10 Steps Governments in Developing Countries Can Take to Leverage AI for Good Governance

This is an indicative AI deployment model being suggested to developing countries like India. This model can be customized and refined after studying the requirements of a particular state or national government.

By Rakesh Raman
New Delhi | October 20, 2025

The Imperative: Bridging the Service Delivery Gap

India, like many rapidly developing nations, faces a dual challenge: managing a massive, growing population while simultaneously improving the quality and reach of essential public services. Despite significant governmental initiatives, the state of critical sectors—such as healthcare, education, transport, and infrastructure—often remains constrained by legacy issues. 

In healthcare, this manifests as acute shortages of medical professionals in rural areas, leading to high out-of-pocket expenditure and poor maternal and child health outcomes, even as government investment increases. In education, the challenge is scalability and personalization, ensuring quality learning reaches every student, irrespective of geographic location. 

Traditional bureaucratic systems, characterized by manual processes, data silos, and a lack of real-time insights, struggle to address these systemic inefficiencies at the required scale. This is where Artificial Intelligence (AI) emerges not just as an improvement tool, but as a transformative technological imperative for achieving true good governance.

AI’s Current Footprint and Lingering Limitations

Currently, AI’s presence in India’s public service sector is primarily focused on Artificial Narrow Intelligence (ANI) applications, aimed at automating specific, defined tasks.

• In Healthcare: AI is used in tools like the CoWIN platform (for vaccine distribution logistics), and in advanced diagnostics (e.g., using computer vision to analyze medical images like X-rays and CT scans to detect diseases like retinal disease or tuberculosis).
• 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 grant applications or tax compliance checks.

Limitations: The current deployment is hampered by several critical factors:

1. Data Quality and Accessibility: Government datasets are often fragmented, incomplete, or “dirty,” making it difficult to train robust and unbiased AI models.
2. Scalability Challenges: Many successful AI pilots 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 frequently developed in silos by different departments or ministries, preventing them from exchanging data and creating holistic, cross-sectoral insights.

AI as a Transformative Technology in Public Service

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

• Personalized Education: AI tutors and adaptive learning platforms can analyze a student’s progress in real-time and deliver personalized curricula, improving learning outcomes dramatically, especially in under-resourced schools.
• Predictive Healthcare: Machine Learning (ML) can be used to forecast disease outbreaks, optimize drug supply chains, and triage emergency cases based on satellite imagery, weather patterns, and patient data. Furthermore, AI-powered diagnostic tools can bring expert-level medical consultation to remote villages.
• Optimized Infrastructure & Transport: Computer vision and ML can monitor public assets (roads, bridges, power grids) for preemptive maintenance, detect traffic patterns to optimize signal timings, and streamline logistics and public transport scheduling, reducing congestion and saving costs.
• Fraud Detection and Compliance: AI can cross-reference terabytes of data from various sources (tax filings, social benefits enrollment, contract bids) to flag non-compliance and prevent corruption more effectively than manual auditing.

Manpower Skills: The New Civil Service Requirements

Implementing and sustaining these transformative AI systems requires a shift in public sector skill sets, demanding a blend of technical depth and domain expertise.

Skill Category	Key Competencies Required
Technical Core	Data Science and Machine Learning (MLOps), Deep Learning, Python/R Programming, Cloud Computing (Azure/AWS), Prompt Engineering (for Generative AI adoption).
Data Infrastructure	Data Engineering (building robust pipelines), Data Analysis & Visualization, Data Governance, Data Privacy and Security.
Responsible AI	AI Ethics, Risk Management, Bias Mitigation, Explainable AI (XAI) for accountability, and Domain Expertise for contextual application.
Strategic Leadership	Critical Thinking, Problem-Solving, Stakeholder Management, and Communication (to explain complex AI outcomes to non-technical policymakers).

Educational Roadmap: Imparting Future-Ready Skills

Educational institutions must adapt swiftly to produce an AI-ready workforce.

