Key Insights
Digital infrastructure is necessary but uneven. The survey shows that digital readiness varies substantially across DISCOMs and across systems within the same utility. Odisha reports stronger foundations across metering, SCADA, outage management, and asset registries, while Tamil Nadu combines more limited infrastructure deployment with emerging analytics capability. This unevenness means that the feasible starting point for AI adoption will differ across utilities: Some can begin integrating operational use cases, while others must first strengthen their underlying digital systems.
Interoperability is the binding constraint. Installing digital systems does not automatically make their data usable across utility operations. Odisha reports greater integration across metering, SCADA, outage management, billing, and asset systems, while Tamil Nadu’s platforms remain more fragmented. This distinction is critical for AI applications that depend on combining information from multiple sources. As smart-meter and other digital deployments expand, interoperability and common data standards will increasingly determine whether DISCOMs can convert new data into operational insights.
Institutional capability determines whether pilots scale. Digital infrastructure must be accompanied by the capacity to use it. Tamil Nadu’s dedicated AI and data analytics wing and Odisha’s analytics function illustrate growing institutional attention to data, but both states also rely on external expertise for parts of their analytics work. Moving from pilots to routine deployment will therefore require DISCOMs to strengthen internal analytics teams, operational ownership, procurement practices, and collaboration with technology and research partners.
Structural Constraints
Procurement Processes
Current public procurement frameworks, particularly the least-cost bidding model, significantly constrain the deployment of AI-based solutions. Specialized vendors offering novel AI-based solutions have higher operating costs due to skilled labor, intensive model development, and lack of scale, making them vulnerable to being undercut by incumbents offering less-capable solutions. Additionally, many AI applications are inherently niche, with only a limited number of qualified vendors, which complicates standard competitive bidding requirements.
As a result, where pilots have demonstrated clear value, DISCOMs struggle to retain vendors or scale successful deployments. This has led to repeated pilot cycles without institutionalization or scale-up, slowing learning and limiting system-wide impact.
Vendor-Operations Disconnect
Procurement challenges are compounded by a persistent disconnect between vendors and system operators. Tamil Nadu highlighted a clear gap between the vendors, system implementors, and line operations. In practice, this manifests in two ways. Contracts are not designed to sufficiently incentivize vendors to deliver AI and value-added analytics or operational decision support, and vendors do not have an appreciation for the operational requirements for a DISCOM, and as such, offer solutions that do not enhance the analytics layers addressing the critical bottlenecks DISCOMs experience. This is driven by a lack of institutional knowledge in DISCOMs when conducting procurement and the lack of centralized practices and requirements on contracting digital tools.
As a result, DISCOMs have successfully contracted and deployed technologies including SCADA, ERP, and metering that generate large volumes of data, but there is no enhanced analytics layer on top of these systems.
Human Capacity and Skilling
The survey suggests that internal capability remains a constraint even where dedicated analytics teams are emerging. Continued reliance on external expertise can make it difficult for utilities to retain knowledge and scale successful applications internally.
Tamil Nadu explicitly notes that existing recruitment rules constrain the induction of data science and analytics talent, underscoring the need for reforms that allow DISCOMs to hire specialized skills and build durable in-house capability. Without greater flexibility in staffing and career structures, DISCOMs will remain dependent on vendors for core digital functions, limiting long-term institutional learning.
Policy and Institutional Roadmap for India
Scaling AI across Indian DISCOMs will require coordinated improvements in data infrastructure, governance, and institutional capacity.
This roadmap should therefore be read alongside India’s emerging digital governance agenda for the power sector. In the submission to the Ministry of Power on the Electricity Amendment Bill of 2025, the lead author of this paper argued for national standards for utility data architecture, interoperability, and third-party access, supported by clear data governance protocols covering data quality, access, sharing, security, retention, and consumer protection. That proposal is directly relevant to the AI readiness agenda, because advanced analytics cannot scale in a fragmented data environment. The broader policy direction under IES further reinforces the need for a layered institutional approach, in which central standards are translated into regulatory protocols and then operationalized by DISCOMs through internal systems and capacity building.
Accordingly, the policy roadmap below focuses on three linked priorities: (1) establishing the minimum data and interoperability foundations for AI use cases, (2) strengthening DISCOM-level institutional capacity to manage and use those systems, and (3) creating a regulatory environment that supports secure, responsible, and scalable digital transformation. This is the basis for moving from isolated pilots toward AI-ready distribution utilities.
Establish National Data Standards and a Layered Governance Framework
DISCOMs must have the minimum viable digital infrastructure required for AI adoption. For many utilities, the binding constraints are not sophisticated analytics tools, but incomplete metering coverage, fragmented asset records, weak interoperability between systems, and inconsistent data quality. Policy should therefore focus on the basic conditions that allow data to be collected, standardized, stored, and used across core operational domains.
At the national level, the Ministry of Power, the Central Electricity Authority, and relevant regulators should create the data and governance foundations required for AI adoption in distribution utilities. A national framework should therefore define core principles for data quality, timeliness, ownership, access, retention, sharing, security, and consumer privacy, while also specifying common formats and protocols for utility systems such as meters, SCADA, OMS, billing, GIS, and asset registries.
