Why traditional grid planning fails in the AI era

From an unprecedented surge in power demand to severe environmental liabilities, the energy and utilities sector is facing converging pressures – and decisions made by utility leaders today will have multi-decade consequences.

In response to artificial intelligence, data center expansion, industrial reshoring, and widespread electrification, the U.S. Department of Energy expects total energy demand to grow by 15% to 20% in the next decade.¹

Providers are stepping up spending to power this energy boom. With U.S. utility CAPEX projected near $1.3 trillion for the 2026–2030 period, macro forces are simultaneously expanding the market and increasing the cost of making wrong decisions.²  

For utility leaders, the critical question isn’t if the grid needs to expand, but exactly where capital should be deployed to fortify the grid and maximize returns.

The spatial disconnect in AI energy demand

A single AI task can consume up to 1,000 times more electricity than a traditional web search, shifting site-selection metrics from latency and fiber access toward a desperate search for any available megawatts.³ This mad dash is redrawing the energy map, requiring grid planners to expand into unfamiliar territory.

According to the Solar Energy Industries Association (SEIA), U.S. energy storage installations hit a historic high in Q1, rising 32% year-over-year.⁴ Tech giants and hyperscalers are building massive battery systems to get their data centers online without waiting in conventional interconnection queues. This modern infrastructure is spilling out of traditional hubs like California and Texas and into less familiar territories like Georgia, Iowa, and Mississippi.

For utilities in these emerging regions, marco-level load forecasting won’t cut it. When a single data center can swing a regional grid’s entire balance, operators need precise, parcel-level visibility to validate near-net business density and localized infrastructure capacity.

The data gap in catastrophe risk modeling

While fighting to keep up with AI-driven energy demand, industry leaders can’t afford to lose sight of escalating environmental risks. Wildfires and other severe weather events are generating multi-billion-dollar financial obligations for utility providers.

The fight over who pays when utility infrastructure sparks an environmental catastrophe is ongoing. State legislatures are actively studying new risk tolerances⁵, while utility companies are pursuing alternative self-insurance policies and state-backed catastrophe funds to protect their balance sheets.⁶

To reduce risks that affect capital investments, address-level insight into serviceable locations is key. Standard, aggregated hazard models consistently underestimate risk because they lack asset-level context. Simply put: if your data can't connect a specific physical asset to exact parcel boundaries, reconstruction costs, and local vegetation layers, your risk modeling is incomplete.

How we help with grid planning

To successfully expand the grid, capture emerging markets, and de-risk portfolios, utilities need to shift from retroactive data aggregation to proactive property intelligence. That’s where Cotality steps in.  

With geospatial data and predictive modeling, we help energy and utility companies turn physical asset data into strategic, actionable intelligence.

With Cotality, enterprise teams can:

• Make confident decisions based on growth indicators, parcel feasibility, and near-net business density
• Use visual insights to map infrastructure buildouts where future growth is expected
• Target high-value addresses and optimize subscriber acquisition using neighborhood demographics and new-mover signals
• Analyze catastrophe risks and receive timely notifications for proactive action on at-risk properties

Ready to modernize your grid planning? Contact us to discuss how we can help.