Generative AI for Asset Lifecycle Management

Generative AI is rapidly enhancing Asset Lifecycle Management by building on previous AI advancements and creating new opportunities for operational efficiency. By moving beyond mere analysis and prediction to the realm of creation, generative AI introduces innovative applications in Asset Lifecycle Management (ALM), such as expedited Failure Mode Effects Analysis (FMEAs) and interactive maintenance assistants. To harness the benefits of generative AI, businesses must undergo a second wave of adoption, focusing on identifying inefficiencies in processes, addressing data gaps, and engaging the appropriate stakeholders throughout the organization.

In recent years, artificial intelligence (AI) has facilitated remarkable advancements in predictive maintenance, resulting in smarter, real-time asset monitoring and improved uptime and efficiency. With the arrival of generative AI, Asset Lifecycle Management (ALM) is advancing even further, providing additional avenues to reduce maintenance expenses, optimize physical assets, and support sustainability and energy cost objectives—even for systems that were previously deemed optimized. Businesses are already well into the heady days of AI. Many collect data on almost every aspect of their assets: Operational data from their facilities; resource data on electricity and other utilities; asset data for tracking age, condition, maintenance history; and replacement parts for machines in factories. Many have brought together various databases and metrics under one dashboard, cleaned data of outliers or errors, labelled data and even started tuning or training AI models for their specific needs.

Collecting this data and leveraging AI-driven insights is important, and has already had a real impact, giving some enterprises an advantage that helps differentiate them as leaders in front of the pack. For example, computer vision—where machine learning helps identify and understand people and objects—helped Ford Motor Company reduce defects on the assembly line, from 40 per month to zero. The zero-defect system even helped drive design improvements. In another example, computer vision is analyzing photos from drones and other sources to help optimize maintenance for the Great Belt bridge in Denmark, which is projected to extend the bridge’s life by 100 years

In some ways, generative AI follows the trend of ongoing technological improvement, but it also marks a considerable leap forward. Since generative AI is inherently capable of creation—alongside analysis, extraction, prediction, and more—it empowers businesses to advance and explore new possibilities specific to Application Lifecycle Management (ALM). One key advantage of generative AI is its potential to close the skills gap that numerous employer face in today’s competitive landscape. For instance, instead of demanding that a field technician be equipped to handle a vast array of potential issues at any given moment, conversational chat models are aiding in rapidly providing the correct information when needed. When confronted with a problem, a field technician can use natural language to gather insights about a particular type of asset or a recent failure. They can input data incrementally and rely on the AI to conduct the extensive analysis, which may involve prompting the technician for additional information to assist in swiftly identifying the root cause of a problem.

Generative AI plays a vital role in Asset Lifecycle Management (ALM) for both employees and organizations. Many insights derived from generative AI utilize time series forecasting models, which examine historical data on asset changes over time, spot anomalies, and predict future outcomes for specific assets. These AI-generated forecasts can assist businesses in steering clear of costly and unnecessary shutdowns.

Similarly, generative AI can expedite the process of conducting failure mode and effects analysis (FMEA). When asset failures occur—as they inevitably will, particularly in sectors like manufacturing—it's essential for companies to be well-prepared with the required parts and expertise readily available. Failing to do so can result in significant downtime and financial losses. Consequently, it is crucial to swiftly identify the root cause of asset failures and implement the necessary modifications to prevent recurrence. Conducting a detailed FMEA to identify all potential failure causes can be a lengthy process.

Generative AI can enhance efficiency in several respects. For instance, virtual assistants can quickly identify all potential causes of failure, outpacing any individual or team of employees, and subsequently assist in pinpointing the most probable source of the issue, leading to a faster resolution. This application is already in use with software solutions that equip businesses with extensive libraries of asset-specific failure information and mitigation strategies, allowing them to proactively plan for, prevent, or quickly address failures.

Additionally, research indicates that training AI models using a comprehensive collection of asset reliability strategies can yield a significant amount of essential content for developing Failure Mode and Effects Analyses (FMEAs). In the future, generative AI will likely be able to compile this information and offer organizations tailored recommendations on the timing and methods for maintaining their assets to prevent these failures.

Furthermore, generative AI can assist in overcoming data-related challenges. For many organizations, a major hurdle in advancing AI initiatives is their concern over data quality; they wish to avoid scenarios where subpar data leads to suboptimal results. Generative AI can help in several ways. For example, layering generative AI on top of other diagnostic AI tools can help improve the accuracy of the “failure codes” assigned when ALM problems occur. Those codes often are missing or incorrect, but now tools can automatically generate failure code recommendations by training models on long and short descriptions from relevant work orders.

As technology advances at an unprecedented pace, businesses can’t afford to become complacent with their initial achievements in AI. Generative AI is improving employee abilities, changing the talent landscape, speeding up complex diagnoses, and enabling the extraction of greater insights from limited data. There are still chances for organizations to stay “ahead of the curve.” Whether companies aim to enhance decision-making, asset performance, workforce productivity, or all of the above, there are ways to utilize generative AI. To get started, first pinpoint the weaknesses in the existing processes. Next, determine the necessary data, seek out the appropriate colleagues and experts, and begin gathering what is available.. Consider how generative AI could help bridge the remaining gaps. We have already witnessed this during the initial phase of AI adoption; now is the time to advance with the next phase.

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