Experiences on Using Large Language Models to Re-Engineer a Legacy System at Volvo Group
Vanshika Singh, Caglar Korlu, Wesley K. G. Assunção · 2025
Digital processes driven by data are the basis for industry operations nowadays. Despite relying on software for such processes, the industry faces significant challenges due to legacy systems. Legacy systems are pieces of software that, despite being vital for industry operations, have limitations in terms of performance and scalability needs. Thus, to ensuring that these systems can still continue to deliver business value, there is a need for re-engineering such legacy system. This is a situation faced by Volvo Group, with their system called SCORE, initially implemented in 2017, and used by HR teams and managers to manage team structures, employee data, and operational workflows. The SCORE system faces challenges related to performance issues, database inefficiencies, outdated user interface, lack of flexibility, and lack of modern software engineering practices. As a strategy to keep the business value of SCORE, Volvo has started using Large Language Models (LLMs) to speed up it re-engineering. This paper presents the experiences of using LLMs at Volvo Group. More specifically, we describe how GPT-4 and Claude AI were applied, with three learning strategies (i.e. zero shot, one shot, and few shot), to address the challenges of the legacy system. The prompts and examples of the responses given by the Foundations Models are presented and discussed. By adopting the insights provided by LLMs, we were able to reduce API response times from 20–30 seconds to 3 seconds, improve UI usability by restructuring elements for easier navigation, and enhance scalability with better database queries and code modularization. The CI/CD pipeline was also streamlined, enabling faster and more reliable deployments. As an additional contribution, we report six lessons learned, allowing other industries and researchers to comprehend the strategic value of integrating LLMs into legacy system modernization.