This framework should be implemented through a layered governance model. At the national level, the Ministry of Power and the Central Electricity Authority should define the standards and policy principles that guide digital transformation in the sector. At the regulatory level, the Central Electricity Regulatory Commission and the State Electricity Regulatory Commissions should translate those standards into enforceable protocols for utilities. At the utility level, DISCOMs should operationalize them through internal systems, data pipelines, and operational workflows. This approach is especially important in a concurrent sector such as electricity, where effective digitalization depends on coordination across central and state institutions.
Recent policy developments provide a strong foundation for this agenda. The Ministry of Power’s National Electricity Data Sharing Framework proposes a common architecture for electricity sector data sharing built around standardized metadata, machine-readable datasets, interoperable APIs, data governance officers, and a National Electricity Data Portal. Together with the creation of IES, these initiatives represent the first steps toward a national electricity data architecture capable of supporting advanced analytics and AI-enabled utility operations.
Build Internal Capability
DISCOMs should establish dedicated analytics units and provide role-specific training for engineers, planners, control-room staff, and data managers. The ability of DISCOMs to design, implement, and sustain AI tools internally should be strengthened. Many DISCOMs continue to rely on consultants and external expertise when developing AI tools. That dependence is understandable in the short term, but if it persists, it will limit institutional learning and make utilities overly dependent on vendors for core operational capabilities.
To address this challenge, DISCOMs should invest in targeted upskilling programs for engineers, planners, control-room staff, and data managers. These programs should focus not only on the use of digital tools, but also on the operational logic behind them: how data is cleaned, how models are interpreted, how exceptions are handled, and how analytics inform day-to-day decisions. Capacity building should be role-specific rather than generic, so that staff can directly apply what they learn in their work.
DISCOMs should also establish dedicated analytics units or innovation cells, including those mentioned in Odisha and Tamil Nadu. These units can serve as internal anchors for pilot design, data coordination, vendor management, and use case prioritization. Their role should be to help utilities move from fragmented experiments to a structured pipeline of digital initiatives. Where necessary, such teams can be supplemented by short-term external support, but the institutional objective should be to create durable in-house ownership.
Deepen Partnerships with Academia and Research Institutions
DISCOMs will need to cultivate a stronger innovation ecosystem by establishing systematic linkages with universities, research institutions, and applied technology partners. The Indian power sector has a large knowledge base in institutions such as the Indian Institutes of Technology, the National Institutes of Technology, the Indian Institutes of Management, and specialized policy and engineering centers. These institutions can provide research depth, technical expertise, and independent testing environments needed to accelerate utility innovation.
This approach is consistent with international experience. In the United States, utilities often coordinate with research institutions and public programs to accelerate the development of AI-enabled technologies for distribution systems. The California Energy Commission’s Electric Program Investment Charge program has supported large-scale clean energy research and innovation in partnership with major utilities, while other city- and utility-based initiatives have helped commercialize targeted clean-technology solutions and improve grid resilience. India should adopt the core logic of these models: Utility data and operational problems can become the basis for collaborative innovation when public agencies, utilities, and researchers work together.
For India, this suggests a practical agenda. DISCOMs should establish structured partnerships with academic and research institutions for joint problem solving, pilot development, model testing, and staff training. These partnerships can help utilities access analytical expertise while also ensuring that research is grounded in real operational needs. They can also create a pipeline for the next generation of AI-trained engineers and utility professionals.
Create Sector-Wide Coordination Mechanisms
The policy response should also facilitate a stronger unified platform for collective learning and collaboration across DISCOMs. The newly established AIDA has a vital role to play in this regard by pooling resources, sharing use cases, curating good practices, and disseminating implementation lessons across states. Such a platform would be especially useful for public utilities, many of which face similar constraints but lack the internal capacity to design solutions independently.
A sector-wide mechanism can also help standardize training, coordinate pilot efforts, and reduce duplication across utilities. Rather than each DISCOM reinventing the same processes, a common platform could support template-based guidance on data management, use-case design, vendor evaluation, and performance monitoring. It could also serve as a bridge between utilities and the broader ecosystem of academia, startups, and technology firms. In this sense, sector-wide coordination is not a substitute for utility-level reform; it is a force multiplier that can make reform more efficient and more scalable.
Build International Partnerships
Coordination within India addresses only part of the capability gap, and many of the issues are not unique to Indian utilities. The United States is a useful counterpart on data standards enforcement, how regulators assess analytical investments, and how smaller utilities acquire capacity collectively. AIDA can now coordinate engagement between state governments and DISCOMs in India and state governments, public utility commissions, and partners in the United States.
This can be operationalized in several ways, including building relationships between AIDA and its functional counterparts in the United States and pursuing cross-learning opportunities with the public power and rural electric cooperatives whose members more closely mirror DISCOMs. Collaboration between Indian research institutions and American national laboratories has great potential. The U.S.-India Collaborative for Smart Distribution Systems with Storage, or UI-ASSIST, operational from 2017 to 2023, is one of many examples of previous U.S.-India collaboration in the smart grid space, led by Washington State University and IIT Kanpur. With the rapid evolution of technology and research since 2023, there is an even greater need for building formal international data standard frameworks in the energy space. The United States and India can potentially lead those efforts leveraging their strong existing bilateral relationship.