How many decisions did you and your organization make last year without the analysis to fully support them?
It's not a rhetorical question. Most energy leaders can name them: the investment that went forward on six-month-old assumptions because the study wasn't done, the Integrated Resource Plan (IRP) scenarios that didn't get run because the deadline was hard, the project that fell below the investment threshold for a full analysis and the call was made on instinct instead. The analytical backlog is real, and in an environment where the stakes have never been higher, the associated challenges are getting harder to ignore.
In 2024 alone, 45 GW of new renewable and storage capacity came online across North America. More than 2.2 terawatts of additional projects are waiting in interconnection queues — nearly double the total installed capacity of the U.S. grid today. Add electrification, extreme weather, data center load growth, and constant policy shifts, and the picture is clear: energy organizations are navigating an unprecedented level of complexity.
The problem? The modeling and analytical infrastructure responsible for informing those decisions was built for a slower era. Studies that were once run annually now need to be run monthly. But the underlying workflow, which is largely manual, dependent on scarce expert talent, and operates on fragmented data hasn't caught up. The gap between the speed of change and the speed of analytical insight is widening. And when analysis can't keep pace, something else happens—decision makers stop relying on it.
This isn't a theoretical risk. In 2024, NERC identified poor modeling as a direct contributor to suboptimal planning practices and recorded 27 weather-related events that each exceeded $1 billion in losses. In 2021, inadequate scenario modeling contributed to an estimated $80–130 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 20 years swept across Spain and Portugal — a stark reminder that inadequate planning carries consequences that extend far beyond the balance sheet.
And the cost compounds in quieter ways too. Regulatory filings lack the ideal depth because data preparation consumed the time that should have gone to analysis, weakening your organization's position in front of regulators and interveners. Smaller investments — the ones that fall just below the threshold for a full study — get made on instinct. And when modeling consistently arrives too late to influence the decisions that must be made, on time, executives stop waiting for it and fill the gap with instinct.
This cycle is insidious, for when the decisions are made without the analysis, the organization absorbs the risk, one under-analyzed decision, compounding, at a time.
AI is showing up in every energy board room, right now because it isn’t hype— it has the power to fundamentally change how organizations operate, and those without a strategy, will fall behind. Organizations across the sector are fielding the same top-down discussion: show us how you're applying AI to transform what you do.
For energy leaders, the answer to that question should go beyond applying generic AI to already struggling workflows. The highest-leverage place to apply AI is to the entire energy decision workflow— from modeling and data setup, to scenario analysis, to the insights that get presented to the decision makers at the top of the organization— informing every major capital commitment your organization makes.
This is where Energy Decision Intelligence comes in.
Energy Decision Intelligence is the next evolution of energy modeling and simulation — applying AI across the modeling workflow to deliver trusted analytical insights faster, at greater scale, and directly into the hands of the people making decisions. Not by replacing the rigor that makes those insights defensible, but by removing the manual bottlenecks that slow it down.
For decision makers leading organizations with modeling teams, the impact is concrete.
Consider the Integrated Resource Plan, a study that traditionally takes several months to complete and produces around eight scenarios. With Energy Decision Intelligence, that same team can now produce 40 scenarios for the IRP, before the deadline. That isn't just an efficiency gain for your analysts. It means you commit capital with a fuller picture. You walk into a regulatory filing with a stronger, more defensible position. You can absorb a late-breaking policy change without sacrificing the depth of the analysis. The decisions you make are grounded in a wider range of stress-tested scenarios, not the handful your team had bandwidth to run.
That same shift plays out across capital allocation, resource adequacy, and operational decisions. More investments get the rigorous analysis they deserve — not just the largest ones. And rather than waiting weeks for outputs to be packaged and summarized, executives can interrogate modeling results directly, in plain language, without routing every question through a modeling team.
Energy Decision Intelligence makes your organization faster — and in today's environment, that matters. But speed is a means, not an end.
What you're actually gaining is the confidence to act. To commit capital knowing more scenarios were evaluated, not fewer. To file with regulators knowing the analysis behind your position is current and comprehensive. To walk into a board conversation knowing the answer to the next question is already available, not three weeks away.
Every decision that previously fell below the threshold for full analysis is now a decision that can be made on evidence. That's not a productivity story. It's a risk story. And it's the one your board is already asking you to tell.
The energy system is not going to slow down. The analytical infrastructure that informs how you navigate it needs to catch up. Energy Exemplar is best positioned to get you there, and Energy Decision Intelligence is how it happens.