Human–AI collaboration capability: A new driver of organizational performance
TL;DR
The framework reframes the AI productivity problem as a capability challenge rather than a computational one, clarifying the conditions under which human–AI collaboration, rather than computation itself, becomes a scarce and advantage-generating resource.
Abstract
As access to advanced artificial intelligence becomes near-universal among organizations, performance differences among adopters continue to widen, suggesting that the AI asset itself is not the source of competitive advantage. This paper locates the residual explanation in an organizational capability rather than in the technology stock. Human–AI Collaboration Capability (HAICC) is defined as an organization's ability to set up, orchestrate, and continuously recalibrate human–AI work systems such that joint output exceeds what either party could achieve alone with the same resources. Grounded in the resource-based view, dynamic capabilities theory, the complementarity tradition in the economics of organization, and human–automation research, HAICC is conceptualized as a second-order formative construct comprising six dimensions: task decomposition acuity, interpretive competence, calibrated reliance, workflow reconfigurability, joint learning loops, and accountability architecture. Two mechanisms give the framework analytical leverage: the coupling tax, the transaction cost of joint human–AI work, and reliance elasticity, the responsiveness of human reliance to changes in system reliability. The paper links HAICC to operational, market, and financial performance through decision quality, operational agility, and knowledge recombination, and specifies environmental dynamism, task interdependence, data infrastructure maturity, regulatory intensity, and workforce learning orientation as contingencies. A staged validation protocol and candidate item pool are proposed to support future empirical testing. The paper is conceptual: it reports no primary data, and the propositions and candidate items are advanced for subsequent empirical validation rather than tested here. The framework reframes the AI productivity problem as a capability challenge rather than a computational one, clarifying the conditions under which human–AI collaboration, rather than computation itself, becomes a scarce and advantage-generating resource.