Intelligent Decentralization Becomes the New Blueprint for AI-Enabled Energy Infrastructure

The traditional power grid, a century-old marvel of centralized engineering, is becoming dangerously obsolete. Its rigid, top-down hub-and-spoke model, built for a simpler era, cannot keep pace with the demands of electric vehicles, distributed energy, and the massive power needs of AI data centers. The only way to manage this complex, decentralized future is to rebuild the grid with a new operating system: artificial intelligence.

Besnik Sulmataj, an AI systems builder and Customer Service Manager at electrification service provider Qmerit, focuses on closing the gap between energy infrastructure and intelligent systems. He believes the industry needs a mindset shift, where AI is no longer treated as a feature added to the grid but recognized as its very foundation.

"Shifting towards intelligent decentralization would be a fundamental paradigm change from the traditional hub-and-spoke model we've had. This means moving away from solely relying on large, centralized power plants and towards a more distributed, networked approach, and AI is the only way to coordinate these distributions at scale. Without it, we're stuck with the outdated top-down energy flow that cannot adapt fast enough," Sulmataj says.

The vision of "intelligent decentralization" is already taking shape in the form of microgrids. These are not abstract concepts but tangible, real-world systems that can power critical infrastructure and communities with a new level of autonomy and resilience.

• The rise of microgrids: "A real-world example would be the rise of microgrids," Sulmataj explains. "These are smaller, localized energy systems that can operate independently or connect to the main grid. They might power a university campus, a hospital, or even a small community, integrating local solar, wind, and battery storage. This includes residential solar panels with battery storage, smart thermostats, and even EVs with two-way charging."

• Foundation, not a feature: For this new model to succeed, Sulmataj insists that AI cannot be treated as a superficial layer of software analytics. "AI is embedding itself into the core operating model of the grid," he says. "AI must be part of a grid's foundation, not just a dashboard that you add on later." This AI-native approach is the only way to build a system that is truly adaptive and intelligent.

Of course, a grid operated by algorithms introduces a new class of vulnerabilities. The security challenge is no longer just about guarding a physical perimeter; it’s about protecting the data, logic, and decision-making processes of the AI itself.

• A new security imperative: "You cannot secure tomorrow's grid with yesterday's assumptions," Sulmataj warns. "So we need an AI-native security approach that protects not just the control systems, but the very decision logic and the data pipelines that power our intelligent grid."

• A call for collaboration: This technological and security transformation cannot happen in a vacuum. Sulmataj argues that its success hinges on breaking down the traditional silos that have long separated key business functions, demanding new levels of cooperation between public and private sectors. "Leaders must foster unprecedented collaboration," he urges. "All these teams—the operations and engineering, the AI leaders, and energy leaders—must come together. AI and energy require a holistic view where experts from different domains co-create solutions."

Ultimately, Sulmataj reframes the entire purpose of integrating AI into our energy infrastructure. While most discussions focus on efficiency and cost savings, the true measure of success is far more critical. The true goal isn't just a faster or cheaper grid, but one that can withstand the shocks of an uncertain future. "Efficiency is important, but the primary goal of AI integration should be to create a grid that's highly resilient to both cyber threats and physical disruption."