India’s clean energy transition is no longer defined by intent or capacity targets alone, but by the ability to govern scale. With more than 180 GW of installed renewable capacity and a national goal of 500 GW of non-fossil power by 2030, the challenge has shifted from deployment to orchestration, across millions of assets, diverse geographies and complex institutional systems. In this context, artificial intelligence is emerging not as an experimental technology, but as an enabling layer of public infrastructure. From decentralised solar under PM KUSUM to carbon markets, climate reporting and regulatory oversight, AI is increasingly shaping how policy translates into measurable public outcomes. Suman Chandra, Director, Ministry of New and Renewable Energy, Government of India shares with Garima Pant and Nijhum Rudra of Elets News Network on how AI is being embedded into India’s climate and energy governance to deliver impact at the population scale. Edited Excerpts:

The India AI Impact Summit emphasised a shift from pilots to population-scale outcomes. How does this resonate with MNRE’s clean energy mandate?

India’s clean energy transition has entered a fundamentally different phase. With more than 252 GW of renewable energy already operational and an ambitious target of 500 GW of non-fossil capacity by 2030, the challenge before policymakers is no longer one of intent or technology availability, but of governing scale with precision.

At this level of complexity—millions of distributed assets, heterogeneous state capacities, variable resource profiles and evolving market structures—traditional governance tools reach their limits. Artificial Intelligence, therefore, must be viewed not as a frontier innovation, but as a layer of digital public infrastructure that enables planning, execution, monitoring and course-correction at national scale.

This is why India AI Impact Summit’s emphasis on moving beyond pilots is important. In MNRE’s context, AI is increasingly embedded to translate policy into measurable outcomes—optimising deployment, improving compliance, strengthening accountability and enabling evidence- based decision-making across the energy value chain.

PM-KUSUM is often highlighted as a flagship scheme. What makes it a compelling case of AI-enabled public value creation?

PM-KUSUM is a unique programme because it operates at the intersection of energy transition, agricultural reform and fiscal sustainability. Targeting nearly 4.9 million decentralised solar pumps and plants, the scheme addresses long-standing challenges of agricultural power subsidies, groundwater over-extraction, farmer income vulnerability and grid stress.

At this scale, manual or episodic monitoring is neither feasible nor effective. AI-enabled tools allow feeder-level demand forecasting, geospatial suitability assessment, performance analytics and anomaly detection, enabling states and DISCOMs to manage decentralised assets proactively rather than reactively.

Crucially, AI in PM-KUSUM does not replace institutional responsibility—it strengthens it. By improving visibility, predictability and responsiveness, AI helps ensure that public expenditure translates into verifiable public value: reduced subsidy burden, improved quality of supply, enhanced farmer incomes and measurable emissions reduction.

How does MNRE’s regulatory and policy work reflect India’s broader philosophy on AI governance?

As non-fossil energy now constitutes over 51% of India’s installed power capacity, regulatory frameworks have become significantly more complex. Policy today must balance grid stability, investor confidence, consumer affordability and climate commitments—often under conditions of uncertainty and rapid technological change.

AI-driven analytics enable scenario modelling, stress-testing of regulatory assumptions, demand-supply simulations and risk forecasting, allowing policymakers to anticipate second-order impacts before interventions are rolled out. This strengthens regulatory credibility and reduces unintended consequences.

This approach mirrors India’s broader AI governance philosophy: responsible innovation at scale. AI is deployed with clear accountability structures, transparency, and human oversight, ensuring that technological sophistication enhances—not substitutes—democratic and institutional decision-making.

Climate negotiations increasingly hinge on data credibility. How does AI strengthen India’s global climate positioning?

India’s climate engagement is rooted in principles of equity, developmental jus tic e an d differ entia ted responsibilities. Increasingly, however, credibility in global negotiations also depends on the quality, granularity and integrity of data supporting national commitments.

AI-assisted climate modelling enhances the accuracy of mitigation pathways, adaptation cost assessments and co-benefit analysis—particularly in areas such as employment generation, energy access and resilience building. This allows India to articulate commitments that are both ambitious and realistic, grounded firmly in national development priorities.

By strengthening the analytical backbone of climate reporting and projections, AI reinforces India’s position as a serious, solutions-oriented actor that aligns climate ambition with socio-economic transformation.

Carbon markets and the circular economy are emerging as critical instruments. Where does AI deliver concrete value?

Both carbon markets and circular economy frameworks depend on credible, scalable measurement systems. As India operationalises its carbon credit trading mechanism, robust digital Measurement, Reporting and Verification (MRV) becomes foundational.

AI enables automated emissions estimation using satellite imagery, smart meters and sensor networks, significantly reducing transaction costs while improving accuracy and trustworthiness. This is essential for market integrity and international interoperability.

Similarly, in the clean energy ecosystem, AI-supported lifecycle tracking of solar modules and batteries enables forward planning for recycling infrastructure, resource recovery and waste minimisation—ensuring that energy transition today does not create environmental liabilities tomorrow.

As AI becomes embedded in climate and energy governance, what safeguards are non-negotiable?

As AI increasingly informs subsidy allocation, market incentives and compliance mechanisms, institutional safeguards become paramount. Explainability, transparency and human oversight are essential to ensure fairness, prevent systemic bias and maintain public trust.

Also Read | How India Is Embedding Intelligence into Governance and the Economy

India’s approach emphasises that AI must augment institutional capacity without eroding accountability. Responsible governance— rooted in trust, safety and public value—is what ultimately converts technological capability into sustainable, inclusive outcomes. At population scale, legitimacy is as important as efficiency.

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