What is enterprise asset management (EAM)?

EAM can be thought of as an extension of an enterprise’s asset lifecycle management (ALM) strategy. It deploys specific technologies, systems and procedures in service of the organization’s broader ALM framework, incorporating elements of work management, energy management, asset maintenance, planning and scheduling, supply chain management and environmental, health and safety (EHS) initiatives.

Without a comprehensive EAM framework, organizations face a greater risk of service disruptions and equipment failures, limited visibility and reduced efficiency across business processes. These disruptions can quickly add up. For instance, downtime costs Global 2000 companies—Forbes’ ranking of the world’s largest enterprises by sales, profits, assets and market value—an average of USD 200 million each year, accounting for 9% of total profits, according to a 2024 Splunk report.

EAM also plays an important role in governance and security. Enterprises in highly regulated industries, such as healthcare, aviation and utilities, are often subject to more stringent EAM compliance protocols. For example, manufacturers in the United States must abide by special workplace safety and supply chain due diligence laws, which require them to undergo regular audits and inspections, monitor environmental impacts and track equipment conditions and performance.

In the Internet of Things (IoT) era, where everything from valves to vehicles is connected by sensors and systems, advanced analytics and artificial intelligence platforms have brought a deeper level of precision to EAM. The resulting insights can help enterprises improve decision-making, enhance efficiency and maximize investments.

Assets include virtually any piece of equipment needed to sustain production, services and operations—such as buildings and facilities, hardware and machinery, transportation fleets, manufacturing equipment and energy infrastructure. Today, many EAM strategies extend to intangible assets as well, including apps, software, patents and trademarks. The global value of intangible assets reached an all-time high of USD 79.4 trillion in 2024, up 28% from the year before, according to consultancy firm Brand Finance.

While EAM traditionally took place locally, enterprises increasingly rely on cloud-based EAM software to maintain assets and optimize usage. Cloud-based solutions enable organizations to moderate asset provisions in real time, adjust scale with precision, analyze robust usage data and integrate multiple architectures and geographic locations. The EAM market size is expected to grow to USD 13.7 billion by 2032, representing a 10.9% yearly growth rate, according to Fortune Business Insights.

EAM is often associated with a computerized maintenance management system (CMMS), but a closer look reveals key differences between the systems, especially in terms of their scope.

While EAM covers an asset’s entire lifecycle from procurement to disposal, a CMMS is concerned primarily with asset performance during the maintenance stage. Put another way, CMMS is often just one component within a wider EAM system, generally covering a narrower band of functions. EAM grew out of CMMS starting in the 1990s as companies sought a more comprehensive, cross-functional strategy for maintaining their assets.

A CMMS often takes the form of a centralized database where maintenance and operations teams can access information about every asset in the system. Through this central repository, teams can handle work order management, preventive and predictive maintenance, inventory management and auditing. Today, maintenance divisions automate many of these processes, enhancing their speed and accuracy.

Unlike CMMSs, EAM systems seek a more holistic view of a company’s assets by analyzing factors such as long-term value and lifetime cost. For example, while CMMS is concerned primarily with an asset’s physical condition and performance, EAM might also consider how procuring new assets or decommissioning outdated ones would affect the organization’s financial health. EAM involves collaboration between multiple departments and functions—not just the maintenance team—and is a better suited for monitoring assets across different environments and architectures.

Asset performance management (APM) can be thought of as a modern reinterpretation or evolution of CMMS. Like CMMS, it is relatively narrow in scope, with a focus on asset maintenance and optimization. However, it uses advanced analytics, digital twins and machine learning to not only assess current asset health but also predict future performance with more accuracy and precision than traditional methods.

Asset lifecycle management (ALM) is a high-level strategy that establishes a set of goals, such as improving efficiency and extending resource longevity, to optimize an organization’s asset usage. EAM, meanwhile, explores the practical steps a company takes to implement its ALM strategy, including which services, tools and frameworks it uses to maintain and optimize its resources.

Enterprise resource planning (ERP) takes an even broader view than ALM, incorporating all business operations, not just ones related to asset lifecycles. Enterprise asset management is often just one element of a larger ERP strategy.

