Accelerating modernization while protecting reliability

“Utilities don't have the luxury of choosing between modernization and operational stability. The challenge is finding ways to modernize faster while maintaining the reliability, governance and resilience that customers depend on every day."

Utilities have spent decades modernizing critical systems that support grid operations, customer service, asset management and regulatory compliance. But today's modernization environment looks very different than it did even a few years ago.

Reliability remains non-negotiable, but AI is giving utilities new ways to modernize without compromising operational stability. At the same time, they must balance innovation with reliability, cybersecurity, regulatory requirements and public trust. As a result, successful modernization efforts are increasingly focused on business outcomes, disciplined governance and long-term adaptability rather than technology upgrades alone.

How is AI changing the way utilities approach modernization?

Historically, modernization projects in utilities were often large, complex initiatives focused on replacing aging systems, migrating infrastructure or moving applications to the cloud. Those efforts centered on core platforms such as outage management systems, customer information systems, work and asset management platforms and grid operations technologies.

Today, more utilities are approaching modernization with a much stronger focus on business value. Whether the goal is reducing outage restoration times, improving customer service, increasing field workforce productivity, strengthening asset performance or supporting grid modernization initiatives, modernization efforts are increasingly tied to value.

AI is accelerating that change. Organizations can use AI-assisted development tools to modernize legacy applications, automate portions of the software development life cycle and reduce the effort required to move from older environments to modern architectures. This allows utilities to modernize faster while reducing cost and delivery risk.

Just as importantly, AI is helping utilities modernize systems that support critical business functions without disrupting the way they operate, as traditionally associated with large-scale transformation efforts.

Why is modernization becoming more urgent for utilities?

Utilities are facing growing pressure from multiple directions. Infrastructure is aging. Severe weather events are becoming more frequent. Customer expectations continue to rise. At the same time, electrification, rapid grid expansion, distributed energy resources, smart meters and grid-edge technologies are creating new operational complexity and generating unprecedented volumes of data.

Utilities are being asked to deliver more reliable service, manage costs and support the energy transition while maintaining safe and uninterrupted operations.

For these organizations, modernization is about creating the agility needed to respond to changing grid conditions, evolving regulatory requirements and emerging customer demands. Organizations that modernize effectively are building platforms that can support everything from predictive asset maintenance and outage response to customer engagement and grid planning without requiring another large-scale transformation every few years.

What role does data play in AI-enabled modernization?

Data remains one of the most important foundations for modernization success. Many organizations are eager to implement AI use cases, but they often underestimate the importance of governance, data quality and data accessibility. The reality is that AI cannot scale effectively without trusted data, regardless of industry.

For utilities, this challenge is particularly important because information often exists across customer information systems, outage management platforms, GIS environments, asset management systems, smart meter networks and enterprise applications. Without a strong data foundation, organizations can end up with duplicated information, inconsistent outputs and growing operational complexity.

A large utility we recently worked with had years of critical project knowledge buried across hundreds of unstructured documents, with teams spending hours searching for answers that should have taken minutes. We helped them stand up an AI Knowledge Agent, built on a governed data foundation — not a rip-and-replace — that turns that documentation into trusted, natural-language answers and cut search time by roughly 85%. It's a powerful example of modernization creating measurable operational value without requiring a disruptive rip-and-replace transformation.

The most successful organizations are creating governed, reusable data products that bring together trusted information from across the enterprise. That foundation can support a wide range of use cases, from predicting equipment failures and prioritizing vegetation management efforts to improving outage communications and enabling AI-powered customer service.

The organizations seeing the greatest value from AI are not treating data as a byproduct of operations. They are treating it as a strategic asset that supports both modernization and long-term operational resilience.

How can utilities innovate with AI while managing risk?

This is one of the defining challenges facing the industry. Utilities operate critical infrastructure, which means reliability, security and compliance can never be compromised. Leaders want to leverage AI to improve efficiency and innovation, but they also need confidence that these technologies are being implemented responsibly.

For example, utilities may use AI to improve outage prediction, enhance asset maintenance planning, support grid operations or automate customer interactions. But those use cases must be governed carefully to ensure transparency, cybersecurity, regulatory compliance and operational reliability.

The organizations making the most progress are not chasing the newest AI model or technology trend. Instead, they are focusing on specific business challenges and building flexible architectures that allow them to evolve as AI capabilities mature.

That approach helps utilities capture value today while maintaining the governance and oversight needed to manage operational and regulatory risk.

What differentiates utilities that are successfully modernizing?

Leadership commitment is a major factor, but culture and execution discipline are equally important. Many organizations have invested in AI tools, but purchasing technology alone does not create transformation. Successful utilities are embedding AI into everyday workflows, building adoption programs and helping employees understand how these capabilities can improve the way they work.

That might mean enabling field technicians with AI-powered assistants that surface maintenance history and asset information in real time, helping customer service representatives resolve billing and outage inquiries more efficiently, or supporting engineering and IT teams with AI-assisted development and documentation capabilities.

Organizations that operationalize AI in this way are also measuring outcomes. They track productivity improvements, operational efficiencies and business results to ensure modernization efforts continue creating value.

What advice would you give utility leaders today?

Start small, but start now. Utilities do not need to wait for perfect data, perfect governance or perfect organizational readiness before taking action — but start with data they can trust and a strong foundation. The technology will continue to evolve, and organizations learn most by gaining practical experience.

The best approach is to begin with targeted, high-value use cases that solve real business challenges while building organizational capability. A utility might start with a focused initiative around predictive maintenance, outage communications, workforce productivity or customer service before expanding to broader enterprise-wide applications.

Over time, those successes create momentum for broader modernization efforts.

Leaders should also recognize that the return on knowledge can be just as important as the return on investment. The utilities that begin building experience, governance practices and workforce readiness today will be better positioned to take advantage of future advances in AI and modernization.

The utilities best positioned for the future won't simply deploy more AI. They'll build the operational, data and governance foundations that allow AI to continuously improve reliability, resilience and customer outcomes at scale.