Governing with Artificial Intelligence: Insights from the OECD’s 2025 Roadmap

Introduction
Artificial intelligence (AI) is fast becoming a cornerstone of modern governance. What was once a frontier of speculative debate is now a practical tool in the hands of public administrations eager to improve efficiency, responsiveness, and fairness. On 18 September 2025, the OECD released its landmark report “Governing with Artificial Intelligence: The State of Play and Way Forward in Core Government Functions”. This publication reviews over 200 real-world AI implementations spanning 11 core government functions, identifies enabling conditions and key risks, and offers a policy roadmap for governments to adopt AI responsibly.

In this article, I summarize the OECD’s key findings, reflect on their implications for governance, and propose how countries—especially those in the Global South—can chart a path to harness AI while preserving trust, equity, and accountability.

From Principles to Practice: The OECD’s AI Trajectory

The OECD has long played a pivotal role in shaping global AI governance. Back in 2019, its AI Principles became the first intergovernmental framework guiding member states on inclusive, human-centred, transparent, and accountable AI. Those principles laid the normative groundwork—emphasizing values like fairness, safety, and accountability.
However, principles alone are insufficient. Realizing them in public institutions demands a deeper understanding of institutional, technical, and political challenges.

The 2025 report represents a transition: from high-level norms to empirical, functional deployment. By examining how governments are currently using AI in tax, social protection, justice, procurement, health, public finance, and other domains, the OECD moves beyond theory to practice. This “state of play” approach makes the report a valuable roadmap for policymakers who must navigate both opportunities and pitfalls.

Why Governments Embrace AI

Governments adopt AI not out of novelty, but out of necessity. The OECD highlights several driving forces:

1. Productivity and Efficiency
   Public administrations face burgeoning workloads, limited human resources, and legacy processes. AI can automate repetitive tasks (data entry, indexing, routine checks) and help civil servants focus on analysis, strategy, and citizen interaction.
2. Responsiveness & Personalization
   Citizens now expect government services that are as convenient and customized as those in the private sector. AI enables predictive services—anticipating needs, tailoring communication, and speeding up decision cycles.
3. Better Evidence & Policy Design
   AI can analyse large, heterogeneous datasets (economic, environmental, behavioural) to detect trends, stress points, or early warning signals, thereby enabling more agile and targeted policy interventions.
4. Fraud Detection, Compliance, & Integrity
   One of AI’s most mature uses is in detecting anomalies—identifying patterns that suggest fraud, under-reporting, or compliance risks. With AI’s help, tax and customs agencies can more effectively allocate scrutiny and audits, improving fairness and revenue collection.

Yet, the OECD stresses that these benefits accrue only if AI is introduced thoughtfully—with appropriate safeguards and institutional readiness.

Key Risks and Governance Challenges

The OECD categorizes the challenges into several interconnected risk domains:

1. Ethical Risks & Bias
   Data-driven decisions may perpetuate historical biases—disproportionately affecting marginalized groups. If left unchecked, algorithmic systems can deepen inequality or embed unfair treatment.
2. Operational Risks & Reliability
   AI depends on high-quality data and robust infrastructure. Poor inputs, system errors, or cyber vulnerabilities can undermine the validity of results.
3. Exclusion Risks
   Those lacking digital literacy or connectivity may not benefit from AI-driven services. There’s a risk that the “AI-enabled state” leaves some citizens behind.
4. Opacity & Public Trust
   If AI decisions are opaque or inscrutable, citizens may perceive them as “black-box” authority—fuelling resistance, suspicion, or backlash.
5. Risk of Inaction
   Paradoxically, one of the greatest dangers is delay: governments that hesitate may fall behind in efficiency, competitiveness, and citizen expectations.

The report is clear: managing these risks is as important as achieving technical capabilities.

