AI is helping the utility sector keep the lights on

Organizations in the utility sector are facing a once-in-a-generation challenge. Demand for power is rising rapidly as AI–driven data centers, crypto mining, and electrification place new loads on the grid. At the same time, weather-related disruptions are becoming more frequent and severe, increasing uncertainty across infrastructures that were not designed for this level of volatility.

One of the clearest signs of stress is the interconnection queue. For example, at least 2.6 terawatts of new generation capacity is waiting to connect to the U.S. grid, according to a report from the Lawrence Berkeley National Laboratory. That’s more than the capacity of the entire existing generation fleet.

Accelerating those connections is not just a technical challenge. It also requires new transmission, regulatory approvals, capital investment, and careful balancing of new and existing assets — all while maintaining reliability and acceptable investment returns. Time is the limiting factor in every direction.

Grid balancing has also become more complex, because utility companies typically no longer own or control many of the assets connected to their systems. Hyperscalers, third-party generators, utility-scale solar, residential solar, microgrids, electric vehicles, and battery storage all potentially contribute power in some capacity. 

These factors are causing the utility sector to increasingly rely on commercial agreements rather than direct operational control. Although those contracts may look reliable on paper, weather, maintenance, outage, and cost variability introduces real-world risk that traditional or manual approaches struggle to manage. This new reality is complex, and there is no simple solution.

Where AI is a difference-maker

The most effective use cases are not experimental or flashy. They focus on improving how utilities can run core business processes in the face of overwhelming complexity.

When operational technology (OT) data from grid assets is connected with information technology (IT) data across customers, field service, supply chain, and finance, organizations gain a fundamentally different view of events as they unfold. Instead of reacting based on partial information, teams can see operational impact and customer impact together, in near real time.

During a power outage for example, that visibility is critical. Utilities need to understand: 

• Which assets and customers are impacted
• Locations of crews and skill sets that can move quickly to resolve the issue
• Availability of equipment for repairs
• How long restoration is likely to take 

Today many of these insights exist in disconnected systems, requiring significant manual effort to assemble. AI-driven analytics can not only bring those signals together, helping prioritize restoration based on defined outcomes rather than best guesses, but in a more modern world, also predict necessary maintenance to prevent outages before they happen.

Effective AI starts with how data is structured and accessed. Grid operations, customer systems, workforce management, and financial planning have evolved in parallel, often without strong integration. As grid complexity increases, those gaps become a constraint. AI alone cannot overcome fragmented data or disconnected processes in many cases.

A more durable approach includes establishing a shared data foundation that enables operational and business signals to be analyzed together. It requires enabling IT and OT data to intersect where decisions are made, without replacing existing platforms or moving large volumes of data. When data remains in place and is accessible through a common layer, near-real-time analysis can occur with far less manual effort, significantly reducing the time to action.

A unified data foundation enables AI to be applied where it delivers some of the most significant value: routine, high-frequency business decisions. Workforce dispatch, outage prioritization, inventory allocation, and cost assessment all benefit from analytics that reflect current operating conditions. Teams can evaluate trade-offs dynamically — based on customer impact, resource availability, and operational constraints — rather than relying on static rules or delayed reporting.

Platforms such as SAP Business Data Cloud (BDC) are designed to support this approach by enabling data harmonization across SAP and non-SAP systems while providing a foundation for analytics and AI at scale. 

Take a simple but very real example: Fragmented systems and processes often hide a unified view of customer account issues: meter faults, outages, billing alerts, and customer programs such as off-peak EV charging. SAP Business AI and SAP BDC, meanwhile, can give customer service reps a near-real-time summary so they have the latest accurate information for the customer.

The value comes not from isolated use cases but from creating the conditions for consistent, coordinated action across the business. AI and BDC are not replacing people. They are simply very powerful tools that move enterprises from a system of record to a system of action.

For many utilities, SAP partnerships with Databricks and Snowflake make this shift to a system of action well within reach.

Statistically, many AI projects are failing primarily due to issues with data quality that span complex matrixed organizations and processes and because of unrealistic cost, time, and value outcome expectations.

Discover how SAP helps organizations in the utility sector meet today’s challenges as well as establish readiness for whatever comes next. Click here to learn more.