Human–AI Collaboration in the Modernization of COBOL-Based Legacy Systems: The Case of the Department of Government Efficiency (DOGE)
This paper aims to explore the challenges of maintaining and modernizing legacy systems, particularly COBOL-based platforms, the backbone of many financial and administrative systems. By exploring the DOGE team’s initiative to modernize government IT systems on a relevant case study, the author anal...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-06-01
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| Series: | Computers |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2073-431X/14/7/244 |
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| Summary: | This paper aims to explore the challenges of maintaining and modernizing legacy systems, particularly COBOL-based platforms, the backbone of many financial and administrative systems. By exploring the DOGE team’s initiative to modernize government IT systems on a relevant case study, the author analyzes the pros and cons of AI and Agile methodologies in addressing the limitations of static and highly resilient legacy architectures. A systematic literature review was conducted to assess the state of the art about legacy system modernization, AI integration, and Agile methodologies. Then, the gray literature was analyzed to provide practical insights into how government agencies can modernize their IT infrastructures while addressing the growing shortage of COBOL experts. Findings suggest that AI may support interoperability, automation, and knowledge abstraction, but also introduce new risks related to cybersecurity, workforce disruption, and knowledge retention. Furthermore, the transition from Waterfall to Agile approaches poses significant epistemological and operational challenges. The results highlight the importance of adopting a hybrid human–AI model and structured governance strategies to ensure sustainable and secure system evolution. This study offers valuable insights for organizations that are facing the challenge of balancing the desire for modernization with the need to ensure their systems remain functional and manage tacit knowledge transfer. |
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| ISSN: | 2073-431X |