Paper Type

Complete

Paper Number

PACIS2026-1114

Description

Despite substantial investments in Artificial Intelligence (AI), evidence on its business value remains mixed. We argue that this heterogeneity stems not from whether firms adopt AI, but from how they deploy it. Drawing on Resource-Based Theory and organizational search theory, we conceptualize AI capability as two-dimensional: AI deployment depth — technological variety of AI implementations — and AI deployment breadth — organizational scope of AI diffusion. Three resources shape these dimensions differentially: AI-skilled human capital drives depth, digital infrastructure drives breadth, and data-driven culture supports both. Using archival microdata from 770 large Spanish firms, staged OLS models show that firm performance is positively associated with the interaction between depth and breadth, consistent with a complementarity logic. We contribute by reconceptualizing AI capability as a deployment configuration and offering a configurational explanation for the AI productivity paradox.

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Jul 5th, 12:00 AM

Beyond AI Adoption: An Empirical Study on the Antecedents and Performance Outcomes of AI Deployment in Organizations

Despite substantial investments in Artificial Intelligence (AI), evidence on its business value remains mixed. We argue that this heterogeneity stems not from whether firms adopt AI, but from how they deploy it. Drawing on Resource-Based Theory and organizational search theory, we conceptualize AI capability as two-dimensional: AI deployment depth — technological variety of AI implementations — and AI deployment breadth — organizational scope of AI diffusion. Three resources shape these dimensions differentially: AI-skilled human capital drives depth, digital infrastructure drives breadth, and data-driven culture supports both. Using archival microdata from 770 large Spanish firms, staged OLS models show that firm performance is positively associated with the interaction between depth and breadth, consistent with a complementarity logic. We contribute by reconceptualizing AI capability as a deployment configuration and offering a configurational explanation for the AI productivity paradox.