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Shailesh Jain

Shailesh Jain

Los Angeles Metropolitan Area

Shailesh, Principal Advisor at SCE with 30+ years in T&D, specializes in strategic asset planning and risk-based strategies. He leads efforts advancing energy transition and modern grid solutions.

The Year the Grid Met AI: 2025 and the Collision Between Exponential Compute and Linear Infrastructure

The Year the Grid Met AI: 2025 and the Collision Between Exponential Compute and Linear Infrastructure

Generative AI didn’t just increase electricity demand—it shattered planning assumptions. In 2025, utilities faced fast, concentrated load, broken forecasts, and new reliability risks, forcing a shift from passive supply to active stewardship of compute-driven infrastructure.

What’s Holding Utilities Back from Scaling the Grid for AI?

What’s Holding Utilities Back from Scaling the Grid for AI?

AI demand moves in months while grid infrastructure moves in years, creating a strategic mismatch. Unless utilities evolve from building capacity to orchestrating flexible demand, the grid becomes the bottleneck of the AI economy

From Control to Cognition: Training the Grid for the Age of AI

From Control to Cognition: Training the Grid for the Age of AI

The power grid is evolving from human command to machine learning—where AI, guided by engineers, enables adaptive, self-learning systems that boost reliability, speed, and resilience in an AI-driven energy era.

The Utility-AI Playbook: A Practitioner’s View from the Grid Edge

The Utility-AI Playbook: A Practitioner’s View from the Grid Edge

AI in utilities isn’t about replacing people—it’s about process, trust, and governance. The Utility-AI Playbook outlines seven steps to make intelligence utility-grade: start small, structure data, fine-tune, phase in, measure, govern, and evolve. Reliability remains human-led.

The Utility-AI Value Framework

The Utility-AI Value Framework

The Utility AI Value Framework helps utilities prioritize AI and GenAI by scoring reliability, renewables, cost, customer, compliance, and innovation. With 25 key use cases, it shows how priorities differ by utility type and context, while stressing data quality, workforce, and governance.

Fine-Tuning AI for the Grid: How Industry-Specific LLMs Are Transforming Utilities

Fine-Tuning AI for the Grid: How Industry-Specific LLMs Are Transforming Utilities

Utilities are adopting fine-tuned AI models trained on sector-specific data to avoid costly misinterpretations, integrate securely with grid systems, and deliver gains in reliability, efficiency, and decision-making that general-purpose LLMs cannot match.