The Heterogeneous Diffusion of AI: Individuals, Organisations, and Adoption Barriers

Artificial Intelligence (AI) is adopted far faster by individuals than by organisations, yet most diffusion models treat both populations as homogeneous and independent. We develop an extended Bass model with heterogeneous archetypes, bidirectional cross-group spillovers, endogenous barrier decay, and productivity feedback. Base parameters are estimated from survey data on individual and organisational AI adoption across 45 countries (2020-2025); the extended model is calibrated and simulated over ten years. Two findings emerge: cross-group spillover from individual to organisational adoption is the dominant diffusion accelerator in the cross-country evidence, while within-group imitation is undetectable in the current two-year panel; and heterogeneity generates a 5.2-year spread across firm archetypes in time to 50% adoption, capturing most of the 6-year empirical firm-size gap. The calibrated model simulates overall organisational AI adoption reaching 50% by 2029 and 74% by 2033 under the benchmark calibration, translating into sustained productivity growth. Education and training delivers the largest adoption gain per unit of intervention intensity, while direct subsidies most effectively narrow the adoption gap between large and small firms.