<p>Non-Technical Debt (NTD), arising mainly from organizational, cultural, and people-related inefficiencies, significantly compromises the quality and sustainability of AI-enabled software systems. While a growing ecosystem of AI-related international standards addresses various technical and ethical requirements, organizations often struggle to connect these high-level frameworks with the specific instances of NTD that emerge during development. This study addresses this gap by associating real-world NTD instances with lifecycle processes, providing a structured basis for process-level diagnosis. Using a Design Science Research approach, we developed a conceptual artifact by analyzing 107 validated NTD instances from industrial projects spanning six categories, including People Debt, Team Debt, Requirement Debt, Resource Management Debt, Project Management Debt, and Privacy and Compliance Debt, across multiple ML domains such as Computer Vision, NLP, and Time Series Analysis. The resulting artifact shows that 72.5% processes align with ISO/IEC 5338:2023, while People Capability Maturity Model serves as a complementary reference for aspects extending beyond the standard’s primary scope, corresponding to Training and Development, Communication and Coordination and Participatory Culture process areas. The artifact was subsequently validated using a hybrid approach combining LLM-assisted analysis and expert review. Quantitative analyses, including frequency distribution, co-occurrence analysis, association rule mining, and Jaccard similarity networks, indicate that NTD in AI projects is frequently associated with deficiencies in Project Planning and Human Resource Management processes. This work offers project stakeholders a standards-aligned perspective on where NTD tends to appear in AI-enabled systems, highlighting processes that may systematically contribute to the accumulation of NTD.</p>