Every asset manager, no matter the size of their organization, is tasked with the same goals: streamline maintenance schedules, increase the reliability of assets or equipment, and fine-tune workflows to boost quality and productivity. Embracing new technologies is key to achieving these improvements.
A 2023 CFO survey by Grant Thorton marks a turning point for asset management firms in terms of artificial intelligence (AI). An impressive 30% of CFOs reported they’re already utilizing generative AI, with another 55% exploring its potential uses.
With the right governance, AI opens up vast possibilities for the asset management sector, leading to more streamlined operations and improved results.
Here are the top 5 applications.
Creating Work Instructions
Your front-line team, including field service technicians, maintenance planners, and supervisors, need detailed job plans and instructions for asset repairs. By training a hybrid AI or machine learning (ML) model on both enterprise and public data, including information about new assets and locations, you can provide them with visual analytics and immediate content delivery.
This access to knowledge can enhance field service availability and increase the rate of fixing issues on the first try, saving costs, boosting worker productivity, and enhancing customer satisfaction.
Enhancing Work Order Efficiency
Work orders, which depend on work and job plans for authorization and resources, are crucial but can be time-consuming, often causing delays. Generative AI can improve this by training foundational models with all necessary instructions, parts, tools, and skills for specific assets or classes, thus streamlining the planning process.
Optimizing Asset Allocation
AI-driven asset management systems offer insights into the condition of assets and equipment, enabling optimal asset and resource allocation. This ensures that the right personnel, tools, and spare parts are readily available.
Improving Maintenance Quality
Reviewing completed work orders can highlight areas for compliance or process improvement. Generative AI can suggest updates to enhance the effectiveness of maintenance plans and create new job plans as needed.
Simplifying Field Crew Workflows
Field maintenance doesn’t have the luxury of waiting for approvals to understand asset breakdowns. Here, AI models act as digital assistant, providing immediate, relevant information. AI can contextualize this information, further aiding in-field decision-making.
Exploring AI for Asset Maintenance
AI stands on the brink of redefining the asset management landscape, heralding an era of data-driven, personalized, and resilient investment strategies. Its capacity to analyze extensive amounts of structured and unstructured data, simulate market conditions, and forecast trends in real time not only enhances decision-making but also equips for risk management, ethical investing, and bespoke portfolio management.
Nevertheless, as AI further evolves and integrates into the asset management sector, it is imperative to confront challenges related to data quality, model interpretability, and ethical considerations. Asset managers must tread carefully, balancing the exploitation of AI’s potential with the assurance of transparency, accountability, and regulatory adherence.