One major benefit of ERP is that it consolidates data from across the organization by making it available through a centralized interface. While an ERP database contains data and resources related to the organization’s assets, employees can also access modules regarding human resources (payrolls, performance benchmarks and training resources), finances (budgets, forecasting tools and expense tracking), sales (customer data, lead tracking and order fulfillment) and more.

Organizations often build and refine EAM systems in multiple stages, beginning with basic asset tracking before moving to more advanced, computationally complex maintenance strategies. Common steps include:

• Planning: Assessing the scope of an organization’s maintenance needs, defining goals and key performance indicators (KPIs), consulting with stakeholders and choosing an appropriate EAM software solution.
  
  
• Data preparation: Collecting, integrating and standardizing asset data from across the organization. Identifying inaccessible, incompatible or corrupted data and closing information gaps.
  
  
• Duty allocation: Defining which teams are responsible for maintaining which assets. Setting clear boundaries between teams while creating open lines of communication across the organization. Balancing workloads, schedules and resources between departments.
  
  
• Rollout: Deploying the system, evaluating technical challenges and fine-tuning processes to achieve operational efficiency, compliance and sustainability.
  
  
• Refinement: Iterating on EAM procedures to streamline maintenance operations. Gradually implementing advanced frameworks and technologies, such as automation, machine learning, IoT integration and virtual asset simulations.

Many enterprises are still in the process of fully embracing an EAM framework. Roughly 15% of companies currently take a “reactive” approach to asset management, meaning they wait until a piece of equipment malfunctions before restoring or replacing it, according to a 2023 IFS report. Around 62% have more complex systems, with at least some ability to prevent assets from breaking down in advance.

An estimated 12% use advanced maintenance strategies such as condition-based maintenance (CBM) and risk assessments to proactively maintain equipment and assets. These systems reach beyond standardized maintenance schedules to identify vulnerable assets based on real-time metrics, offering a greater level of visibility and control.

EAM is important because it helps organizations track, assess, manage and optimize asset quality and reliability. Organizations of all kinds often manage hundreds, thousands or even millions of assets.

A cohesive EAM strategy can help enterprises in asset-intensive industries proactively care for equipment and resources throughout their lifecycle, rather than race to fix malfunctions and failures as they arise. EAM practices help maintenance teams gain greater control of complex environments by:

Many EAM systems use a CMMS to tell maintenance managers where an asset is, what it needs, who should work on it and when. CMMS databases often include historical information about critical assets, along with real-time data showing their current state and projections about how they might perform in the future. This centralized approach improves the traceability and accessibility of asset information for all relevant stakeholders.

Enterprise asset management software often features preventive capabilities that can help organizations maintain equipment for stable, continuous operation. Traditional EAM platforms can flag when a particular asset might need servicing or replacement based on historical trends. Modern systems, meanwhile, use sensors, data analysis and other mechanisms to monitor individual assets for signs of wear in real time.

For example, maintenance teams might receive a warning each time a computer reaches 25% battery capacity, indicating that it’s due for a battery replacement. These proactive measures help ensure contract compliance (alignment with the terms and conditions tied to a particular asset) and preempt issues such as service downtime that might otherwise disrupt core operations.

Remote monitoring tools, which often use AI to detect data anomalies and monitor behavior patterns, can deliver actionable insight into current and expected states of assets. These tools aggregate data across departments and information silos, enabling more accurate, comprehensive alerts and enhanced decision-making.

This monitoring extends to system configurations, helping ensure that aging architectures are routinely upgraded and maintained. Assets can be monitored through multiple methods, including WiFi-enabled tracking, QR codes, GPS and radio frequency identifier tags.

Some EAM systems include built-in risk management tools that can anticipate equipment malfunctions and suggest actions to protect against them. For example, EAM software might spot vulnerabilities in a central mainframe, prompting an enterprise to set up a backup. The redundant mainframe can take over if the original is knocked offline.

