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Can AI Fight Gridlock?

By: Steve Lewis

How intelligent is Artificial Intelligence (AI)? Is it smart enough to solve its own challenges of scaling? There are those who have confidence it will.

At this point we’ve probably all heard of the benefits that AI, Machine Learning (ML), and Large Language Models (LLM) can provide business (to learn more, see AI Products to Simplify Your Business, on pg 20). For some, however, the problems of scale extend beyond the operational and downstream cost benefits.

These concerns are part and parcel of the current multitude of changes disrupting all commercial real estate markets. It’s a time of transition and uncertainty, given not only the economic picture but also the ongoing advancement of sustainability efforts and substantial infrastructure modernization in the electrical grid. For instance: In the recent issue of Site Selection Magazine, two industrial experts expressed these concerns:

Patty Horvatich, senior vice president of Business Investment for the Pittsburgh Regional Alliance (the Economic Development affiliate of the Allegheny Conference) sees the clean-energy transition as a major challenge/opportunity. Like AI itself, “Its full impact will take time” to measure, she says. One of the main issues is power availability. This is especially true with such advancements as the increasing use of electric vehicles and AI. “People just think it’s flipping a switch. But you have to follow it back, ultimately to the power source.”

Steve Kozarits, SIOR, SVP of Industrial Services and Tenant Advisory for Transwestern Commercial Services in Chicago, agrees: “While politicians are mandating electrification, they won’t pick up the phone and recognize that the national grid needs updating.” This is especially true as green energy solutions tie increasingly into the environmental, social, and governance (ESG) protocols of corporate users.

There is, however, an increasingly shared interest in the greening of the grid. “There are more energy choices every day,” says Geoffrey Kasselman, SIOR, the Chicago-based co-founder and CEO of Evoke Partners. Plus, the cost of some of those sources, such as solar, “have plummeted from four or five years ago.” The growth of corporate ESG programs proves that “Consumers are much more educated and much more sensitive to their corporate persona and carbon footprint today.”

Nevertheless, Bryan Gardner, SIOR, calls the national grid “the most complicated invention we’ve ever created.” The EVP of McIntyre Real Estate in Reno, NV sees the energy infrastructure as “a juggling act, where you have all of these different substations and power sources–wind vs. solar vs. coal–with no clear way of determining what you’re paying for and why.”


Yes, just like AI itself, the optimization of the grid (and the move ultimately to a smart grid), is in its infant stages, and progress, though slow, is being made. But, what of the original question? Can AI/ML help us puzzle it all out? More than one expert says yes.

“The concerns over optimizing the grid are complex and multifaceted,” says Ra'eesa Motala, SIOR, cofounder and president of Evoke Partners. The Minneapolis-based Motala explains that, given the right algorithms, “AI can predict certain components and track utilities.”

The national grid is the most complicated invention we’ve ever created.

Kasselman adds that, if, let’s say, the algorithm dictates an energy-use reduction by 2040, “You can prompt your AI ‘mouse’ to scour the grid to seek and secure the most appropriate, available, and affordable energy source.”

Therein lies the essential character of AI. “Algorithms are constantly self-improving,” he says. And, as they grow more robust and responsive, in a sense, “they’re creating their own networks. Those AI-driven networks are connecting with other AI-driven networks, exponentially accelerating the improvement for all involved.” This relates as well to connections with local municipalities, “allowing them to see and plan for what their local businesses need.”

Of course, that grand-scale connectivity will take time. But given AI’s above-stated self-improvement capabilities, Motala disagrees with the assumption that the grid will come up short in the near-term. In a sense, that would be counter to the very purpose of AI. “There’s a lack of understanding thus far around how the grid and AI will interact,” she says. “AI can help with energy consumption and reduce downtime by giving users–manufacturers, for instance–a heads-up on usage peaks and off-peaks so they can schedule the work of their operations accordingly. This improves efficiencies and productivity while reducing costs.”

That point of view is shared by no less than the Department of Energy (DOE) and its Office of Technology Transitions (OTT). “How much electricity will you need tomorrow?” the DOE asked as early as 2019. “To manage the uncertainty in predicting power needs, grid operators rely on computer models that help estimate everything from power demand to traffic patterns.”

Not surprisingly, this involves “incredibly complex math problems,” says the OTT report. As our sources imply, it is AI to the rescue.

Thus, the report continues: “With the assistance of AI, researchers at DOE’s Argonne National Laboratory are developing new ways to extract insights from vast quantities of data on the electric grid, with the goal of ensuring greater reliability, resilience, and efficiency.”

Those three qualities pertain as much to current functions as well as to future capabilities. This year, Vik Chaudhry, cofounder of Buzz Solutions, told Forbes that AI does for the grid what it does for operations. It takes the manual heavy lifting out of the process of grid inspection, and is responsible for a reported 70% in time savings. “It is trained to identify anomalies like rust, missing or broken parts, and damage,” he said.

AI can also contribute to a more robust grid, more immune from weather anomalies. “An AI model trained on data from across the country, rather than from a single regional utility, can better predict grid problems as a result of a one-of-a-kind weather event,” says Chaudhry. “Utilities are best prepared to react to and prevent significant disruptions from weather events they are used to. It’s the anomalies that lead to wildfires and blackouts.”

The future looks great then, even if it is slow in coming, for scaling AI. If there is an issue, it is not with the applications. It lies with those applying it.

“There’s a digital divide,” says Kasselman. “What’s the age of those decision makers and what’s the state of their training? What value do they place on technology as a tool?” He sees what he calls “a greater digital alignment” with decision makers increasingly coming from the ranks of Millennials and GenZ.

The assumption there of course is twofold. First it assumes that some Boomers (certainly not all), now nearing retirement, are or have been a drag on the advancement of technologies. It also assumes, rightly, that the pace of tech, no matter which tech we are discussing, will only accelerate.

The scaling of AI is certainly no different




Media Contact
Alexis Fermanis SIOR Director of Communications
Steve Lewis
Steve Lewis
Wordman Inc.

Steve Lewis is a freelance writer and president of Wordman, Inc. He can be contacted at wordmansteve@gmail.com.