The AI augmentation gap: data richness, automation and business value

Purpose Grounded in the resource-based view (RBV), this study examines how data richness, as an organisational resource, is associated with business value through two deployment processes, automation and artificial intelligence (AI)-enabled augmentation, and whether they differ in their capacity to generate value. Design/methodology/approach Survey data from 366 Greek firms across four sectors were analysed using partial least squares structural equation modelling (PLS-SEM) with bootstrapped mediation analysis and coefficient-difference tests; business value is measured as perceived value from digital technology. Findings Data richness is positively associated with both deployment processes. Automation is positively associated with business value and channels part of the association between data richness and value, whereas AI-enabled augmentation, the less developed process, shows no significant direct association; the difference is statistically significant, revealing an AI augmentation gap. Data richness also retains a significant direct association with value, and additional analyses suggest that the capability associations with value run through enacted digital transformation. Practical implications Firms should build a strategically governed data foundation, expand automation for near-term returns and develop augmentation as a longer-term capability built on skills, role redesign, validation and trust in AI-supported decisions; EuroMed policy instruments can be aligned with this sequencing. Originality/value Drawing on the RBV, the study shows that deployment processes of the same digital resource differ significantly in their association with value, a heterogeneity interpreted as reflecting the maturity of the complementary capabilities each requires, and provides evidence from a resource-constrained Mediterranean economy.