometimes opaque nature of AI outputs, which require critical assessment in light of the relevant legal framework. As a result, tax professionals must combine technical expertise with the ability to interrogate and challenge technology-driven conclusions.

In practice, this calls for clear governance frameworks, robust review processes, and proper documentation to evidence that appropriate professional judgement has been exercised. Ultimately, technology does not reduce the standard of care expected from tax professionals; rather, it reinforces the need for disciplined oversight and informed judgement in an increasingly digital environment.

A key challenge arising from the increased use of AI is how tax professionals, and particularly at the junior level, will develop the expertise needed to understand, supervise, and challenge AI-generated outputs. While AI can deliver results comparable to traditional junior-level work, this efficiency risks undermining the experiential learning on which technical skills have historically been built.

In traditional models, junior professionals develop expertise through direct engagement with data, legislation, and routine tasks. As AI increasingly performs these functions, this foundational learning layer may be compressed, weakening the ability to critically assess outputs or identify errors and biases.

This creates a structural tension: the same tasks that technology automates for efficiency have traditionally served as the training ground for developing professional judgement. As a result, organisations will need to rethink how expertise is developed, ensuring that the shift towards AI does not come at the expense of long-term technical capability and professional judgement.