A Three-Pillar Framework for Trustworthy AI in Government

To guide governments, the OECD proposes a three-pillar framework:

1. Enablers
   • Data foundation & digital infrastructure: interoperable data systems, open formats, secure storage, real-time flows.
   • Workforce capacity & institutional know-how: training public servants, hiring data-science talent, embedding AI literacy.
   • Funding, procurement, and innovation ecosystems: budget flexibility, public-private partnerships, controlled experimentation.
2. Guardrails
   • Legal and ethical frameworks: binding standards, rights-based safeguards, regulation aligned with human rights.
   • Impact assessments & algorithmic audits: ex ante reviews of risk, ongoing audits, redress mechanisms.
   • Transparency, accountability, and oversight: clear lines of responsibility, explainable models, citizen recourse.
3. Engagement & Participation
   • Citizen and stakeholder involvement: co-design, public consultations, feedback loops.
   • Cross-government coordination: avoid silos, promote consistency and interoperability.
   • International cooperation: shared standards, regulatory alignment, cross-border learning.

Only by combining enablers, guardrails, and engagement can AI in governance be both effective and trustworthy.

Role of AI in Tax Scrutiny

1. Fraud Detection and Risk Assessment
2. AI systems analyse massive volumes of taxpayer data, transaction records, and filings to identify unusual patterns or anomalies.
3. Machine learning models can flag cases with higher risk of evasion or under-reporting, allowing tax authorities to focus resources on genuine suspicious cases instead of random audits.

• Data Integration and Analysis
• Tax authorities collect data from multiple sources—banks, employers, customs, e-commerce platforms.
• AI integrates and analyzes this data, creating a fuller picture of taxpayer behavior and uncovering discrepancies between declared income and actual activity.

• Improving Efficiency
• Traditionally, tax audits required extensive manual work.
• AI reduces this burden by automating routine checks, freeing auditors to focus on complex investigations.

• Personalized Compliance Services
• Chatbots and virtual assistants powered by AI help taxpayers understand rules, deadlines, and filing processes.
• This reduces unintentional errors and administrative workload while improving compliance.
• Transparency and Accountability

• Continuous Monitoring and Adaptation
• AI models learn from new data and continuously adapt to emerging evasion techniques.
• Regular audits of AI systems ensure that bias or overreach does not compromise taxpayer rights.

• Safeguards Required
• Human Oversight: Final decisions should rest with tax officials, not algorithms.
• Privacy Protection: Personal data must be handled securely and proportionately.
• Fairness: AI models must be trained on unbiased data to avoid discriminating against certain groups.
• Transparency: Taxpayers should have clarity on how AI contributes to scrutiny and should be able to contest outcomes.

Illustrative Use Cases in Tax and Public Finance

While the OECD covers 11 functions, some of the most compelling examples come from taxation and public finance—domains where data volume, compliance incentives, and stakes are high.

• Risk-based Audits & Compliance Prediction
  Tax authorities apply supervised learning models to taxpayer behaviour (income, expenses, third-party data, patterns over time) and flag returns with high likelihood of errors or evasion. This allows selective, data-driven audits instead of blanket inspections.
• Fraud Detection and Forensics
  AI systems sift through transaction flows, identify abnormal spikes, suspicious transfers, or shell-company linkages, enabling early intervention.
• Revenue Forecasting & Budget Planning
  In public finance, AI helps forecast revenue trends, simulate tax reform impacts, or analyse macroeconomic shocks, contributing to more agile budgeting and fiscal planning.

These applications are already in use in several OECD countries, and the report provides details (e.g., how models are validated, how thresholds are set, how human oversight is layered).