1. Curriculum Modernization: Integrate data science, ethics, and programming (Python/R) starting from the high school level. University curricula must shift from theoretical computer science to applied AI, MLOps, and specialized AI tracks for sectors like healthcare and finance.
2. Multidisciplinary Programs: Launch joint degree programs (e.g., B.Tech in Computer Science + Public Policy or Data Science + Economics) to create professionals who understand both the technology and the socio-economic context of its application.
3. Government-Academia Partnership: Establish Centres of Excellence for AI in universities, partnering with government departments (like NITI Aayog or state IT departments) to use real-world public data sets for student projects.
4. Continuous Professional Development (CPD): Mandate upskilling programs for existing civil servants, focusing on AI literacy, data governance principles, and ethical usage, ensuring that policymakers can ask the right questions and implement effective governance frameworks.

The Evolution: From ANI to AGI

The journey of AI development is generally categorized into three stages:

1. Artificial Narrow Intelligence (ANI): The current reality. ANI is specialized, performing one task exceptionally well (e.g., spam filtering, facial recognition, recommendation systems). All current government AI applications fall into this category.
2. Artificial General Intelligence (AGI): The aspirational goal. AGI would possess human-level cognitive ability to learn, reason, and generalize knowledge across a wide range of tasks without explicit reprogramming. This stage would allow for truly cross-sectoral decision-making (e.g., an AGI system that simultaneously optimizes transport, public health, and food supply).
3. Artificial Superintelligence (ASI): A hypothetical future where AI surpasses human intelligence across virtually every domain.

For developing countries, the immediate focus must be on maximizing the use of ANI tools and ensuring the data infrastructure is robust enough to eventually support the complexity of AGI, while simultaneously establishing strong ethical guardrails for future deployment.

A Stepwise Roadmap for AI Implementation in Governance

Governments must follow a phased, responsible, and unified strategy to integrate AI effectively.

Step	Focus Area	Actionable Goal
1	Foundation	Establish Centralized AI Governance: Create a nodal agency (like the IndiaAI Mission) to define a unified national AI strategy, principles, and ethical policies.
2	Infrastructure	Build the Data Backbone: Digitize all essential government records and establish secure, interoperable data exchange platforms to break down data silos.
3	Human Capital	Launch Mass Skilling Initiatives: Implement mandatory AI literacy training for civil servants and fund advanced education programs for AI engineers and ethicists.
4	Risk Assessment	Classify AI Risk Levels: Categorize all potential AI use cases (e.g., high-risk for judicial/social benefits, low-risk for chat support) and apply appropriate governance measures.
5	Pilot & Prove	Invest in High-Impact ANI Pilots: Select 3-5 high-priority sectors (e.g., agricultural yield prediction, localized epidemic forecasting) and fund small, measurable pilot projects.
6	Transparency	Mandate Explainable AI (XAI): 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	Establish Data Trust Frameworks: Implement strict data protection laws and employ privacy-preserving technologies (like federated learning) to utilize data without compromising individual identity.
8	Partnerships	Foster Public-Private-Academia Collaboration: Use regulatory sandboxes to allow private AI companies and researchers to test solutions on public datasets under strict supervision.
9	Resource Allocation	Ensure Equitable Access: Mandate that AI solutions prioritize serving the most marginalized populations, such as providing multi-lingual AI services and ensuring accessibility for persons with disabilities.
10	Continuous Review	Implement Oversight and Audit Mechanisms: Conduct 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.

The deployment of AI in developing nations like India is more than a technological upgrade; it’s a strategic move to leapfrog decades of infrastructural and bureaucratic hurdles. By focusing on the 10-step roadmap—from establishing ethical governance and robust data infrastructure to investing heavily in the right manpower skills—governments can successfully transition from basic Artificial Narrow Intelligence (ANI) applications to a future that supports the complexity of Artificial General Intelligence (AGI). 

This transformation ensures that the benefits of digital progress are not confined to urban centers but reach every citizen, making public service truly efficient, equitable, and accountable. I have laid out the framework, but implementing it requires constant refinement.

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.

Rakesh Raman  |  LinkedIn  |  Facebook  | Twitter (X)