Artificial intelligence is expected to radically transform energy management over the next decade—from data centers and industrial facilities to potentially even residential systems. This forecast was shared by Mikhail Vazisov, General Manager of Ippon, one of Russia’s leading manufacturers of uninterruptible power supplies (UPS) and energy security solutions.

Why AI Is an Ideal Energy Manager
According to Vazisov, the key factor is the direct link between economic performance and energy costs. AI can optimize consumption, storage, generation, and electricity distribution in real time, extracting maximum efficiency from equipment.

He stated that his personal forecast was that within 10 years the industry would see tangible steps toward using AI as a tool for managing energy resources. Because the economy is closely tied to energy prices, saving electricity alone could provide a significant competitive advantage. By controlling usage scenarios—as well as storage, generation, and distribution—AI could deliver real benefits, optimize operational schemes, and ultimately reduce production costs.

Savings could reach 5–10% depending on the scenario—already a meaningful figure for large consumers such as data centers, where every percentage point matters. However, even data center operators do not yet have precise global estimates.

Different Speeds Across Industries and Regions
The adoption of AI in the energy sector is expected to be gradual and uneven. Vazisov pointed to several causes of delay, including limited infrastructure, differing corporate priorities, and geographic disparities.

He explained that some organizations are already prepared and understand the advantages, while others remain conservative. Geography also plays a role: Western countries are keeping pace with technological change, China is actively advancing, and Russia is somewhat behind for a variety of reasons.

AI is likely to appear first in mission-critical segments such as data centers and UPS systems, where energy security is paramount. In these areas, the transition could occur within the next five years.

For residential systems and small businesses, however, AI assistants are not yet technically justified and are largely driven by marketing considerations—the equipment remains too expensive, and the savings do not offset the investment.

Benefits and Risks: From Efficiency to Cyberattacks
The advantages are clear: optimization, cost reduction, and improved reliability. Yet there are also serious drawbacks.

First, the effectiveness of these new solutions has not yet been fully proven in practice. Second, integrating AI with existing systems is complex and costly—the AI market is expanding faster than infrastructure can adapt.

In Vazisov’s view, the greatest threat is cybersecurity. He stressed that risks must be assessed realistically, as an attack on such a node could theoretically disable an entire business.

If AI becomes the central controller of an energy system, a breach could paralyze a manufacturing plant, a data center, or even an entire industrial cluster.

Global Context: AI and Energy
Ippon’s forecast aligns with global trends. According to the International Energy Agency (IEA), data center electricity consumption is expected to double by 2030, potentially reaching 945–1,050 TWh—more than Japan consumes today. AI accelerators are growing at roughly 30% annually, increasing pressure on power grids.

At the same time, AI itself could become a powerful optimization tool, enabling predictive maintenance, intelligent load distribution, and better integration of renewable energy sources.

In Russia and other countries with a more conservative approach to energy digitalization, the transition may take longer—but in data centers and large-scale industrial operations, it appears inevitable.

In Brief
Mikhail Vazisov of Ippon predicts that within the next decade AI will become a primary tool for managing energy resources—from optimizing consumption and storage to distribution and generation. Potential savings could reach 5–10%, but adoption will be gradual: data centers and UPS systems are likely to lead (possibly within five years), while homes and small businesses will follow later. The main barriers are infrastructure, integration costs, and cybersecurity, as an attack on an AI-controlled system could disrupt an entire business.