As companies gathered for Climate Week, a tension was impossible to ignore: the same firms showcasing emissions‑reduction progress were simultaneously accelerating one of the most energy‑intensive buildouts in modern operations—AI data centers. These facilities have become a lightning rod for communities and regulators, especially as their power and cooling demands surge, reports The Wall Street Journal (Sept. 21, 2026).
For operations managers, the implications are profound. A standard AI‑ready data center consumes as much electricity as 100,000 households, and the largest facilities under construction will use 20 times more, driven primarily by cooling loads required to keep high‑density compute equipment stable. This is no longer an IT issue—it is an operations ecosystem challenge involving energy procurement, water usage, thermal engineering, community relations, and regulatory compliance.
Climate control is emerging as the defining constraint. Gas‑turbine backup systems raise air‑quality concerns. Water‑intensive cooling technologies trigger scrutiny in drought‑sensitive regions. States like Virginia are already issuing new standards for water use and energy efficiency, signaling a wave of regulatory pressure that will reshape site selection and facility design.
At the same time, national security priorities are pushing for rapid AI expansion, creating a dual mandate: scale fast, but scale responsibly. Companies are responding with renewable power purchase agreements, advanced cooling systems, and more transparent community engagement. Yet firms still struggle to communicate how their operational plans address environmental risks—especially to residents living near proposed sites.
For OM leaders, the strategic question is shifting from How do we build more compute? to How do we build sustainable, resilient, community‑aligned compute capacity? This requires integrating energy modeling, climate‑risk forecasting, water‑management strategies, and cross‑functional planning across engineering, facilities, and public affairs.
AI may ultimately help solve climate challenges—from grid optimization to disaster prediction—but only if its physical footprint becomes operationally sustainable. The next frontier in operations management is mastering the climate‑control and infrastructure demands of AI at scale.
Classroom discussion questions:
- How should operations managers balance the explosive growth of AI data‑center demand with rising environmental and community constraints?
- What innovations in cooling, energy procurement, and facility design could meaningfully reduce the climate impact of AI‑driven data centers?
- How does the community concern with the infrastructure demands of data centers relate to the “Factors That Affect Location Decisions” in Chapter 8, Location Strategies?
AI is rapidly shifting from digital decision‑support to hands‑on operational control. As AI systems move into the physical world, they are reshaping core OM functions—especially inventory management, quality assurance, and maintenance. 
Using this definition, 9% of all U.S. jobs are in the skilled trades, with 60% of those consisting of jobs in construction and manufacturing. For each person completing a training program, the skilled trades have 3 job openings, creating a talent gap of 1.3 million workers. Around 25% of workers in the skilled trades are 55 or older, meaning demand is expected to rise as workers begin to retire.
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Cities and transportation agencies worldwide are experimenting with this technology. In Greece, AI cameras identify drivers who fail to wear seat belts, who use cell phones, or who exceed speed limits. In Goa, India, 26 locations have installed cameras to identify similar violations. Mississippi uses AI cameras to alert officers to traffic violations. Houston is purchasing AI cameras to improve traffic flow. In the Dallas–Fort Worth area, AI cameras assist criminal investigations
There are about 5,000 finished or under-construction data centers across the U.S. Farmland is an attractive target for technology companies. Data centers need large amounts of flat land and access to water and energy, the same as farmers do. Tech companies have faced backlash from locals concerned about power usage and the strain on their local grids. Lawmakers in about two dozen states are considering banning or restricting their development.
That’s the new reality for many manufacturers facing a stubborn obstacle: the ever-widening gap between data and decisive action. Now, a new class of digital entities is changing that equation. AI agents powered by decision intelligence are beginning to sense, reason and act across the manufacturing ecosystem, cutting decision latency from minutes to milliseconds.
Unfortunately, the system, which was meant to make ordering easier, did anything but, with customers reporting that the automated ordering system added extra items to their tab. McDonald’s ended its partnership with IBM in 2024, but announced that it was exploring “voice ordering solutions more broadly.” Now, the fast food chain has found a new partner in Google to bring its Arch IQ and drive-thru assistant, Archy, back to life.
Professor Misty Blessley raises an interesting AI issue-chip plant scheduling.
TSMC ( Taiwan Semiconductor Manufacturing Company), the world’s largest semiconductor foundry, makes the most advanced chips on the planet. NVIDIA is a global leader in accelerated computing and AI. For decades, TSMC has manufactured NVIDIA’s chips, and this partnership has come full circle. TSMC uses NVIDIA’s AI technologies inside its fabrication plants (“fabs”), which in the semiconductor industry refers to highly specialized facilities where silicon wafers are processed into microchips.
At that time, Starbucks’ CTO wrote: “The tech is currently live across thousands of coffeehouses, and will be in use across the chain’s entire North American system by the end of September. At cafes using the AI systems inventory is now counted 8 times more frequently, giving us real-time visibility and enabling faster, more precise replenishment.”
