Executive Summary
Across the utilities industry, AI already helps teams work faster. However, far fewer leaders can tie that speed to a defensible business outcome.
A survey of utility executives revealed that 96% of the leaders interviewed view AI as strategically important, yet only 26% have moved beyond proof of concept.
That's the AI value gap, and it matters because utilities are under pressure from every side.
U.S. energy demand is growing at an unprecedented pace. Data centers could consume 9% to 17% of the country’s electricity generation by 2030.
Keeping up means funding new generation while maintaining aging infrastructure and grid reliability. Inflation pushes those costs higher, even as regulators scrutinize every dollar for affordability.
The supply chain offers little relief. With skilled crews and equipment in short supply, utilities must deliver more with the resources they have.
Leaders we spoke with see orchestrated, end-to-end workflows as the long-term goal. But before scaling further, they want proof of what works. That proof often depends on how AI investments are sequenced and measured.
Our latest white paper shows how AI can act as a capacity multiplier. It lets teams spot constraints earlier and focus scarce expertise where judgment matters most.
The paper maps where AI value sits across four pools and how to measure it through operating results.
Download the paper to start building the business case for your next AI investment.
Frequently asked questions
Start with the categories where a supply disruption would do the most damage to grid reliability and capital project timelines, such as transformers and switchgear. Score each AI use case on its operational impact and on how ready your data is to support it. Fund the use cases that score high on both first, then use those early results to build the case for scaling into adjacent workflows.
An AI-native platform maps your supplier base down to sub-tier manufacturers and shows where a single source supplies critical components across multiple projects. It tracks lead times and supplier capacity signals continuously, flagging potential shortages well before they affect your capital project schedules. That early visibility gives you time to qualify alternate suppliers and reserve manufacturing slots ahead of demand.
Set clear ownership and quality standards for supplier and contract data, because every AI recommendation inherits the accuracy of the data behind it. Define which decisions AI can execute autonomously and which need human approval, with thresholds set by spend value and risk level. Build audit trails and compliance rules into each workflow so your team can trace any decision back to its source when regulators ask