Data centers drove roughly half of all U.S. electricity demand growth last year. Power consumption is projected to hit record levels again in 2026. And the infrastructure to meet that demand is still years behind.
Development teams feel this acutely. Pipelines that used to carry 20 active sites now need to hold 80 to hit the same conversion rate. Project timelines have compressed while headcount hasn't grown to match.
Other industries have responded to this kind of pressure by turning to AI. The energy and utility sector has been slower, with only a 13.6% AI adoption rate, one of the lowest rates across major industries.
That hesitation isn't irrational. Energy development is jurisdiction-specific. What applies in MISO doesn't translate to PJM. Permitting timelines vary by county. Interconnection queues aren't standardized. AI trained on generic data doesn't automatically account for those differences and in this industry, those differences can make or break a project.
The specificity of energy development doesn't eliminate a role for AI. It just means the role has to be precise. The teams pulling ahead are the ones who've figured out which workflows AI can meaningfully accelerate and which ones still require a person who understands the nuance.
Where AI is creating real value today
Site search and origination
The first filter in any development workflow is finding sites worth pursuing. Traditionally, that means analysts pulling parcel data, cross-referencing zoning maps, estimating grid proximity and building site lists that can take days before a single site gets evaluated.
AI-assisted site search operates at a different scale. Define your development criteria (acreage, voltage requirements, distance to transmission, land use type) and get back a ranked site list in minutes. The output is a priority list based on viability signals.
What makes it work is what's underneath. AI site search is only as good as the data it draws from. Proprietary, regularly maintained data layers that reflect current grid infrastructure, zoning classifications and parcel ownership separate useful output from noise.
Site triage and prioritization
For teams with existing pipelines, the challenge is often the inverse: too many sites, not enough bandwidth to evaluate them properly. Inbound opportunities, broker submissions and origination campaigns can produce hundreds of candidates. Going through them manually means slow decisions and missed opportunities.
AI-supported triage applies your screening criteria automatically. A 500-site list becomes a focused shortlist in the time it used to take to set up the spreadsheet. The result is a faster judgement call by cutting the noise before a person has to touch it.
Project tracking
Once a site moves forward, the coordination overhead begins. Permitting timelines vary by jurisdiction and change frequently. Tracking what's been submitted, what's pending and what's at risk requires sustained attention across multiple workstreams.
AI-assisted project management centralizes that work: organizing permitting documents, flagging upcoming deadlines and building project timelines that reflect where things actually stand. Items that used to fall through the cracks in a spreadsheet get surfaced before they become a problem.
Community sentiment research
Technical site criteria are only part of what determines whether a project gets built. Community dynamics and political context have become just as consequential at the local approval stage.
Community sentiment for data center projects is at an all-time low. Political support for renewable energy projects is split along party lines and varies by district. Development teams that used to focus primarily on technical diligence are now spending significant time on stakeholder research and community engagement strategy before a site ever goes to an approval body.
AI can generate the research and diligence reports that support those conversations, faster than a team working manually. Once you have the information and a strategic plan, you can act on it.
Where to start
For a team that hasn't introduced AI yet, the right entry point is the highest-volume, most repetitive workflow, not the most sophisticated one. Site screening and triage are the natural place to begin: the criteria are already defined, the output is easy to evaluate and the time savings show up quickly.
The more consequential applications (automated diligence, AI-supported submission workflows, community research) follow once a team has a working sense of how to evaluate AI output and where the gaps are.
AI compresses the research and screening work. The decisions, relationships and local judgment calls are still yours. Getting started means figuring out which workflows to hand off and staying close to the ones that still need you.
Want to see these workflows in action with a real-life example? Join us June 30 at 3 PM ET for a live session with Tony Wagler, Director of Development and Kyle Baranko, Head of Product, as they walk through AI development workflows and show exactly where the human review still matters.
