Making sense of the machine: a sensemaking analysis of AI adoption among independent consultants in southern Alberta

Abstract

Canadian SMEs face a deceptive paradox: despite substantial national investment in artificial intelligence and widespread reports of value among adopters, generative AI (Gen AI) uptake remains abnormally low. This thesis explores that paradox through the lens of sensemaking theory, arguing that adoption barriers are primarily interpretive rather than technical. Drawing on Weick's sensemaking and Enactment-Selection-Retention (ESR) model, the thesis analyzes how three independent consultants in Alberta construct the meaning, relevance, and strategic implications of Gen AI under conditions of institutional, professional, and market-category equivocality. Using a qualitative, multiple-case study design, three teaching cases capture how leaders' interpretations emerge through identity commitments, analogical reasoning, social cues, and early enactments. Findings suggest that these leaders often frame Gen AI as illegitimate, professionally misaligned, or too ambiguous to operationalize, leading to defensive sensemaking and strategic inertia with implementing Gen AI advantages. These interpretive patterns narrow the perceived opportunity space, lock in "not relevant" narratives, and impede the development of AI-augmented knowledge capabilities. At scale, such micro-level interpretations contribute to systemic vulnerability across Canada's SME sector. The study extends sensemaking theory to technology-triggered ambiguity, identifies varieties of equivocality, and offers practical and policy recommendations focused on reshaping interpretive infrastructure rather than solely improving technical readiness.