Google was ahead of the curve when it started using an AI for Energy Efficiency to regulate its data centers. It realized that artificial intelligence could reduce the amount of energy that they consumed, making them more profitable and efficient. Now, though, in 2026, more businesses than ever before have access to these tools. They’re allowing them to reduce their energy costs and get more results from the specific energy input. What exactly is going on? How is AI being used?Â
Smart HVAC optimisation
Artificial intelligence is being used in many companies for smart HVAC optimization. Sensors are able to monitor variables like weather forecasts, humidity, and occupancy to determine how much energy to dedicate to heating and cooling. This means that systems are able to upgrade significantly over traditional timed or static schedules. They adapt and respond to real-world conditions, reducing energy use and emissions by between 10 and 20 percent in some cases. In retrofitted buildings, the energy savings can be even higher when systems have been inefficient previously, leaving more room to improve them in the future.
Predictive maintenance and fault detection
AI is also being used in the world of predictive maintenance and fault protection. This means that businesses can get a heads up on what might be going wrong with their systems before experiencing any downtime.
What’s nice about predictive maintenance is that it cuts inefficiency dramatically. While companies might be efficient when everything is running perfectly, they often have long void periods where things simply can’t happen because of maintenance requirements. These are costly and mean that staff often have to go home. With predictive maintenance, it’s possible to extend asset life and reduce depreciation. It’s also easier to schedule regular maintenance that fits around existing workflows rather than production shut-offs.
Renewable energy integration microgrids
We’re also seeing the emergence of artificial intelligence combined with commercial solar and microgrids. The idea here is to optimize the use of energy throughout the firm. For example, companies need AI systems to control:
- Energy storage
- Sales back to the grid
- How much energy to draw from the grid
They also need to know where energy is going and how it is being used in the organisation to optimise routing. Again, all of this data can be fed into central panels and dashboards through analysis by facilities managers.
Demand forecasting and load management
Finally, many companies using AI to predict demand and forecast load management are integrating various data streams like weather patterns and occupancy rates, enabling businesses to shift their usage to off-peak times or use more renewable energy solutions.
Demand forecasting previously required staff to understand complex software and then create reports based on data outputs. Now, though this is all changing, many companies are able to simply use AI to evaluate the data and provide them with predictions. They can then automatically feed this into other integrated systems on their premises.
So, which of these AI-based business energy upgrades will you be using at your firm?