Implications for India and Similar Developing Nations

For countries like India, the OECD’s lessons offer both opportunity and caution:

• Digital Infrastructure Edge
  India’s large-scale systems—Aadhaar, GSTN (Goods & Services Tax Network), UPI—lay the groundwork for integrating AI into governance. These platforms generate rich transactional data and provide avenues for service delivery.
• Bridging the Digital Divide
  However, large disparities remain in connectivity, literacy, and digital trust. AI-driven services should be inclusive, with fallback human channels and support for vulnerable populations.
• Ethics, Accountability, and Redress
  As AI expands, India must institutionalize algorithmic oversight—ethics boards, transparency requirements, impact audits, appeals mechanisms—to guard against overreach.
• Pilot and Scale Approach
  India should start with low-risk, high-value use cases (e.g. revenue prediction, fraud detection in non-sensitive areas) before rolling into welfare, litigation, or citizen profiling.
• Capacity Building
  Upskilling civil servants, recruiting data scientists, and fostering public-sector AI labs are essential. Partnerships with academic and private sectors can accelerate capacity.
• Regional and Global Collaboration
  Engaging with global AI governance networks (such as OECD, G20, UNESCO) enables shared learning, standard alignment, and cross-border projects.

By aligning with OECD’s three-pillar framework, countries like India can pursue an AI-enabled state that is efficient, legitimate, and equitable.

Policy Recommendations for National Governments

Drawing on the OECD’s findings, here are key recommendations for governments embarking on AI-driven governance:

1. Adopt a phased approach
   Begin with pilot projects in relatively safe, well-understood domains. Use learnings to scale and refine models.
2. Institutionalize governance mechanisms
   Create bodies responsible for AI oversight (e.g. algorithmic ethics boards, data protection authorities, audit units).
3. Mandate algorithmic impact assessments
   Require ex ante and ex post evaluations of AI interventions, especially in areas affecting rights or economic outcomes.
4. Ensure transparency & explainability
   Use interpretable models where possible; when using opaque models, complement them with explanation modules and human review.
5. Embed citizen participation
   Co-design AI systems with users, invite feedback, allow appeals or contestation of decisions.
6. Strengthen digital infrastructure
   Invest in data interoperability, secure computing infrastructure, cloud capabilities, real-time data pipelines.
7. Build human capacity
   Train civil servants in data science, AI literacy, ethics. Foster joint academic–government partnerships.
8. Legal & regulatory alignment
   Update legal codes to clarify liability, data protection, algorithmic fairness, and accountability.
9. Promote international cooperation
   Engage with standard-setting bodies, participate in cross-border pilots, share open data and models where feasible.
10. Continuously monitor and adapt
    AI technologies evolve rapidly. Policies must be flexible and subject to periodic review, iteration, and sunset clauses where needed.

India’s Way Forward

India is uniquely positioned to benefit from AI in governance, given its scale, digital infrastructure, and demographic advantage. However, to ensure AI adoption remains trustworthy, the following OECD-inspired steps are critical:

1.Strengthening data infrastructure: Modernize IT systems, expand broadband connectivity, and standardize public sector data.

2.Developing digital skills: Launch large-scale AI literacy and training programs for civil servants and citizens.

3.Embedding ethics and accountability: Create AI ethics boards, update IT laws, and ensure algorithmic transparency.

4. Risk-based governance: Adopt frameworks that match oversight intensity to application sensitivity.

5. Citizen engagement: Conduct consultations in multiple languages, ensure grievance redressal, and maintain AI registers.

6. International collaboration: Partner with global AI initiatives, startups, and research institutions for knowledge exchange.

7. Adaptive policies: Start with pilots in low-risk areas such as agriculture and scale up gradually based on evidence.

These measures will allow India to harness AI’s potential while aligning with OECD principles to preserve fairness, trust, and inclusiveness.

Conclusion

The OECD’s 18 September 2025 report, “Governing with Artificial Intelligence”, is more than a descriptive account of AI use in governments—it is a strategic call to action. It reminds us that AI can help public institutions become more responsive, efficient, and data-driven, but only if deployed with care, accountability, and legitimacy.

For countries seeking to modernize governance, the lessons are clear: invest first in enablers (data, skills, infrastructure), enforce guardrails (laws, audits, transparency), and maintain deep citizen engagement. That triad—when balanced—can help prevent the pitfalls of algorithmic overreach, exclusion, or erosion of trust.

As governments across the world embark on this journey, they must remember: AI is a means, not an end. Its ultimate purpose should be to enhance democracy, fairness, and service to citizens—not to automate power without accountability.