How many organizations are making high-stakes energy decisions without the modeling insight to back them up?
More than one might think. A trading desk needs accurate, physics-based, fundamental price forecasts to trade on, but rarely has the specialized modeling talent in-house to build them. A data center developer needs a clear view of power availability and cost before committing to a site, but doesn't have a resource planning group to turn to, like a large scale energy company might. A small utility, corporate energy buyer or independent power producer (IPP) can carry real exposure to market volatility without ever building a team to model it. For all of them, rigorous energy modeling has been out of reach, not because the need is smaller, but because building that capability internally hasn't been feasible.
Data center developers are a clear examples of this gap. In a recent survey of 150 data center professionals, power availability was the single most-cited challenge in securing power for a project, ahead of regulatory approval, cost uncertainty, and land availability. Yet only 22% of respondents said they assess power availability and cost before acquiring land. More than half wait until the planning and business case stage, well after the siting decision has effectively been locked in. That's not a failure of judgment. It's what happens when a decision this consequential has to be made without the modeling capability to stress-test it in advance.
Renewable developers face a version of the same problem. More than 2.2 terawatts of renewable and storage projects are currently sitting in U.S. interconnection queues, with average wait times approaching five years. Every one of those projects carries exposure to congestion, curtailment, and price volatility that only becomes visible under real scenario analysis, the kind that traces how a project's output actually interacts with everything else on the grid around it. Developers with that analytical capability can catch those risks before they commit capital. Developers without it are, in effect, making investment decisions with less information than the market requires.
Energy Decision Intelligence is a market category, not a single product. It's the next evolution of energy modeling and simulation, applying AI across the modeling workflow to deliver rigorous, physics-based energy insight faster, more accessibly, and at greater scale than ever before.
What makes this a meaningful shift isn't just speed for the organizations that already have modeling teams. It's access for the organizations that don't. Historically, rigorous energy modeling insights required specialized talent that not all organizations could justify hiring . Energy Decision Intelligence changes that equation, not by replacing the value of a dedicated modeling function, but by making that same caliber of fundamental insight available to organizations that could never build one.
PLEXOS® Pulse shows what this looks like in practice. A trader's traditional morning starts with a static report: a handful of charts and some narrative, prepared by someone else, hours old before the trading window even opens. With PLEXOS® Pulse, that same desk can instead go to a guided, AI-powered experience built on daily-updated PLEXOS® modeling, asking what's driving the day-ahead price or how this week's generation mix compares to last week's, without a modeling team standing between the question and the answer. It's one example of what's possible when decades of PLEXOS® modeling, the same foundation trusted for resource plans, reliability assessments, and regulatory filings, gets delivered in a form built for accessibility rather than specialization.
For organizations without a modeling function, the choice has previously been binary: build a capability that can't be justified, or make consequential decisions without the insight that's actually needed to make an informed one. Neither option is good. The first ties up capital and headcount an organization may not be able to defend. The second means renewable projects get built without a clear read on curtailment risk, trading desks work from information that's already stale by the time it reaches them, and data center sites get chosen before power availability is understood.
Energy Decision Intelligence removes that tradeoff, not by making every organization a modeling organization, but by decoupling the value of rigorous energy modeling from the cost of building it in-house. That's the direction the industry needs to go for every organization that has historically had to choose between building a modeling capability it can't justify and deciding without the insight it needs.
That's a different kind of confidence than speed alone. A renewable developer shouldn't have to accept blind exposure to a crowded interconnection queue as the cost of doing business. A trading desk shouldn't have to act on a market view that's already gone stale. A data center developer shouldn't have to discover a power availability problem after the land is bought. Energy Decision Intelligence closes these gaps, wherever they show up, for whichever organization is facing them.
The energy system isn't getting any less complex, and that complexity doesn't spare organizations just because they do not have the resources to model it themselves. Energy Decision Intelligence removes that barrier.