What is AI asset management?

AI-driven asset management is revolutionizing how asset managers, operations managers and maintenance teams keep operations running.

AI-powered asset management encompasses a range of areas, including portfolio management, wealth management, risk management, investment management and compliance. Asset management solutions powered by AI are applicable across asset portfolios, including real estate and facilities, manufacturing, renewables, IT assets and digital assets. Asset management can refer to financial services or the operational side of a business. This article aims to cover AI asset management for critical physical equipment and infrastructure.

Unified asset and facilities management solutions are using generative AI for asset management, combined with traditional AI, to optimize workflows, automate tracking and mitigate workforce constraints. These foundation models are built on large language models (LLMs) and trained on a vast amount of data.

The technology is moving organizations away from scheduled or reactive maintenance to continuous, data-driven asset oversight. Through predictive analytics and real-time performance monitoring, organizations can maximize their asset value.

Intelligent AI systems for physical assets are transforming the entire business equation for organizations and helping chief executive officers (CEOs) increase operational efficiency and reliability.

Modern enterprises are facing more complexity, costly unplanned downtime and big data. Tumultuous market conditions and unpredictable market trends are making it difficult for organizations to plan ahead. AI-driven solutions can help mitigate and prevent these challenges before they become operational problems by amplifying important processes, such as asset integrity management (AIM) and asset tracking.

In recent years, it’s become clear that more organizations are recognizing the value of AI platforms. According to a report from the IBM Institute for Business Value, 71% of executives say gen AI fundamentally changes how they will manage assets. Similarly, 72% say it increases the strategic value of physical asset management to their enterprise.

Across industries, equipment failure is one of the most disruptive and expensive operational challenges a team can face. AI models can help mitigate equipment failures by performing predictive maintenance and forecasting potential outages.

Modern operations equipment generates more sensor data than maintenance teams can process. AI asset management solutions can streamline data processing and use it to drive more strategic decision-making and smarter investment decisions.

Other challenges include aging infrastructure and a gap in institutional knowledge as experienced personnel retire. AI tools offer scalable technology options that can address these gaps and apply that knowledge at scale.

AI asset management involves several key steps and core AI technologies to drive the process.

Internet of Things (IoT) devices, sensors and drones continuously produce large amounts of data. This data is vital to the asset performance management process because it’s the source of the power that everything else depends on.

Different types of sensors capture different types of data, such as vibration, temperature, pressure, acoustics and fluid levels. It’s important to recognize that sensor data serves as the input layer for the asset management process. AI outputs are directly tied to clean, continuous data.

Machine learning (ML) models learn from historical data what “normal” looks like and monitor incoming data for deviations.

Enterprise asset management (EAM) platforms integrate AI and ML natively to flag hidden issues and detect anomalies before they cause unplanned downtime. Essentially, ML gives every piece of equipment its own continuous health monitor, and it is attuned to how the asset behaves.

Like machine learning, predictive analytics uses historical data and real-time data to forecast when an asset or component is on the decline or on the verge of failure.

Predictive analytics uses AI algorithms to analyze data and calculate the remaining useful life of components and schedule targeted maintenance on an as-needed basis. This level of insight allows maintenance teams to stop relying on fixed calendar schedules and schedule work based on actual asset condition.

Natural language processing (NLP) reads and interprets unstructured data, such as maintenance logs, technician notes, work orders and failure records. The technology can surface patterns across thousands of text records and turn them into insights that would otherwise stay buried.

The adoption of AI and NLP can enable users to search repair histories and recommend solutions with ease.

Generative AI helps asset management teams with day-to-day tasks and workflows. Gen AI is less about prediction and more about delivering original content, such as work orders or maintenance reports.

The technology helps asset managers act on AI-generated insights faster and reduces the administrative burden. Gen AI isn’t meant to replace human judgment or skills—it handles information gathering and tedious communication tasks that take time away from actual maintenance work.

Agentic AI is forthcoming, but it’s not yet a mainstream technology for asset management.

Asset managers use AI throughout the asset management process to automate tasks and predict failures. However, agentic AI is a complete AI system that can take sequences of actions autonomously from start to finish. This type of AI application still requires human oversight and human sign-off for major decisions.

Operations and maintenance teams can benefit from AI asset management throughout their workflow. Some common real-world use cases include task automation, budget allocation and asset investment planning:

• Asset lifecycle management: Asset managers can use AI to analyze the total cost of ownership (TCO) of an asset from pricing to emergency repairs and disposal. AI-driven solutions help to determine the optimal point to repair, refurbish or replace the asset.

• Energy and sustainability optimization: AI asset management solutions address challenges with power generation and sustainability and identify energy waste across facilities and assets. In addition, unified platforms monitor power generation assets regardless of the data sources.

• Equipment health monitoring: AI-driven asset management solutions provide continuous real-time monitoring of hardware, machinery and infrastructure. When large language models (LLMs) are fed clean datasets, AI tools can analyze the data to detect early warning signs before failure.

• Facilities management: AI asset management applies to building systems like a heating, ventilation and air conditioning (HVAC) system, lighting, elevators and energy consumption. A facility team uses AI to optimize performance and reduce costs by analyzing asset options and making informed decisions.

