Purpose The purpose of the article, “Sensemaking in the AI Workplace: A Guide for Organizational Learning” is to explore the employment of sensemaking ensuring that organizational learning is deliberately interspersed appropriately and responsibly in a workplace shaped by Artificial Intelligence (AI). It seeks to furnish workplace design specialists and practitioners with insights to proactively transform the ever-evolving AI work environment Design/methodology/approach The article examines the impact of AI’s data saturation and algorithmic opacity on the workplace conceptually. It advocates a shift in focus from traditional sensemaking (interpreting ambiguous environments) to AI sensemaking (validating and contextualizing accurate but opaque algorithmic outputs). Findings The core findings of the article are: (1) AI Sensemaking is important because it changes the focus from understanding uncertainty to validating and putting opaque algorithmic outputs in context. Practical implications The article provides several practical implications for organizations functioning in the AI workplace: (1) Organizations must establish formal sensemaking protocols, such as after-action reviews, to actively extract and codify essential knowledge, especially the boundary-spanning knowledge of key employees. Originality/value The article explicitly addresses the knowledge gap between the subjectivity of human meaning-making in organizational theory and the objectivity of AI systems. The article is unique in its framing that while the integration of AI and organizational learning is being discussed, more importantly, the value of their juxtaposition lies in a direct focus on sensemaking as the essential bridge for converting algorithmic opacity into responsible, appropriate organizational learning. The article presents a practical guide by offering practitioners a guide for proactively reshaping the AI work environment.
Everod Davis (2026) studied this question.