In today’s hyperconnected economy, supply chains are only as strong as the infrastructure that powers them. Yet, the U.S. electricity grid—much of it built in the mid-20th century—is buckling under the dual pressures of aging infrastructure and surging demand from AI-driven data centers. The Energy Information Administration (EIA) projects global electricity demand could increase by about one-third to three-quarters by 2050, depending on the case[1]. As AI becomes a cornerstone of modern business operations, its energy appetite is reshaping the grid and exposing vulnerabilities that could ripple across global supply chains. For C-level executives, the question is no longer if disruptions will occur, but how to prepare. Can AI, paradoxically, be both the disruptor and the solution? How can C – Level executives leverage AI to future-proof their supply chains against energy volatility and infrastructure fragility?
Virginia’s Crossroads: Balancing AI Growth and Energy Demand
Northern Virginia, home to the world’s largest concentration of data centers, offers a cautionary tale. Despite its strategic location and robust digital infrastructure, the region is now facing electricity supply wait times of up to seven (7) years[2]. This bottleneck is not due to a lack of investment—billions are pouring into AI infrastructure—but rather the inability of the existing grid to meet the explosive demand for power. One striking example is xAI’s Memphis facility, which resorted to using portable gas-fired generators due to delays in grid access[1]. These generators are significantly more expensive and less efficient than grid-connected power, but they were the only viable option to keep operations running. This scenario underscores critical vulnerability: even the most advanced AI systems are powerless without reliable electricity. The implications extend beyond tech. Manufacturing, logistics, and retail sectors—already strained by geopolitical tensions and climate disruptions—now face a new threat: energy insecurity. If the grid falters, so too does the digital backbone of modern supply chains.
The grid we have today wasn’t designed for the world we’re building. Originally conceived for one-directional electricity flow, it now faces volatile, multi-directional energy demands driven by AI, extreme weather, and distributed sources. AI data centers are projected to consume 12% of U.S. electricity by 2028, up from 4.4% in 2023
The image is a line graph titled “Projected U.S. Electric Grid Usage by Data Centers (% of Total, 2023–2028)”, illustrating the anticipated growth in electricity consumption by data centers across the United States over a six-year period.
From 2023 to 2028, the graph shows a consistent upward trend in the percentage of total U.S. electric grid usage attributed to data centers. Starting at approximately 4.5% in 2023, the usage is projected to rise steadily each year, reaching around 12% by 2028. This indicates a significant increase in energy demand from data centers, reflecting their expanding role in powering digital infrastructure and cloud computing.
AI’s Dual Role: Risk Amplifier and Strategic Enabler
According to Avasant’s Intelligent Infrastructure Services 2024 RadarView™, over 65% of enterprises cite grid modernization as a top priority for operational continuity, especially in AI-intensive environments. AI is both a driver of grid strain and a potential solution. Executives must understand this paradox to deploy AI effectively.
AI as a Strategic Enabler
Despite its energy demands, AI also offers powerful tools to enhance grid and supply chain resilience:
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- Predictive Analytics & Scenario Simulation: AI can model thousands of supply chain scenarios, identify vulnerabilities and recommend contingency plans. For example, AI-driven “cruise control” systems in industrial buildings have doubled energy reserves and cut costs by 10% in European trials[3]
- Real-Time Grid Optimization: AI systems are already being used to monitor transmission lines, isolate faults, and balance supply and demand in real time—capabilities far beyond human operators4
- Load Shifting & Demand Forecasting: AI can smooth out energy usage by shifting non-critical operations to off-peak hours, reducing strain on the grid and lowering costs.
However, AI is not a standalone solution. RAND corporation researchers found that when too many grid operators adopted AI without coordination, overall performance declined due to unpredictable decision-making.
From Fragile To Agile: Rethinking Resilience Across The Value Chain
True resilience requires more than AI. It demands diversified infrastructure, supply chain redundancy, and proactive energy strategy.
C-Level executives can strengthen their operations through the following approaches:
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- Champion resilience as a competitive differentiator.
- Treat energy strategy as core to digital transformation.
- Empower cross-functional teams to integrate AI, infrastructure, and supply chain planning.
Takeaway: Strategic Actions for C-Level Leaders
According to Avasant’s RadarView™, Enterprise Resilience Benchmark 2025 organizations with diversified energy sourcing and AI-driven risk modeling experienced 40% fewer supply chain disruptions during regional outages compared to peers.
To lead through the volatility of AI-driven energy demand and grid fragility, C-level executives must act decisively to transform risk into resilience. This requires more than operational tweaks—it demands a strategic shift in how enterprises model risk, engage with energy ecosystems, govern innovation, and design supply chains. AI is not just a disruptor; it’s a force multiplier when paired with foresight and infrastructure investment.
The following actions are essential for building a future-ready enterprise:
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- Invest in AI-Driven Risk Modeling: Leverage AI to simulate energy-related supply chain disruptions, identify vulnerabilities, and prioritize infrastructure and supplier investments.
- Collaborate on Energy Strategy: Partner with utilities and policymakers to accelerate grid modernization and ensure “speed-to-power” is a core criterion in facility planning. Strategic collaboration reduces energy wait times and accelerates AI deployment.
- Balance Innovation with Oversight: Establish governance frameworks for AI in energy and supply chain operations. Ensure transparency, auditability, and cybersecurity to prevent performance degradation and protect critical infrastructure.
- Mandate resilience as a capital planning requirement: allocate annual investment toward renewables, supplier audits, and geographic diversification.
Conclusion
The U.S. electricity grid is at a crossroads. As AI accelerates demand and complexity, the risk of supply chain disruptions grows. Meeting this challenge will require both advanced technologies like AI and broader strategies in energy diversification and governance. For C-level executives, the imperative is clear:
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- Embrace AI not just as a tool for efficiency, but as a cornerstone of resilience.
- True operational strength will come from pairing AI with diversified energy and supply chain strategies that reduce single points of failure.
- The future belongs to those who can power through uncertainty—literally and figuratively.
[1] https://www.eia.gov/pressroom/releases/press542.php
[2] The Electricity Supply Bottleneck on U.S. AI Dominance
[3] AI and the Future of the U.S. Electric Grid | RAND
By Lu Esan, Director, Supply Chain & Procurement & Matthew Lovelace, Manager, Supply Chain & Procurement