Generative AI in maintenance: Three use cases 

The challenges in industrial maintenance are many and varied. Companies have to meet high demands in terms of plant availability, while at the same time reducing costs and complying with regulatory requirements. In many areas, maintenance processes are still heavily characterized by manual activities, which leads to inefficiencies and quality problems.

Generative AI offers innovative solutions that not only automate processes but also improve decision-making. Three practical examples show how companies are already benefiting from this technology. 

Use case 1: Automated creation of inspection plans

Many companies regularly have to evaluate extensive inspection reports – often in unstructured formats such as PDFs or handwritten notes. Analyzing these documents manually is not only time-consuming, but also prone to errors. 

This is where Generative AI comes in: By using Natural Language Processing (NLP), AI can analyze unstructured inspection logs, extract relevant content and automatically create structured inspection plans. The system recognizes patterns, standardizes information and generates suggestions for maintenance measures. Technical experts only need to check these and adapt them if necessary. 

The advantages are obvious: The workload for creating inspection plans is drastically reduced – from several hundred working days per year to just a few days. At the same time, the consistency and quality of the data improves considerably, as sources of human error are minimized. In addition, the standardized documentation enables better traceability and optimized further processing of the data in downstream systems. 

Use case 2: AI-supported voice assistant for maintenance reports

Recording fault reports is a major challenge in many companies. Technicians are often on the road, have no direct access to digital systems or work in environments with poor network coverage. In many cases, documentation is therefore delayed or incomplete, leading to a loss of information and inefficient processes. 

An AI-supported voice assistant can solve these problems. Employees can use a landline number that is available around the clock to report faults by voice. The AI analyzes the spoken message in real time, extracts relevant information such as damage description, affected components and urgency and transfers it to the maintenance system in a structured manner. At the same time, the conversation is transcribed and stored as documentation.

This solution offers several advantages: Firstly, it reduces media disruptions and manual input processes, which saves time and improves data quality. Secondly, it enables a faster response time to faults, as information is transferred to the system immediately and in a standardized form. Especially in critical maintenance environments, where every minute counts, this can lead to a significant increase in efficiency. 

Use case 3: Automation of master data processes

In the field of Asset Lifecycle Management, the precise and efficient management of master data is a major challenge. An innovative AI tool for automating master data processes makes a decisive contribution to improving data quality and productivity. 

Master data is the backbone of every maintenance process and must always be correct, complete and up-to-date to support fact-based and automated decision-making. Automating these processes using artificial intelligence offers significant benefits. The tool addresses the typical challenges of master data management, including the handling of large volumes of data, linguistic diversity, complex transformations, duplicates, insufficient standardization and previously manual processes. 

The use of AI in this use case leads to a significant reduction in the time and effort required for data migration and transformation. The tool extracts and migrates master data from structured and unstructured sources and images, regardless of heterogeneous column names and different formats, and transforms it into uniform formats. It automatically fills in data schemas, detects duplicates beyond rule-based approaches and normalizes multilingual data, ensuring accurate and efficient data migration. 

The integration of such AI technologies into the maintenance processes of SAP systems enables companies to significantly improve their master data management. For example, implementing the tool can reduce master data migration time by up to 57%, ultimately leading to more efficient and accurate business decisions. 

This tool underlines the role of artificial intelligence as a catalyst for digitalization in maintenance and provides a solid foundation for optimizing asset lifecycle management processes. Companies benefit from better data quality and can use this as a basis for making informed decisions that significantly boost their business success. 

Conclusion and outlook

These three use cases illustrate how generative AI is revolutionizing maintenance. By automating documentation and reporting processes, companies can not only save time and money, but also ensure higher data quality and more efficient maintenance. Furthermore it opens up new opportunities for predictive analysis and data-driven optimization. 

Companies that rely on AI-supported processes at an early stage create the basis for an intelligent and future-proof maintenance strategy. In the coming years, generative AI will continue to develop and open up new application possibilities – from predictive maintenance to fully autonomous maintenance.
Those who invest today will reap the rewards tomorrow.