Risk assessments can also identify threats related to cybersecurity, compliance, safety, sustainability and budgeting. IoT, machine learning and advanced analytics can enhance monitoring practices, making them more accurate and robust. For example, AI-powered asset tracking and traceability—using advanced algorithms to predict emissions patterns, equipment status and other metrics—help enterprises meet increasingly complex environmental, health and safety (EHS) requirements.

Historical and real-time data collected from analytical and diagnostic tools can help extend the availability, reliability and usable life of physical assets. EAM systems help organizations make data-driven decisions regarding maintenance costs and replacements to maximize returns on investment.

An enterprise might determine, for example, that servicing a piece of machinery every three months can extend its life by several years. The opposite might also be true: Enterprises can scale back maintenance or replacement frequency if doing so has only a marginal effect on an asset’s end-to-end lifecycle.

EAM establishes a single system to manage virtually all asset types, promoting continuity across the organization. This strategy aligns teams around a shared set of data pipelines and resource usage goals, which helps ensure that each department draws from the same metrics while drafting asset strategies.

While asset management solutions vary widely depending on an organization’s specific goals and priorities, the central pillars of an effective EAM strategy often include:

EAM platforms centrally manage and track maintenance activities, including both planned and unplanned work, from initial request through to completion. EAM workflows might also incorporate actuals reporting, or keeping a record of revenue and expenditures throughout an asset’s lifecycle.

Advanced EAM strategies move from corrective maintenance, when repairs are made after a problem occurs, to preventive maintenance, when repairs are scheduled in advance, and finally to predictive maintenance, when repairs are made because data indicates imminent failure.

EAM systems might display work orders and preventive maintenance schedules graphically with a Gantt chart (a bar chart that visualizes project timelines). They can also automate maintenance schedules and assignments, freeing up dispatchers to prioritize more urgent tasks.

EAM platforms integrate assets and their maintenance materials into the supply chain, often with the help of IoT technologies and advanced analytics. They also manage an inventory of spare parts and critical components so that assets can be quickly replaced in response to a failure. Finally, they maintain oversight over the supply chain to prevent bottlenecks and shortages.

EAM systems use incident analysis, corrective action traceability and change management to comply with health and safety regulations. They often feature a robust reporting strategy to document security vulnerabilities, governance concerns and EHS conflicts. They also include detailed guardrails so that teams can align around a shared set of rules and policies.

EAM platforms read meters, bar codes and radio frequencies to capture electronic signatures, aiding with remote monitoring and oversight. They might also take advantage of mobile device capabilities, including cameras and voice-to-text, to improve information-gathering, promote collaboration and enable offline tool access.

EAM systems run descriptive and diagnostic analytics, often powered by AI, to gain operational insights into the underlying causes of inefficiencies. They use optimization models to automate planning, scheduling and work management processes. They might also prescribe operational changes that can extend asset lifecycles and reduce inefficiencies.

EAM solutions increasingly support software as a service (SaaS), cloud-based deployment or hybrid cloud deployment to control costs, improve system flexibility and decrease dependency on IT.

As organizations replace their traditional on-premises architectures with multi-cloud or hybrid environments, they might find it more difficult to track and manage assets. To address this problem, many enterprises turn to cloud-based EAM solutions for help optimizing asset usage and restoring operational oversight.

Many EAM software providers use a SaaS model, charging enterprises a monthly or annual fee for access to EAM capabilities. Common options include IBM Maximo, SAP EAM, Oracle EAM, IFS EAM, MaintainX and Facilio. Enterprises might rely on just one EAM product or use a variety, with each specializing in a different industry or capability.

Cloud-based EAM solutions give enterprises the flexibility to expand and contract their resource provisioning based on data demands. Users pay only for the data resources they need. When service providers make upgrades in the cloud, clients can immediately access the new and improved service. Most modern EAM platforms also offer access to advanced analytics and AI tools, including digital twins, prediction-based modeling, sustainability tracking and more.

Finally, because most SaaS solutions offer built-in technical support, organizations are also less dependent on in-house IT departments. As a result, capital expenses related to IT can be converted into operational expenses and resources.