According to Utility Dive, power system modernization with artificial intelligence is being slowed by a lack of access to system data for AI training, with an estimated 95% of all data behind utility cybersecurity and customer privacy walls. Scale AI CEO Alexandr Wang testified to the House Committee on Energy and Commerce on April 9 that “data is AI’s oil, gas, wind and solar all wrapped into one,” emphasizing that every major AI advancement depends on data shaped by human expertise. Major initiatives like EPRI’s Open Power AI Consortium and Schneider Electric’s “One Digital Grid Platform” with Microsoft and Esri are addressing these barriers, while regulatory efforts including the bipartisan AI framework and PREPARED for AI Act include data sharing provisions. Southern California Edison executives project that AI-enabled systems could perform 10 million computations in the time it currently takes to do 10,000, potentially avoiding nearly $3 billion annually in New York’s grid flexibility potential alone.
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Table of Contents
The Data Access Paradox
The fundamental challenge facing utilities isn’t just technical—it’s institutional. Utilities have spent decades building sophisticated data protection systems to safeguard critical infrastructure and customer information, creating what essentially functions as a digital fortress. Now they’re being asked to dismantle those very protections in the name of progress. This creates a classic security-innovation tradeoff where the potential benefits of AI-driven optimization must be weighed against very real cybersecurity threats. The EPRI’s Open Power AI initiative represents a middle ground, but utilities remain understandably cautious about creating new attack vectors in systems that literally keep the lights on.
The Distributed Energy Management Challenge
What makes this data problem particularly urgent is the explosive growth of distributed energy resources. Southern California Edison’s projection of 15 million customer-owned smart devices by 2045 represents a complete transformation of grid architecture from centralized to distributed control. Traditional utility operations were designed for one-way power flow from large generation facilities to passive consumers. The emerging reality involves millions of active participants—solar panels, batteries, EVs, smart thermostats—all requiring real-time coordination. Without sophisticated AI systems capable of processing this complexity, utilities face either massive infrastructure overbuilding or reliability crises. The Brattle Group’s assessment of New York’s grid flexibility shows the staggering economic stakes involved.
The Regulatory Fragmentation Problem
State-by-state approaches to data access are creating a patchwork of standards that complicates nationwide AI deployment. While Massachusetts enacts Senate Bill 2967 for advanced metering data protocols and Minnesota approves open data access standards with $563.7 million for Xcel Energy’s smart meter deployment, other states lag significantly. This fragmentation means AI models trained in one jurisdiction may not transfer effectively to others, reducing the scalability of solutions. The lack of federal standards creates uncertainty for technology providers who must navigate 50 different regulatory environments. The PREPARED for AI Act and bipartisan framework represent steps toward standardization, but their stalled progress highlights the political challenges.
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Cybersecurity and Accountability Gaps
Perhaps the most underappreciated challenge is the liability question: who’s responsible when AI-driven decisions go wrong? As Brattle’s Ramakrishnan noted, blaming an operator’s decision on an AI recommendation “will not satisfy regulators or customers.” This creates a fundamental accountability gap that utilities are rightly concerned about. Traditional grid operations rely on human operators making decisions with clear chains of responsibility. AI systems introduce opacity—even with explainable AI, the complexity of neural networks can make it difficult to pinpoint why specific decisions were made. Until regulatory frameworks establish clear liability standards for AI-driven grid operations, utilities will remain hesitant to cede control to algorithms, regardless of their potential efficiency gains.
The Path Forward Requires Balanced Approach
The solution lies in developing graduated data access frameworks that balance innovation with security. Rather than treating data access as binary—either completely open or completely closed—utilities need tiered systems that provide appropriate data for specific use cases while maintaining core protections. The NARUC Grid Data Sharing Playbook provides a foundation, but implementation will require significant investment in data governance infrastructure. Utilities that crack this code will not only optimize their own operations but position themselves as data platform providers in the emerging energy ecosystem. The transition won’t be quick or cheap, but the alternative—falling behind while distributed resources overwhelm traditional grid management—is far more costly.
