The integration of AI, IoT, and automation in cloud computing is changing the Enterprise Asset Management (EAM) sector at an unprecedented rate. Companies are integrating these technologies to improve the performance output of the respective assets.
Most Notable Changes in EAM
Offering Predictive Maintenance Services Using AI Enables Algorithms to Analyze Data
The greatest transformation in asset management has stemmed from AI predictive maintenance tools. In 2024, IBM's Maximo Application Suite bundled their products with enhanced AI analytical features, decreasing fault detection duration by 25%. Algorithms from machine learning utilize assets and their details to analyze predictions, thereby decreasing both repair costs and downtimes.
Asset Tracking Through the Internet of Things (IoT) Improves
Tracking through IoT is a positive development in operational visibility. In 2024, Infor began working with a logistics company to implement IoT-based tracking for fleet vehicles to improve asset management for their clients, resulting in the formation of a partnership. Sensors now provide real-time data for asset utilization, resulting in a drop in maintenance expenditures and a rise in improved operational efficiency.
Enhanced EAM Efficacy and Changes to Increased Tier Maintenance Adoption in Clouds
The adoption of cloud technology for EAM has greatly simplified asset management. Enhanced automated processes and improved security were added when SAP launched the new upgraded cloud-based EAM in 2024. These platforms have brought about a form of remote access that has fundamentally improved multi-location asset industries through better collaboration and central data management.
AI and ML in Asset Lifecycle Management
Machine learning insights are transforming asset lifecycle approaches within an organization. In June 2024, Copperleaf Technologies was acquired by IFS to further amplify their AI-powered asset management capabilities. The tools lower lifecycle spending and maximize asset exploitation in many resource-intensive businesses.
Mobile EAM Applications For Field Activities
Asset management monitoring is more efficient with mobility. Service request management improved after CentralSquare EAM was adopted by Pasco County Public Works in July 2024. Now, utility firms and even maintenance teams use mobile apps for asset inspections and updates in real-time.
Automation and AI-Enabled Analytics
A combination of technology and business processes creates a responsive organization. Hitachi Energy announced the new Lumada APM in October 2023, an asset performance management software that uses AI for comprehensive analytics. Reliability of assets is enhanced through automated processes, data analytics, and minimized operational expenses.
Industry Impact
These improvements are transforming the asset management sector through increased productivity, reduced errors, and instantaneous decision-making. Using AI and the IoT, predictive maintenance can be performed. Cloud solutions expand the reach and use of data, while mobile apps empower field professionals. As businesses digitize more processes, the future of Enterprise Asset Management (EAM) will be paved through automation, artificial intelligence, and highly engineered operational strategies, which will enhance asset performance while minimizing costs.
The global Enterprise Asset Management (EAM) industry is expected to reach around $2.7 billion in spending by 2030, growing at a CAGR of 8.9% between 2023 and 2030 due to the increasing digital transformation, predictive maintenance, and cloud adoption. Investing in artificial intelligence and Internet of Things (IoT) technologies puts firms ahead of their competitors because it decreases spending and enhances the value of their assets.
EAM is one of the first industries to embrace digital transformation, leveraging IoT, AI, and automation to achieve a new level of improvement in performance and management of assets. With the continued adoption of these solutions, advanced companies will be able to further sharpen their competitive edge within a data-driven world.
