A global review published in Energies reveals the transformative role of artificial intelligence in advancing energy productivity, driving industrial transformation, and accelerating digitalization. Their research highlights how AI-driven technologies are reshaping energy economics, enhancing efficiency, and promoting sustainable practices in key sectors worldwide.
The study, titled “The Impact of Novel Artificial Intelligence Methods on Energy Productivity, Industrial Transformation and Digitalization Within the Framework of Energy Economics, Efficiency and Sustainability,” provides an in-depth analysis of the opportunities and challenges posed by AI adoption in energy-intensive industries, identifying both measurable gains and pressing risks that need to be addressed.
AI as a game-changer in energy and industry
The authors analyzed 61 studies published mainly between 2016 and 2025 to examine where AI applications have made the most substantial difference. They found that machine learning, deep learning, edge AI, and digital twins are transforming energy management by enabling real-time forecasting, automated decision-making, and predictive maintenance.
Concrete outcomes include up to 30% reductions in energy use for HVAC systems in cold storage, 10–20% savings in industrial energy consumption through predictive maintenance, and 23.9% lower natural gas use in buildings equipped with predictive controls. AI-based irrigation systems achieved 50% water savings, translating into significant reductions in energy needed for pumping. In agriculture, energy intensity for indoor lettuce production dropped from approximately 9.5 to 6.4 kWh per kilogram.
These results demonstrate not only the immediate cost and efficiency gains of AI integration but also its potential to support long-term decarbonization goals by reducing resource consumption and emissions across sectors.
Challenges hindering widespread adoption
While the report highlights compelling achievements, it also draws attention to persistent barriers that limit the scalability of AI solutions. The authors stress that data quality, bias, and limited explainability of “black-box” models undermine trust and limit their deployment in critical energy systems. The increasing digitalization of energy assets also raises cybersecurity concerns, as connected AI-driven infrastructure becomes a more attractive target for cyberattacks.
Another challenge is the high upfront cost of integrating AI technologies, which often deters small and medium-sized enterprises from adopting advanced systems. Legacy infrastructure and lack of standardization in many industries further complicate integration. The authors argue that without deliberate policies to bridge these gaps, the benefits of AI may remain concentrated in technologically advanced regions.
The paper also raises social and equity concerns, noting that the pace of AI adoption could displace certain jobs while creating others that demand new digital skills. Policymakers, it says, must address the need for workforce reskilling and fair access to AI-powered solutions, ensuring that progress in energy productivity benefits all stakeholders.
Policy directions and future opportunities
The study calls for robust policy frameworks to guide the safe, equitable, and transparent deployment of AI in energy and industrial systems. The authors advocate for explainable AI to enhance trust in automated decisions, as well as for the development of secure-by-design protocols to safeguard critical infrastructure.
They also highlight the importance of hybrid AI models, which combine domain expertise with machine learning, and multi-agent systems that can coordinate decentralized energy grids. Further innovation is expected from AI-based digital twins, which enable simulations of energy systems for optimization and predictive maintenance, and from green AI, which reduces the energy footprint of computing itself.
The authors foresee promising advances in quantum computing and neuromorphic AI, which could tackle the complexity of large-scale energy networks and support more precise optimization in renewable energy integration and demand response.
