Making Sense of AI Limitations: How Individual Perceptions Shape Organizational Readiness for AI Adoption

TL;DR

This paper explores how individual perceptions of AI limitations influence organizational readiness for AI adoption, emphasizing the importance of social learning and continuous strategic adaptation.

Contribution

It introduces a dynamic model showing how individual sensemaking and social interactions shape organizational AI adoption readiness.

Findings

Hands-on AI experience fosters realistic expectations and trust.

Peer networks and champion systems support effective AI integration.

Organizational readiness is an ongoing learning process.

Abstract

This study investigates how individuals' perceptions of artificial intelligence (AI) limitations influence organizational readiness for AI adoption. Through semi-structured interviews with seven AI implementation experts, analyzed using the Gioia methodology, the research reveals that organizational readiness emerges through dynamic interactions between individual sensemaking, social learning, and formal integration processes. The findings demonstrate that hands-on experience with AI limitations leads to more realistic expectations and increased trust, mainly when supported by peer networks and champion systems. Organizations that successfully translate these individual and collective insights into formal governance structures achieve more sustainable AI adoption. The study advances theory by showing how organizational readiness for AI adoption evolves through continuous cycles of individual understanding, social learning, and organizational adaptation. These insights suggest that organizations should approach AI adoption not as a one-time implementation but as an ongoing strategic learning process that balances innovation with practical constraints. The research contributes to organizational readiness theory and practice by illuminating how micro-level perceptions and experiences shape macro-level adoption outcomes.