AI in tax and legal: From disruption to advantage

The pace of change is real—and so is the complexity. 

Tax authorities in this region are now using AI to cross-match filing data, flag transfer pricing risks, and identify anomalies across e-invoicing datasets—in some cases, in near real time. Legal teams are deploying AI to draft commercial contracts, conduct due diligence across multi-jurisdictional transactions, and monitor regulatory change as they happen. These are not future possibilities; they are current realities, and they are creating a widening gap between organizations that have adapted their tax and legal functions and those that have not. The window to prepare is narrowing. 

The pace of adoption varies across the region, but the direction is clear. Tax regimes introduced or reformed in the past decade have generated compliance datasets of a scale and granularity that invite AI-driven analysis, and tax authorities are acting on that opportunity. In legal practice, AI tools that were novel three years ago are now table stakes in cross-border transactions and regulatory advisory work. The point of view offered here is grounded in Deloitte Middle East’s experience working with organizations across the region and highlights where the most significant opportunities and risks lie. 

Tax administration intransition

The most visible manifestation of AI in the tax sphere has been at the level of tax administration. Globally, tax authorities are moving from periodic manual audits towards continuous, data-driven compliance monitoring. 

AI-powered risk-scoring, automated cross-matching of taxpayer records, and real-time anomaly detection on e-invoicing platforms are becoming standard features of modern tax administration. These capabilities are emerging in the region as well. The introduction and reform of VAT, corporate income tax, and e-invoicing frameworks across multiple jurisdictions over the past decade have generated compliance datasets of unprecedented scale, and tax authorities are increasingly deploying AI to act on that data. 

The OECD's “Tax Administration 3.0” framework, published in December 2020, sets out a vision of seamlessly integrated, data-driven tax systems in which compliance becomes embedded in day-to-day business processes rather than a periodic obligation.¹ That vision is finding practical expression in the region, with real-time reporting requirements and automated crossmatching of taxpayer data becoming more common features of the compliance landscape. 

For organizations operating in this environment, the implications are significant. A tax authority that uses AI to identify discrepancies or flag risk does not operate on the same assumptions as one that relies on periodic manual audit cycles. Tax functions that have not yet modernized their data, systems, and processes face a genuine asymmetry—one that creates both compliance risk and potential reputational exposure. 

Emerging use casesin practice

Across tax and legal functions, AI is moving beyond back-office automation into areas that directly affect the quality and speed of professional judgement. In tax, it is increasingly used to draft transfer pricing documentation, model the impact of legislative changes across multiple jurisdictions simultaneously, prepare first-pass responses to tax authority queries, and reconcile complex intercompany transactions that would previously have required weeks of manual review. 

In legal practice, AI is accelerating contract lifecycle management—not just by reviewing contracts for risk, but also by generating first drafts of commercial agreements calibrated to specific jurisdictional requirements, flagging regulatory divergences across free zone and onshore regimes, and synthesizing due diligence findings across document sets that would be impractical to review manually at speed. 

The critical point is not that these tools exist, but that they are now reliable enough to reshape how tax and legal teams allocate their time and attention—shifting professionals from document production towards analysis, advisory, and strategic decision-making. 

Legal services: An uneven frontier

In the legal sector, the picture is more uneven. AI-powered tools for contract review, legal research, and due diligence automation have matured considerably at the global level, and their availability is no longer the primary constraint. The more significant challenge in the Middle East is adoption—specifically, the organizational, regulatory, and cultural readiness to integrate these tools into professional practice. 

The region's pluralistic legal landscape creates challenges for AI deployment. Tools trained predominantly on Englishlanguage common law materials require meaningful recalibration for civil law jurisdictions and contexts where Sharia principles inform commercial dispute resolution. Bilingual contracts— common across the region—present a further challenge, as most AI tools process each language in isolation rather than interpreting the document as a unified whole. These are not reasons to delay adoption, but they do demand a level of jurisdictional calibration that off-the-shelf global solutions rarely provide by default. 

