Manufacturers are increasingly turning to AI-powered predictive maintenance systems to prevent costly equipment failures and unplanned downtime. Recent advancements in artificial intelligence, including generative AI and AI-powered robotics, are enhancing these capabilities.

Companies like Aquant are leveraging AI algorithms to analyze past maintenance data and live sensor information to more accurately identify issues requiring maintenance. Their platform is designed to filter out false positives and recommend specific actions to prevent failures.

Meanwhile, Gecko Robotics is deploying wall-climbing robots, drones and robotic dogs equipped with sensors and cameras to safely inspect critical infrastructure like power plants and oil facilities. The data collected is analyzed by Gecko's AI platform to detect problems before failures occur.

Some predictive maintenance providers are also integrating large language models to allow technicians to directly query systems about equipment health and maintenance priorities. This simplifies access to insights and enables faster, data-driven decision making.

However, implementing AI-driven predictive maintenance comes with challenges. The high upfront costs of installing sensors and integrating new systems can be a barrier for some manufacturers. There are also concerns about workforce skills gaps and resistance to new technologies.

Despite these hurdles, the market for predictive maintenance technology is projected to grow rapidly, reaching $70.73 billion by 2032 according to Fortune Business Insights. As capabilities evolve, manufacturers willing to invest in these advanced systems may gain a significant competitive advantage in reducing costly downtime and improving operations.