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AI, in its many forms, is having a revolutionary effect across different industries and applications. In industrial environments, sensor-based AI allows production line systems to learn from data and make predictions and decisions for process optimization. Meanwhile, AI can also analyze and interpret visual information from images or videos, enabling significant advances in areas like autonomous vehicles on the shop floor. And then there is generative AI, with natural language processing allowing computers to understand, interpret and create human language – radically enhancing how we interact with software. AI, in short, is making a huge impact everywhere and anywhere across industrial operations.

Improved asset management

Asset Management is not exempt from this transformation. EAM, in particular, is an area where AI is set to have a significant impact. In recent years, EAM software have proven a popular way of managing and maintaining physical ‘things’ such as buildings, plants, machinery, and vehicles. Such platforms are increasingly deployed by organizations in sectors such as chemicals, food, and logistics to improve reliability, streamline operations, and help ensure the highest levels of health and safety. More specifically, EAM can provide a comprehensive overview of the productivity and costs of assets and enable the optimization of uptime through ideally coordinated maintenance management.

But technology is moving fast. Now, integrating AI into EAM platforms could radically enhance capabilities, resulting in higher employee productivity and further maximizing asset performance, with all relevant data protection in place. By leveraging AI, future EAM toolsets will be more intuitive, accessible and predictive, therefore driving unprecedented efficiency and effectiveness in asset management practices. That is the exciting potential, for sure, but getting there means some obstacles still need to be overcome.

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