This is not an argument against adoption; it is an argument for thoughtful adoption. The efficiency gains that AI can deliver in contract lifecycle management, regulatory change monitoring, and dispute risk assessment are real and increasingly well-documented. The organizations that will realize those gains most effectively will be those that invest in understanding how these tools perform in their specific jurisdictional and operational context, rather than deploying them wholesale on the basis of global benchmarks. 

Governance: The non-negotiable foundation

The deployment of AI in tax and legal functions is not simply a technology implementation question; it is a governance question. In both domains, AI-generated outputs can have material consequences: a misread contract clause, a misjudged compliance position, an unexplained audit flag. The professional and legal accountability for those consequences rests with people, not systems.

According to Deloitte's 2023 Global Tax Technology and Transformation Survey, 56% of tax leaders globally reported plans to increase investment in tax technology in the near term, with AI and automation identified as top priorities.² Yet investment in technology without a corresponding investment in governance frameworks—covering data quality, model validation, human oversight, and explainability—creates risk rather than reducing it.

Deloitte's research on AI governance maturity has consistently found that organizations at advanced stages of governance development are significantly more likely to report that their AI investments are delivering expected outcomes.³ In a region where many organizations are still in the early stages of formalizing their AI governance structures, this gap is both a risk to manage and an opportunity to lead.

Data residency adds a further governance dimension. Several jurisdictions in the region impose requirements on where personal and sensitive data may be processed and stored, and these constraints apply directly to AI tools that rely on cloud-based processing. Tax and legal functions deploying AI need to verify that their chosen platforms offer compliant hosting configurations (something that global vendors do not always provide by default) and factor data sovereignty into their governance frameworks from the outset, not as an afterthought.⁴ 

Talent: The long-term differentiator

No technology transformation succeeds without the right people behind it. The integration of AI into tax and legal functions requires professionals who can combine domain expertise with genuine fluency in how AI tools work, where they fail, and how to interpret their outputs critically. This is not a generic “digital skills” challenge; it is about developing professionals who can exercise judgement over AI-generated work with the same rigor applied to work produced by a junior colleague. 

The response to this challenge cannot be passive. Organizations that are serious about AI-enabled transformation need to invest actively in structured upskilling of existing professionals, build institutional knowledge about how AI tools perform in their specific practice areas, and create career pathways that reward hybrid capability rather than treating it as a niche specialism. 

National talent strategies across the region are creating momentum, but the supply of professionals who combine AI fluency with deep tax or legal domain expertise remains well below demand, and the gap will widen as adoption accelerates. 

What to do now 

For tax and legal leaders, three priorities stand out. First, audit data maturity honestly: AI tools are only as effective as the data infrastructure beneath them, and fragmented legacy systems will not support the real-time analytics that modern compliance demands. Second, establish governance frameworks before deploying AI tools in production—with particular attention to human-in-the-loop review for highstakes outputs and compliance with local data residency requirements. Third, invest in building hybrid talent through structured upskilling, crossfunctional rotations, and hiring for profiles that bridge domain expertise and technology fluency. 

The organizations that act now— deliberately, with the right governance and the right people—will not merely keep pace with the changes ahead. They will be positioned to shape them. Those that wait will find the gap increasingly difficult to close. 

By Mohamed Serokh, Partner, Transfer Pricing and Tax and Legal Growth Leader and Joelle El Khoury, Director, Tax & Legal Growth, Deloitte Middle East

Endnotes

1. OECD, 'Tax Administration 3.0: The Digital Transformation of Tax Administration' (December 2020).
2. Deloitte, 'Global Tax Technology and Transformation Survey' (2023).
3. Deloitte Insights, 'Fueling AI Transformation: Four Ways Leaders Stand Apart' (2024).
4. For an overview of data residency requirements relevant to AI deployment in the region, see Deloitte, ‘Data Sovereignty and Cloud Compliance in the Middle East’ (2025).