Capital is flowing faster than ever. The systems built to inform how we deploy it haven’t kept up. It’s time for a new approach.
The energy industry is undergoing a period of significant change, with unprecedented scale, and capital. Global energy investment was set to reach $3.3 trillion in 2025, with $2.2 trillion of that flowing into clean energy sources. More than 2.2 terawatts of new generation projects are waiting in interconnection queues in the U.S. alone, nearly double the entire installed capacity of the U.S. grid today. Data centers, the infrastructure backbone of the AI economy, are on track to consume between 9 and 17 percent of U.S. electricity by 2030, according to EPRI's most recent projections.
The scale of change is extraordinary, but that alone doesn’t capture what makes this moment genuinely exceptional for energy organizations. What makes it unprecedented is the rate of change.
Energy organizations — utilities, ISOs, renewable developers, traders, and heavy industry — have always operated in complex, uncertain environments. They’ve managed volatile markets, changing regulations, and shifting supply and demand for decades. The analytical disciplines that underpins these decisions, energy modeling, simulation, and analytics were built precisely to navigate that complexity.
The assumption was that complexity could be managed through rigorous analysis: careful model building, thorough scenario analysis, and well-documented assumptions. Get the model right, and you get the decision right. That assumption isn't incorrect, but on it's own, it's no longer sufficient.
The energy system today doesn’t only require rigorous analysis, it requires it fast. Renewable integration, extreme weather events, electrification, and surging data center load are reshaping supply and demand continuously, rather than on annual planning cycles. 45 GW of new renewable and storage capacity came online across North America in 2024 alone. The questions organizations need to answer — where to invest, how to plan, how to respond — are arriving faster than the systems built to answer them were ever designed to work. As the pace of change increases, periodic, meticulous analysis is no longer sufficient.
One part of the problem is relatively obvious. When modeling and analysis can’t keep pace, study queues lengthen, integrated resource plans (IRPs) take months to complete, and the scenario analyses arrive after the investment decision has already been made.
However, there’s a subtler consequence that rarely gets discussed, and it may be the more damaging one.
When modeling and analysis consistently fall behind the necessitated pace, decision makers lose confidence in them. Not because the models are wrong, but because by the time the outputs arrive, the market has moved. Assumptions that were current six months ago may no longer reflect reality. Executives, faced with urgent decisions, stop waiting for the analysis. They fill the gap with instinct, with incomplete information, and with the best approximations available.
This is the quiet erosion that happens when the pace of modeling and analysis can’t match the pace of the markets. It doesn’t announce itself as a failure, it just gradually reduces the influence of modeling on the decisions it was built to inform.
The consequences, when they compound, are anything but quiet. NERC identified poor modeling as a direct contributor to suboptimal planning practices in its assessment overview of 2024, a year in which 27 weather-related events each exceeded $1 billion in losses. In 2021, inadequate scenario modeling contributed to an estimated $80 to $130 billion in losses and at least 210 deaths during Winter Storm Uri. In April 2025, the most serious blackout on the European power system in over two decades left Spain and Portugal without power, a stark reminder of what is at stake when grid complexity outpaces the planning infrastructure meant to manage it.
These aren’t failures of the experts behind the modeling and analytics informing decisions. They are, in part, failures of pacing; the consequence of a growing gap between the speed of market change and the speed of analysis.
It would be convenient if closing this gap were simply a matter of adding more people. But the pool of skilled energy modelers and analysts: professionals with the deep technical expertise to build, run, and interpret production-grade energy models, is finite and highly specialized. Organizations can’t hire fast enough to match the pace at which new demands are arriving. And even if they could, the structural constraints would remain.
The workflows, tools, and processes that most energy organizations rely on were built for an era of annual planning cycles, relatively stable demand, and a slower-moving grid. They were not built for a market in which new use cases like battery storage dispatch, co-optimization with gas, data center load studies, and operational readiness under high renewable penetration pile onto workflows designed for a different era.
The result is that skilled analysts, some of the most valuable people in any energy organization, spend the majority of their time on data preparation, model setup, and reconciling outputs rather than on the higher-value work of designing scenarios, interpreting results, and advising on strategy. Study queues lengthen not because analysts aren’t capable, but because the systems around them weren’t built for this volume or velocity of demand. The highest-stakes investments get rigorous analysis; everything else gets assumptions.
More people building models in the same way won’t close this gap. The approach itself must evolve.
The energy industry needs a fundamentally different approach: one that applies purpose built artificial intelligence across the energy industry’s modeling, simulation, analytics and decision workflow in a way that dramatically changes the speed and scale at which trusted analysis can be produced and acted on.
That approach is what we call Energy Decision Intelligence.
Energy Decision Intelligence is the next evolution of energy modeling and simulation. It represents a new way of thinking about AI in the energy sector, not as a replacement for proven analytical methods, but as a force multiplier on top of them. The goal is to compress the time from question to answer, expand the number of scenarios and decisions that can be rigorously analyzed, and bring trusted energy market intelligence closer to the people who need it — not just the analysts running the models, but the executives making the calls.
Think of it as the democratization of intelligent energy solutions: the same depth of insight that once required a specialized team with weeks of work, now accessible across an organization in the time it takes to ask a question. An IRP that once yielded eight scenarios before a regulatory deadline can, with Energy Decision Intelligence, yield forty, allowing for stress-testing of more options before capital is committed, not after. A senior executive asking for a new scenario in response to a policy shift doesn’t have to wait weeks for the modeling team to surface an answer. A trader who needs a calibrated view of market conditions doesn’t need to be an expert modeler to get one.
Critically, this doesn’t remove analysts from the equation, but elevates what they can do. Freed from repetitive configuration and data work, analysts become even more strategic contributors: the people who shape the questions, interpret the outputs, and translate modeling results into organizational decisions. Energy Decision Intelligence scales their expertise across the organization rather than constraining it to a queue.
The potential of AI in energy has been discussed for some time. What sets Energy Decision Intelligence apart is the application of energy specific AI directly to the production-grade modeling and analytic workflows that consequential energy decisions already depend on — with the rigorous defensibility those decisions demand.
Modeling, simulation and analysis have always been at the center of how the energy industry makes its highest-stakes decisions. Energy Decision Intelligence ensures that remains the case. What shifts is the ability for systems to keep up with the rate of change, informing more decisions before capital is invested.
Energy Exemplar remains steady in the conviction that rigorous, production-grade modeling and analysis is non-negotiable for empowering transformative energy decisions. Energy Decision Intelligence is the next expression of that commitment: applying AI that’s purpose built for the energy industry to the modeling, simulation and decision workflow in a way that accelerates outcomes by orders of magnitude, anchored to the defensibility and integrity those outcomes depend on.
The energy system is moving faster than most organizations and systems were built to keep up with. The question isn’t whether the approach to energy decision making needs to change. It’s whether your organization will be positioned to lead that change, or spend the next decade catching up.