Moving beyond ‘proof points’: Factors underpinning AI-enabled business model transformation

Abstract

Business model renewal is a key consideration for organizations, and AI (artificial intelligence) has been identified as a significant potential enabler for that renewal. However, while there are examples of emerging organizations using AI as a key basis of competitive advantage as well as examples of established organizations trialing AI technologies, there are relatively few examples of established organizations fundamentally transforming their business models through the use of AI. Through case studies underpinned by interviews with named executives of ten organizations and complemented by an applicability check involving 14 executives, advisors and practice-oriented academics, this paper presents an empirically supported set of factors linked to successful AI-enabled business model transformation as well as a model of interactions between these factors. Using a horizontal contrasting approach to articulate the difference between empirical findings and a literature based model, this paper moves from the language of potentially passive top management support towards the concept of proactive leadership and introduces tech-sensitive innovation culture, AI-sensitive risk tolerance and strategic process discipline into the dynamic capability lexicon. The insights of this paper can be used by managers to assess their readiness to move beyond traditional ‘proof-points’ and successfully undertake and accelerate AI-enabled business model transformation.