Strategic AI Integration in Revenue Administrations: A Roadmap for Digital Governance

The World Bank, in collaboration with prominent research institutions like the OECD, IBM Center for the Business of Government, American University of Armenia, and the Australian Taxation Office, has released a powerful and timely blueprint for integrating artificial intelligence (AI) into the core operations of revenue administrations. The report offers a structured and evidence-based strategy for implementing AI in tax and customs systems. With over half of global tax administrations already adopting AI tools, particularly in fraud detection, compliance monitoring, and taxpayer services, the need for a well-governed, scalable approach has never been more urgent. This publication positions AI not as a technological novelty but as a transformative force for financial institutions striving to improve service delivery, transparency, and operational efficiency.

A Smart, Ethical Approach to AI in Tax and Customs

Artificial intelligence, as laid out in the report, holds immense potential to automate complex processes, enhance decision-making, and uncover fraud patterns invisible to traditional rule-based systems. AI tools are already being used to classify taxpayers, cross-check digital invoices, translate multilingual tax forms, and even simulate economic behaviors under different policy scenarios. Large language models (LLMs) like GPT have proven capable of interpreting vast repositories of tax laws and guiding auditors through intricate investigations. However, these advances come with serious ethical and operational challenges. The authors emphasize that explainability, accountability, and legal clarity are essential in building public trust. AI systems in the public sector must be transparent, free of bias, and able to justify decisions affecting citizens’ financial obligations.

The paper underscores the risks of ad hoc AI implementations, projects that often stall or fail due to a lack of governance, poor data quality, or siloed execution. To address these issues, the authors advocate for a framework grounded in clear governance structures, robust data pipelines, human-AI collaboration, and continuous learning cycles. AI cannot be treated as a one-off software deployment; it must be integrated into the DNA of revenue administrations as a dynamic, evolving capability.

From Pilot Projects to Intelligent Governance

The World Bank’s strategic model unfolds in three distinct phases: Inception, Consolidation, and Optimization, each designed to progressively build institutional maturity. During the Inception Phase, organizations focus on data readiness, training key personnel, and conducting small-scale, low-risk AI pilots. These pilots, such as basic taxpayer chatbots or predictive models for filing deadlines, help secure early wins and generate support. Legal groundwork is also laid during this phase, with cross-functional AI committees bringing together IT, legal, and operational units.

In the Consolidation Phase, typically the second year, AI use is expanded across departments. More sophisticated tools are introduced, such as advanced risk scoring models or integrated fraud detection systems. Data governance structures are fully implemented, human review processes are formalized, and legal protections for taxpayers, such as the right to appeal AI decisions, are strengthened. By the Optimization Phase, institutions operate with high-performing, transparent AI systems deeply embedded in day-to-day operations. Dedicated AI departments may be formed, public transparency measures are enhanced, and the organization begins to shape national or even international AI policy standards.

Use Cases that Showcase Global Momentum

Real-world examples from countries like Australia, Georgia, Armenia, and Uzbekistan give this framework a global dimension. The Australian Taxation Office uses over 150 AI models to detect fraud, including unpaid superannuation contributions and tax evasion uncovered through leaked documents like the Panama Papers. In Georgia, AI is being deployed to identify VAT refund abuse, where the system replaces inflexible rule-based checks with dynamic risk modeling. In Armenia, a partnership with the American University of Armenia has led to an AI system that reads and analyzes digital invoices, cross-checks tax filings, and flags anomalies that could indicate fraud.

One particularly innovative project in Uzbekistan involves a multimodal AI tool that classifies products using photographs, commercial invoices, and customs declarations. This system combines image recognition, document embeddings, and LLMs to automate HS code classification, an essential function in international trade. These use cases not only highlight the versatility of AI in public administration but also reinforce the importance of cross-border learning and institutional experimentation.

Governing Data and Nurturing Human Intelligence

Central to this transformation is a strong commitment to data governance. The report emphasizes that AI is only as effective as the quality, integrity, and accessibility of its underlying data. Data governance ensures that taxpayer and trade partner information is handled responsibly, legally, and with clear accountability. To support innovation while protecting privacy, the use of synthetic data, which mimics the statistical structure of real data without exposing sensitive information, is discussed as a viable tool, especially when external vendors are involved in model training.

Equally vital is the role of human-AI collaboration. The report repeatedly stresses that AI should augment, not replace, human workers. AI systems can handle repetitive queries and crunch large datasets, but it is human judgment that ensures fairness, empathy, and strategic insight. Training programs must be tailored for both technical teams and non-technical staff to foster a culture of digital readiness. Institutions that position AI as an assistant rather than a threat are more likely to build internal support and long-term resilience.