This article was originally published on ai4business.it on February 16, 2026.
AI is transforming energy efficiency, offering tools to optimize industrial processes, buildings, and transport, with potential for significant savings. However, the expansion of data centers is increasing global electricity demand. The challenge is to direct its use strategically, maximizing energy benefits and grid flexibility without compromising overall sustainability
Artificial intelligence is forcefully entering the debate on energy efficiency, bringing with it a contradiction that cannot be ignored. On one hand, it is seen as a tool that can help reduce consumption, optimize industrial processes, and make the energy system more flexible. On the other, it is itself a cause of significant growth in electricity demand, driven by the expansion of data centers. It is precisely in this unstable balance that the true game of AI in the energy sector is played.
The potential of AI in industry is undoubtedly enormous. Algorithms allow for direct intervention at the heart of production processes, identifying inefficiencies and optimizing plant operation without necessarily resorting to new infrastructural investments. It is not just about automation, but a new capability to “read” industrial processes in real time to understand their conditions and intervene promptly when necessary.
If adopted on a large scale, AI could generate up to 8 exajoules of energy savings by 2035, a value comparable to the current energy demand of the entire European industrial sector.
The conditions for developing potential
Yet, this potential does not develop “on its own” automatically: it requires the availability of adequate digital infrastructure, access to specialized skills and, above all, a corporate culture capable of considering algorithmic results to make decisions. These needs weigh particularly heavily on small and medium-sized enterprises and risk creating a gap between those who can afford innovation and those who are left behind.
Concrete applications: industry, buildings, and logistics
The Italian context offers concrete examples of how AI can be effectively integrated into energy efficiency strategies. Several companies are already using artificial intelligence and machine learning models to optimize production processes, monitor consumption, predict failures in industrial components and systems, improve plant management in main and auxiliary services for industrial production, building climate control, and more.
AI is, therefore, not an abstract technology, but an operational tool that allows for waste reduction, improved energy performance, and process optimization.
In commercial and smart buildings, sensors for measuring environmental variables, fault detection systems, and intelligent automation allow for dynamic coordination of equipment, with estimated energy savings between 5% and 40%. In this case, too, AI acts as an efficiency multiplier, enhancing existing systems rather than having to renovate or even replace them.
In the transport and logistics sector, AI helps rethink distribution models, particularly in urban areas. Demand forecasting, delivery route optimization, and parts inventory management favor more efficient “hub-and-spoke” models, reducing traffic congestion and atmospheric emissions.
The energy cost and the strategic challenge
However, the “energy cost” of AI remains, which cannot be ignored. The expansion of data centers is today one of the main factors in the growth of global electricity demand. Estimates indicate that their consumption could double by 2030, reaching approximately 945 TWh, with an average annual growth of 12% since 2017. This data fuels legitimate skepticism: does it make sense to promote a technology that, while enabling efficiency, consumes more and more energy?
The answer is by no means obvious and must be sought by analyzing the numbers and taking into account all the uncertainties of the context. From this perspective, the predicted energy savings potentially enabled by AI can far exceed the additional demand forecast for data centers. The problem, therefore, is not AI itself, but the absence of a strategy that directs its use toward high-value energy and systemic applications.
In this sense, AI also becomes a key element for the flexibility of the electricity grid. Through intelligent demand management, algorithms can automate the shifting of consumption to hours when energy is more available or less expensive, contributing to system stability and the integration of new sources.
Conclusions
AI is neither a miracle solution nor a danger to be avoided. It is a powerful tool to be developed carefully and safely because it amplifies both the opportunities and the contradictions of the energy transition. The real challenge is not deciding whether to use it, but where and how to use it; what must be done is orienting it toward efficiency, flexibility, and waste reduction, rather than letting it grow without a coherent energy vision.
What is at stake is not just technological innovation, but the very credibility of the energy transition.