• Predictive maintenance scheduling: AI-powered work order generation helps ensure that maintenance schedules are based on actual asset conditions. Predictive models reduce unnecessary disruptions, servicing and emergency repairs.

• Safety and compliance: AI chatbots and virtual assistants monitor safety conditions in real-time and provide quick guidance. Vendors build AI tools to adhere to local, state and federal regulations.

• Software assets: Software and IT asset management uses AI for intelligent licensing, regulatory compliance and continuous monitoring. AI-powered systems use AI to track usage patterns and identify underutilized applications.

• Travel and transportation: AI-powered asset solutions can keep fleets running reliably and in control. Connected asset data and maintenance workflows help to reduce downtime and extend asset life.

AI asset management is applicable across teams and industries. The use will depend on the organization’s goals and purpose:

• Energy and utilities: Engineers monitoring power generation and distribution infrastructure.

• Facilities and real estate: Facility managers overseeing commercial buildings and campuses.

• Government and public sector: Agencies managing public infrastructure like water treatment facilities or transportation networks.    

• Healthcare: Biomedical and facilities teams managing medical equipment and hospital infrastructure.

• Manufacturing: Plant and operations managers who maintain production equipment.

• Transportation: Fleet managers and rail or aviation maintenance teams.

AI-driven asset management can benefit many stakeholders, both within and outside an organization. New technologies are bringing new opportunities for growth and operational outcomes:

• Enhances capital planning: Assessing asset lifecycle data helps leaders make more accurate decisions about repairs versus replacements.

• Extends asset lifespan: Closely monitoring and timely intervention can stretch the useful life of expensive equipment.

• Improves workforce efficiency: Automating routine maintenance gives human technicians time back to do more high-skill work.

• Lowers maintenance costs: Servicing assets based on actual need rather than a fixed schedule reduces maintenance costs.

• Reduces unplanned downtime: Preemptively catching failures before they happen keeps production lines and facilities running.

New technology offers significant excitement, but integrating an AI system into a traditional asset management process can be challenging:

• Data quality and labeling: AI models are only as good as the data they are trained on. Asset teams must ensure that maintenance and sensor data are clean and complete, otherwise they risk inaccurate results.

• Integration with existing CMMS and EAM systems: Existing computerized maintenance management systems (CMMS) and enterprise asset management (EAM) platforms will need to be connected to the AI tools. This process is not always plug-and-play, requiring more time and resources for successful integration.

• Legacy equipment and data gaps: Older physical assets might lack the sensors or connectivity needed to feed AI systems. The asset will need to be retrofitted, which is a costly and time-consuming process. Teams will need to consider the time and resources before committing to an AI-driven solution.

Organizations looking to adopt AI-powered asset management can take a step-by-step approach. The implementation process will vary slightly depending on the organization, but you can follow these general steps to fully harness the benefits:

1. Audit asset data: Assess the quality of existing maintenance data, sensor data and equipment documentation. Review it for quality and completeness and identify any gaps in accuracy and visibility to target the AI solutions.
2. Start with the highest-cost failure points: Prioritize the most expensive and disruptive areas. Trying to monitor everything at once can be overwhelming. Identify the assets with the most room for improvement and start there.
3. Connect assets: Determine what sensor infrastructure is in place and where it needs to be added to older equipment. Explore Internet of Things (IoT) retrofit options and learn what can be done for legacy equipment.
4. Choose the right platform: Look for an AI-powered asset management platform from reputable providers that aligns with the organization’s needs and goals and current systems. Find a system that integrates with existing CMMS and EAM systems and supports specific asset types for a specific environment.
5. Build in human oversight: Define where AI has its role and where human oversight is necessary. AI agents shouldn’t be making high-consequence actions or decisions but can help direct and steer humans in a particular direction.
6. Measure and iterate: Set baseline metrics before deploying the AI solution. If there are no figures for comparison, such as maintenance cost per asset and unplanned downtime hours, organizations won’t know how well their tools are working.

Assets might not change, but how an organization manages them certainly does, thanks to new technology options entering the asset management space:

• Agentic AI: The autonomous agent is gaining popularity among organizations due to its all-encompassing ability to monitor, triage, schedule and report across entire asset portfolios with minimal human intervention necessary. According to the IBM Institute for Business Value, 55% of organizations are actively developing or deploying an agentic AI operating model.

• Digital twins: AI-powered virtual replicas of physical assets might be the next significant shift in asset management. The digital twins simulate performance, test maintenance scenarios and model lifecycle outcomes before real-world action occurs.

• Edge AI: This new technology brings AI inference directly to the asset without the need for cloud dependency. Assets like smartphones and sensors infused with edge AI can avoid internet transit times and local hardware processes the data in milliseconds.

• Prescriptive maintenance: The next step beyond predictive maintenance is the ability to prescribe actions. AI-driven solutions are now being used to prescribe exact actions, timelines and technician skill set requirements with certainty.

• Sustainability integration: Sustainability has always been part of the conversation, but now it seems AI is a core tool for meeting net-zero and environmental, social and governance (ESG) commitments. AI-powered tools can optimize energy use and reduce material waste across asset portfolios.