Organizational AI Capability: From Algorithmic Capital to Sustainable Competitive Advantage

Abstract

The pace of artificial intelligence (AI) across industries has created a paradox: numerous organizations purchase AI technologies and even build up an arsenal of algorithms, yet many fail to transform this purchase into sustainable benefit. The construct that is most frequently called upon to give an explanation of this gap- AI capability- is still conceptually disjointed. Earlier literature has focused on adoption, preparation, maturity, and resource collection, and a parallel line has started to consider algorithmic assets as a specific strategic resource (algorithmic capital). The transformation, i.e., how organizations transform algorithmic capital and other resources into a higher-order organizational AI capability, and why that capability should be able to create value, is what is unspecified. The present paper builds up an integrative transformation theory based on the Resource-Based View, the Knowledge-Based View, the Dynamic Capabilities perspective, and Organizational Learning theory. We characterize the organizational AI capability as a higher-order, multidimensional construct; present a three-level architecture that bridges AI resources (including algorithmic capital), mid-level capability constructs, and an emergent organizational capability; describe a formation mechanism; develop seven propositions; and state the boundary conditions. The contribution is a coherent account of how firms move from possessing algorithmic capital to possessing an AI-based organizational capability, and from that capability to competitive advantage.