TIM-7074 v1

Haakman, M., Cruz, L., Huijgens, H., & van der Werf, J. M. E. M. (2021). AI lifecycle models need to be revised. Empirical Software Engineering, 26 (1), 95. https://doi.org/10.1007/s10664-021-09993-1

• This exploratory case study at ING Bank reveals that traditional AI lifecycle models often overlook critical stages such as data collection, feasibility studies, documentation, model monitoring, and risk assessment. The research emphasizes the necessity for updated lifecycle frameworks that align with real-world AI development practices, underscoring the importance of comprehensive lifecycle understanding in AI research.

Maskey, M. (2023). Rethinking AI for science: An evolution from data-driven to data-centric framework. Perspectives of Earth and Space Scientists, 4, e2023CN000222. https://doi.org/10.1029/2023CN000222

• The paper advocates for a shift from traditional data-driven approaches to a data-centric AI framework in scientific research. It argues that focusing on data quality and management throughout the AI lifecycle is crucial for advancing scientific discovery, emphasizing the research significance of data-centric methodologies.

De Silva, D., & Alahakoon, D. (2022). An artificial intelligence life cycle: From conception to production. Patterns, 3 (6), 100489. https://doi.org/10.1016/j.patter.2022.100489

• This article outlines a comprehensive artificial intelligence (AI) life cycle framework, detailing the stages from initial conception and design to production deployment and ongoing maintenance.

Beta, K., Nagaraj, S. S., & Weerasinghe, T. D. B. (2025). The role of artificial intelligence on supply chain resilience. Journal of Enterprise Information Management, 38 (3), 950–973. https://doi.org/10.1108/JEIM-12-2023-0674

• This study examines how AI tools enhance supply chain resilience by improving inventory management, reducing logistics costs, and increasing service levels. It highlights the significance of integrating AI throughout the supply chain lifecycle, demonstrating the practical impact and importance of AI in operational contexts.