Gonzalo Sanz Segovia | 09/02/2026
There is much debate about the environmental impact of AI, especially due to its high energy consumption. On the flip side, it’s also true that, when deployed well, AI can be a strategic lever in optimizing the use of energy, reducing emissions, and managing environmental risks. With the right governance, it’s shaping up to be a great ally against climate change.
AI’s enormous processing capabilities mean it can improve the efficiency of data centers, power grids, industrial processes, transport, and agriculture. It also facilitates the development of predictive models that can anticipate operational failures or extreme weather events, which is why it can be considered as a key tool in the transition to more resilient and sustainable energy.
AI is important in energy management because it converts large volumes of data into automated or assisted decisions that optimize consumption, coordinate resources, and reduce losses. The International Energy Agency highlights that the combination of AI and energy isn’t just a trend, but a real strategy with tangible effects. As electricity demand grows, AI offers effective solutions to improve system efficiency.
Reducing footprints and costs
Let’s take data centers as an example. Given the amount of electricity they require, efficient management is critical. Machine learning models allow for adjusting cooling, ventilation, and operation in real-time. A flagship case is the use of machine learning by DeepMind in Google data centers: they managed to reduce cooling energy consumption by up to 40% without compromising performance.
In electrical networks, AI helps forecast the generation of renewable energies like solar or wind, schedule storage, and prioritize resources. Agencies such as the Department of Energy in the USA are already identifying AI applications to plan, operate, and strengthen the resilience of their networks, with the aim of increasing their reliability and reducing costs and emissions.
Also in the industrial field, predictive models detect anomalies in equipment before they fail, preventing unforeseen shutdowns that represent significant energy waste.
In logistics and mobility, AI optimizes routes, fleets, schedules, and loads, all of which translate into lower energy consumption and a reduction in emissions. European institutions and research centers are driving the digitalization of transport as a clear way to advance in decarbonization.
Environmental risk management
Beyond efficiency, AI is also useful for anticipating and managing environmental risks thanks to its predictive capabilities:
- Early detection: AI can analyze satellite images and sensor data to identify deforestation, fires, or changes in water bodies.
- Climate models: AI can anticipate heat waves, floods, or supply interruptions, facilitating an early response.
- Impact assessment: AI simulations help prioritize investments in adaptation or mitigation, such as reinforcing critical infrastructures.
International organizations, such as the Intergovernmental Panel on Climate Change (IPCC), are incorporating AI into their scientific and review processes, having recognized its usefulness alongside the need for robust frameworks for its responsible use.
Advantages, challenges and recommendations
Although its benefits are evident, AI also poses numerous challenges. It’s not a neutral, agnostic technology: its uncontrolled use can increase electricity demand, introduce biases in automated decisions, or generate vulnerabilities to cyberattacks.
For AI to be a truly sustainable and strategic tool, it’s essential:
- To measure your entire energy footprint and prioritize efficiency
- To establish appropriate governance models
- To design KPIs aligned with sustainable objectives
- To promote public-private collaboration
Experiences like those of Google or recommendations from the International Energy Agency emphasize the importance of implementing measurable pilots, defining clear metrics, ensuring transparency in results, and having well-defined policies for the ethical use of AI.
Where can AI be deployed?
- Optimization of energy use, through smart grids and more efficient industrial processes.
- Boosting sustainable transport, improving traffic management, and promoting electric mobility.
- More sustainable agriculture, thanks to climate prediction and environmental stress detection.
- Reduction of industrial emissions, by identifying inefficient processes.
- Environmental monitoring, with satellite data analysis and predictive modeling.
- Design of materials and green technologies, through AI applied to scientific research.
- Decision-making support, based on the analysis of large volumes of data.
A feasible, and necessary, way forward
AI can act as an efficiency multiplier that reduces both costs and the environmental footprint, while strengthening the predictive and responsive capacity of organizations against the risks of climate change. Studies and success stories confirm that the benefits are real, as long as its implementation is carried out under principles of sustainability, with clear metrics and solid governance.
