Developing Nations’ 10-Step AI Governance Roadmap

Developing Nations’ 10-Step AI Governance Roadmap

While the current focus is on maximizing the use of Artificial Narrow Intelligence (ANI) tools, the infrastructure must be robust enough to support the future complexity of Artificial General Intelligence (AGI), and strong ethical guardrails must be established.

By Rakesh Raman
New Delhi | October 21, 2025

This report summarizes a suggested Artificial Intelligence (AI) deployment model for developing countries, such as India, designed to leverage AI for improved public service delivery and good governance. This model is an indicative framework that can be customized based on the specific requirements of a state or national government.

The Imperative for AI Adoption

Developing nations, including India, face the critical challenge of managing massive, growing populations while simultaneously improving the quality and reach of essential public services, such as healthcare, education, transport, and infrastructure. Despite increased governmental investment, critical sectors are often constrained by legacy issues. For instance, healthcare struggles with acute shortages of medical professionals in rural areas, leading to poor maternal and child health outcomes.

In education, the challenge is ensuring scalable, personalized, quality learning reaches every student regardless of geographic location. Traditional bureaucratic systems, marked by manual processes, data silos, and a lack of real-time insights, are incapable of addressing these systemic inefficiencies at the required scale, positioning AI as a transformative technological imperative for achieving true good governance.

Current Limitations and Transformative Potential

Currently, AI utilization in the public sector primarily focuses on Artificial Narrow Intelligence (ANI) applications, which automate specific, defined tasks. In healthcare, ANI is utilized in tools like the CoWIN platform for vaccine logistics and in advanced diagnostics, such as using computer vision to analyze X-rays to detect diseases. In governance, Robotic Process Automation (RPA) and basic Natural Language Processing (NLP)-driven chatbots are deployed for citizen engagement and automating back-office tasks like processing tax compliance checks.

However, existing deployment efforts face critical limitations:

1. Data Quality and Accessibility: Government datasets are frequently fragmented, incomplete, or “dirty,” which makes training robust and unbiased AI models difficult.
2. Scalability Challenges: Successful AI pilots often fail to scale nationally due to differences in local languages, diverse infrastructure standards, and varying degrees of digital literacy.
3. Lack of Interoperability: AI systems are often developed in silos by different departments or ministries, preventing the exchange of data necessary to create holistic, cross-sectoral insights.

AI has the capacity to fundamentally transform public services by shifting them from reactive, one-size-fits-all systems to proactive, personalized, and predictive models. This transformative potential includes:

• Predictive Healthcare: Using Machine Learning (ML) to forecast disease outbreaks and optimize drug supply chains.
• Personalized Education: Adaptive learning platforms and AI tutors that deliver personalized curricula in real-time, improving learning outcomes.
• Optimized Infrastructure & Transport: Employing computer vision and ML to monitor public assets for preemptive maintenance and detect traffic patterns to optimize signal timings.
• Fraud Detection and Compliance: AI can cross-reference vast amounts of data (e.g., tax filings, social benefits enrollment) to flag non-compliance and prevent corruption more effectively than manual auditing.

Manpower Skills and Educational Requirements

Sustaining transformative AI systems demands a profound shift in public sector skill sets, requiring competencies across several key categories. The Technical Core requires expertise in Data Science, MLOps, Deep Learning, Cloud Computing, and Prompt Engineering. Data Infrastructure necessitates skills in Data Engineering, Data Analysis & Visualization, Data Governance, Data Privacy, and Security. Finally, Responsible AI demands knowledge of AI Ethics, Risk Management, Bias Mitigation, and Explainable AI (XAI) for accountability.

To cultivate this AI-ready workforce, educational institutions must modernize curricula. This involves integrating data science, ethics, and programming (Python/R) starting from the high school level, and shifting university curricula from theoretical computer science to applied AI and MLOps. Multidisciplinary programs (e.g., Data Science + Economics) should be launched. Crucially, Centres of Excellence for AI must be established in universities through partnerships with government departments to use real-world public datasets for student projects. Furthermore, continuous professional development (CPD) programs must be mandated for existing civil servants, focusing on AI literacy and data governance principles.

A Stepwise Roadmap for Responsible AI Governance

Governments must follow a phased, responsible, and unified strategy to integrate AI effectively. While the current focus is on maximizing the use of Artificial Narrow Intelligence (ANI) tools, the infrastructure must be robust enough to support the future complexity of Artificial General Intelligence (AGI), and strong ethical guardrails must be established.

The recommended 10-Step Roadmap involves the following actions:

1. Foundation: The initial step is to Establish Centralized AI Governance by creating a nodal agency (like the IndiaAI Mission) to define a unified national AI strategy, principles, and ethical policies.
2. Infrastructure: Governments must Build the Data Backbone by digitizing all essential government records and establishing secure, interoperable data exchange platforms to break down data silos.
3. Human Capital: This requires implementing Mass Skilling Initiatives, including mandatory AI literacy training for civil servants and funding advanced education programs for AI engineers and ethicists.
4. Risk Assessment: It is essential to Classify AI Risk Levels by categorizing all potential AI use cases (e.g., high-risk for judicial decisions or social benefits) and applying appropriate governance measures.
5. Pilot & Prove: Governments should Invest in High-Impact ANI Pilots, selecting high-priority sectors (such as localized epidemic forecasting or agricultural yield prediction) and funding small, measurable projects.
6. Transparency: Policymakers must Mandate Explainable AI (XAI) to ensure that any AI-driven decision affecting a citizen’s life (e.g., eligibility for a scheme) can be clearly explained and audited by a human.
7. Privacy: To ensure data trust, governments must Establish Data Trust Frameworks by implementing strict data protection laws and employing privacy-preserving technologies like federated learning.
8. Partnerships: It is vital to Foster Public-Private-Academia Collaboration by using regulatory sandboxes to allow private AI companies and researchers to test solutions on public datasets under strict supervision.
9. Resource Allocation: Governments must Ensure Equitable Access by mandating that AI solutions prioritize serving the most marginalized populations, providing multi-lingual AI services and ensuring accessibility for persons with disabilities.
10. Continuous Review: The final step is to Implement Oversight and Audit Mechanisms by conducting annual, independent audits of deployed AI systems to monitor for algorithmic bias, efficacy, and alignment with ethical standards, updating the strategy based on real-world outcomes.

This stepwise approach represents a strategic move to overcome decades of infrastructural and bureaucratic hurdles, ensuring that the benefits of digital progress reach every citizen, thereby making public service truly efficient, equitable, and accountable.

By Rakesh Raman, who is a national award-winning journalist and social activist. He is the founder of a humanitarian organization RMN Foundation which is working in diverse areas to help the disadvantaged and distressed people in the society.

As a technology and AI expert, his professional focus is on applying emerging AI and digital technologies to enhance decision-making, operational efficiency, transparency, and democratic participation in governance, media, and business systems. You can click here to view his full profile.

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