People and Management Debt in ML-Integrated Software Projects: Structuring Industry Insights

Abstract

The accelerated development of Machine Learning (ML) tools, combined with broader access to frameworks and infrastructures, has driven the rapid adoption of ML-based solutions in industry. However, their integration into software systems introduces unique challenges, particularly for managing technical debt (TD). Traditional TD research focuses primarily on technical issues, but in ML systems, people and management factors, referred to as nontechnical debt (NTD), play a critical role in TD accumulation and persistence. In this study, we investigate the underexplored dimension of NTD in ML-integrated software systems, focusing on people- and management-related factors. Using Design Science Research (DSR) methodology, we developed an artifact in an iterative incremental manner that categorizes NTD issues in ML systems. As part of this process, we conducted semi-structured interviews with 18 professionals from 15 companies, examining 22 ML projects. Through thematic analysis, we identified 15 NTD categories, 10 of which relate to people debt, and the remaining 5 to management debt. Each category is associated with underlying causes, short-term fixes, and potential solutions. Our findings show that NTD in ML projects frequently arise from inadequate decision-making practices, particularly those related to technology adoption, knowledge management, and human resource planning. Additional sources of NTD include challenges in team dynamics, such as insufficient collaboration, poor skill integration, and ineffective team structuring, as well as communication barriers rooted in organizational culture and team interactions. These factors collectively and substantially impact project outcomes. While band-aid solutions may provide short-term relief, they frequently contribute to accumulation over time. To support practitioners and researchers, our study complements the proposed artifact with actionable recommendations informed by expert perspectives and literature.