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Building the AI-Ready Grid: Connecting Assets, Data, and Intelligence

Blog Post | 08.10.2025 | 4 min read | Carlos Elena-Lens

Discover how Hitachi Energy is helping utilities modernize the grid for artificial intelligence – connecting physical assets, data, and intelligence to unlock resilience, flexibility, and sustainability.

Artificial intelligence (AI) has advanced rapidly across industries, redefining processes, uncovering efficiencies, and creating new opportunities. In the utility sector, however, adoption has been more measured. This isn’t due to lack of opportunity, but because the power grid is a mission-critical system. And changes to the grid can’t be rolled out overnight; upgrades must balance innovation with the uncompromising requirement for reliability.

The reality is that much of the grid’s infrastructure was designed and installed decades ago. Despite the shift of certain core functions to digital IT platforms, the grid still largely consists of analog equipment – transformers, breakers, and high-voltage switchgear– that operate largely independently of modern IT platforms. While these assets have proven effective, they were never designed with data-driven optimization or integration with digital intelligence in mind.

For utilities, the challenge is not whether AI will add value, but how to prepare the grid so that AI can be deployed safely, securely, and at scale. That preparation is what we call “AI-readiness.”

Defining AI readiness in the grid context

The grid is often described as one of the world’s most complex machines.

In the United States alone, the grid is comprised of hundreds of thousands of miles of high-voltage transmission lines and millions of miles of distribution wires that stretch across the country, interconnecting cities, towns, and rural areas. It is then supported by tens of thousands of substations that bring together diverse generation sources such as thermal plants, hydropower facilities, and increasingly, solar and wind farms and battery energy storage systems. These in turn deliver that energy to demand centers such as factories, data centers, and households. Yet, many critical elements remain disconnected from IT systems. And without connectivity, utilities lack visibility into equipment conditions, power flows, and overall system performance that they would otherwise have access to in real time.

AI readiness begins by building a digital foundation across three layers: connectivity, intelligence, and data management.

Connectivity involves equipping physical assets with sensors, software, and secure communications networks so they can be monitored and controlled. Intelligence is then applied by integrating these assets with operational technology (OT) platforms such as protection and control systems, while linking them with IT networks to enable secure, bidirectional data flow. Finally, effective data management consolidates information from (often) siloed systems and transforms it into actionable insights through applications such as advanced distribution management systems (ADMS), asset performance management (APM), and energy optimization platforms.

Together, these layers transform isolated equipment into integrated, intelligent systems capable of supporting AI applications.

Hitachi Energy’s technology stack - developed in collaboration with companies in the broader Hitachi Group– captures this layered approach and is illustrated in the below graphic, reflecting how physical assets, connectivity, cybersecurity, and applications come together to create a grid that is both reliable today and ready for AI-driven operations tomorrow.

At the base are the physical assets that utilities already own. By introducing sensors, connectivity software, and cybersecurity, utilities can connect these assets into a secure digital environment. Once connected, the data becomes a strategic asset that can be applied across multiple functions.

Continuous monitoring of equipment health through asset management tools, for example, can extend asset lifecycles and reduce maintenance costs. Distribution networks can be optimized in real time using ADMS and automation systems, helping utilities integrate higher levels of renewable energy without compromising stability. Predictive analytics can also be applied to energy trading and optimization platforms, improving financial performance by strengthening participation in power markets and enabling more precise grid balancing.

What remains constant is the need for an agile, evidence-based approach that safeguards reliability while unlocking new value.

A step-by-step journey toward AI

Transitioning to an AI-ready grid is not a single step but a staged process.

Each investment in connectivity, intelligence, or data management delivers incremental benefits, improving resilience, enhancing situational awareness, and enabling more flexible operations. The pace and nature of adoption will vary by utility, depending on their asset base, regulatory environment, and strategic priorities.

Hitachi Energy partners with utilities at every stage of this journey. By combining operational technology with IT expertise, and by embedding cybersecurity at every level, we help create systems that are not only more intelligent, but also more secure, flexible, and sustainable.

AI is no longer a distant vision, it’s here, and it’s here to stay. With the right foundations in place, it can become a practical tool for strengthening the grid, improving outcomes for utilities, and powering a more resilient energy future.

If you’d like to speak with our team to understand how you can utilize AI further, or expand and modernize your grid, then please don’t hesitate to get in touch.

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Carlos Elena-Lens
Digital Transformation Leader, Hitachi Energy North America

Carlos focuses on providing customers with the consultative & domain expertise to access the full breadth of Hitachi’s digital services & products for the energy domain. Success means enabling electric utility customers to realize their digital projects and transformation initiatives. His cross-disciplinary expertise across software engineering, management consulting, and strategic design makes him uniquely adept at driving digital solution development and delivery. His current areas of focus include: Utility Analytics (AI/ML), Organizational Change for Digital Transformation, Advanced Simulation and Modelling of Grid Operations, Application of Real-Time and Predictive Analytics for grid operations & maintenance.