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AI and Emerging Technologies for Enterprise Asset Management Systems

Enterprise asset management systems integrated with AI and knowledge graph capabilities equips decision-makers to maximize asset performance.

Executive summary
  • A transformative shift from reactive to proactive asset management, powered by data and analytics, heralds a future-ready approach to pre-empt asset failures.
  • Integration of asset management systems with LLMs can result in heightened efficiency, superior decision-making and a fluid user experience.

Effective enterprise asset management has become a strategic imperative for organizations across the manufacturing and transportation sectors. Managing complex asset networks, disparate data sources and real-time insights is critical for achieving cost savings to maintain a competitive edge and drive operational excellence. By leveraging data, technology and advanced analytics, organizations can monitor assets in real time, pre-empt potential issues and optimize maintenance activities. This proactive stance with preventive and predictive strategies replaces a wait-for-failure approach, thereby enhancing asset performance, minimizing downtime and yielding cost savings.

The global big data analytics market size was valued at US$271.83 billion in 2022 and is projected to grow from US$307.52 billion in 2023 to US$745.15 billion by 2030.1 Owing to technological advancements, such as Internet of Things (IoT) sensors, connected devices and edge computing, organizations generate a significant volume of asset data, which includes performance metrics, maintenance records and sensor readings. To convert these large data sets into actionable insights, advanced analytics techniques, such as machine learning, statistical modeling, pattern recognition, anomaly detection and predictive forecasting, are essential.


The global artificial intelligence (AI) market size was valued at US$428.00 billion in 2022. The market is projected to grow from US$515.31 billion in 2023 to US$2,025.12 billion by 2030, exhibiting a CAGR of 21.6% during the forecast period.2 AI enhances asset management by bolstering data analysis capabilities, automating tasks, and accurately predicting asset performance and maintenance needs.

Leveraging knowledge graphs to determine enterprise asset management systems hierarchies

The need for real-time business insights using rapidly growing IoT-generated data points and demand for efficient low latency queries fuel the need for a more efficient data structure, such as knowledge graphs. Asset management poses the challenge of overseeing a network of intertwined assets, where understanding their interdependencies is vital for effective planning and decision-making. Asset hierarchies simplify this task by organizing assets into a relational structure based on subsystems and components. Examining this structure allows organizations to pinpoint critical assets with significant impacts on system performance. Such relationship analysis enables organizations to discern critical paths, anticipate maintenance needs and prevent failures by addressing issues proactively.

Data integration, optimization and geospatial capabilities of the enterprise asset management system

This amalgamation facilitates real-time reports and visualizations, aiding proactive asset management strategies. It centralizes information, eradicating data silos and providing decision-makers with accurate and current asset information. Integration with additional platforms, such as purchase management and weather systems, enables the consideration of external factors impacting asset performance. 

Spatial data integration in asset management enables geospatial asset visualization, enhancing decision-making via geographic information system (GIS) technology. This spatial perspective optimizes asset placement, boosts utilization and refines maintenance operations. Furthermore, it aids in routing optimization for maintenance crews, promoting operational efficiency, reducing response times and leading to cost savings.

A balance of preventive and predictive maintenance minimizes disruption and optimizes resource allocation. Detection of patterns indicating potential failures prompts proactive asset maintenance, preventing costly breakdowns, reducing unplanned downtime and prolonging asset life-span.

Contextualizing worker notes using natural language processing

There is increased awareness and popularity of using natural language processing (NLP) methods to improve business outcomes. NLP market size is projected to grow from US$24.10 billion in 2023 to US$112.28 billion by 2030, at a CAGR of 24.6%,³ indicating a massive rate of adoption among organizations across industry segments. The true value of maintenance records emerges from its contextualization, enriching raw figures with additional information, such as procedures, technician notes and environmental conditions. This contextualized data can be leveraged to glean insights into performance trends, recurrent issues and maintenance patterns, allowing decision-makers to optimize processes, cut costs and pinpoint training needs. Furthermore, contextual insights can evaluate personnel performance and boost maintenance operations overall. This rich detailing of activities facilitates information sharing across teams, promoting collaboration and informed decision-making, thereby utilizing the organization’s collective knowledge and experience to its fullest extent.

Integration of enterprise asset management systems with Large Language Models (LLMs)


LLMs excel in processing these complex requests by leveraging its advanced language-processing capabilities. Users can pose intricate questions related to asset data, maintenance schedules, performance metrics or any other relevant information. LLMs can provide users with instant access to real-time asset data, maintenance status, work orders and other relevant information. Users can inquire about the current status of assets, receive updates on ongoing maintenance activities and get real-time notifications on critical events. Real-time access to asset information is critical for effective decision-making and timely actions, as it reduces the burden on personnel to manually gather and process data, allowing them to focus on higher-value tasks. 


One of the key advantages of leveraging an LLM is its ability to continuously learn and improve over time. As users interact with the enterprise asset management system, LLMs can learn from these interactions and adapts to better understand user preferences, context and intent. Continuous learning ensures that the system evolves and becomes more proficient in addressing user needs and delivering relevant information. In summary, integration of LLMs within the enterprise asset management system can enhance collaboration, efficiency and decision-making. Streamlining communications, processing complex requests and providing real-time information, empowers users to make informed decisions quickly and effectively. The integration enables organizations to leverage intelligent assistance and drive efficiency in their asset management processes.

The views reflected in this article are the views of the author and do not necessarily reflect the views of Ernst & Young LLP or other members of the global EY organization.


Integration of a traditional enterprise asset management system with knowledge graphs and AI can offer a transformative outlook that empowers organizations to develop proactive maintenance strategies, harness data and analytics, and maximize asset performance, hence acting as a critical asset management catalyst, unlocking the true potential of assets and positioning organizations for successful navigation of the dynamic business landscape.

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