This study explores the design of a compatibility evaluation framework for integrating 3PL warehouse clients into semi-automated warehouse setups. Using Action Design Research (ADR), an artifact was developed that combines data-driven decision-making (DDD) and multi-criteria decision analysis. The framework, implemented in Microsoft Power BI, enables the evaluation of client compatibility based on configurable criteria and relevant metrics. It was co-created with stakeholders and tested using data from 33 warehouse clients, demonstrating its practical value in identifying operational fit while facilitating data-driven discussions. The study highlights the potential of structured decision frameworks in environments with limited data, offering generalizable insights for 3PL warehouses and similar contexts.

This paper explores citizens’ stances toward the use of artificial intelligence (AI) in public services in Norway. Utilizing a social contract perspective, the study analyzes the government–citizen relationship at macro, meso, and micro levels. A prototype of an AI-enabled public welfare service was designed and presented to 20 participants who were interviewed to investigate their stances on the described AI use. We found a generally positive attitude and identified three factors contributing to this: (a) the high level of trust in government (macro level); (b) the balanced value proposition between individual and collective needs (meso level); and (c) the reassurance provided by having humans in the loop and providing transparency into processes, data, and model’s logic (microlevel). The findings provide valuable insights into citizens’ stances for socially responsible AI in public services. These insights can inform policy and guide the design and implementation of AI systems in the public sector by foregrounding the government–citizen relationship.