By now, almost every company – regardless of sector – is beginning to introduce AI into their operations. In 2026, conventional wisdom suggests that any business still hesitating on AI is being left behind. One particularly practical subset of AI is machine learning. Unlike large language models like ChatGPT and Gemini, machine learning programmes are long-term projects which use a company’s data to learn and refine themselves over time. This kind of iterative improvement makes machine learning platforms especially well suited to industries like energy, where operations are constant and data sets are unfathomably large. But what exactly can machine learning bring to the modern energy firm? Some of its current use cases include optimising grid loads and predicting equipment failures before they even occur. Looking ahead, though, there is no telling how far machine learning could take the sector. In fact, many tech and energy companies alike believe that machine learning could be the thing that helps society achieve net zero and stave off the worst effects of climate change. In this week’s Top 10, Energy Digital takes a look at some of the most impressive machine learning (ML) platforms currently available to the world’s energy firms. 10. Uptake Founded: 2014 HQ: Chicago, Illinois, US Notable feature: Predictive asset health scoring across wind, solar, and gas fleets Uptake has carved out a strong niche for itself in industrial AI, with a particular focus on helping energy operators extract meaningful intelligence from machine data. Its platform is able to ingest data from turbines, compressors and grid assets, which it then uses to flag anomalies and recommend actions before systems fail. For utilities that are managing tired, ageing infrastructure, Uptake's model can provide a low-friction entry point into the world of machine learning. 9. Avathon Founded: 2013 HQ: Austin, Texas, US