The Emerging Role of AI in Enterprise Asset Management

⇨ According to recent SAPinsider research, only 7% of respondents have “completed” AI/ML deployments in EAM, while many are planning deployments in the next two years.

⇨ Generative AI use cases in EAM could include a conversational front-end to maintenance documentation like manuals, operating procedures, and installation guides; or a maintenance virtual assistant or chatbot that guides maintenance personnel through work tasks.

⇨ A global food manufacturer has been experimenting with AI in EAM for two years, targeting everything from tractors to pumps and driving better decisions around asset throughput, utilization, and performance.

Much has been written about the potential impact of AI across all aspects of the enterprise, including Enterprise Asset Management (EAM). EAM includes the management, support, and maintenance of a company’s physical assets throughout their lifecycle. This lifecycle encompasses capital planning, procurement, installation, performance, maintenance, repair, regulatory compliance, risk management, and asset decommissioning and disposal. According to recent SAPinsider research, only 7% of respondents have “completed” AI/ML deployments in EAM, while many are planning deployments in the next two years.