Abstract Artificial Intelligence (AI) is widely recognised as a transformative general-purpose technology with the potential to reshape productivity dynamics across firms, industries, and economies. Despite growing empirical research, the evidence remains fragmented due to variation in definitions, measurement approaches, and contexts, making it difficult to draw consistent conclusions about productivity outcomes. This study addresses this gap by systematically synthesising 76 peer-reviewed studies published between 2015 and 2025. The review suggests a positive directional pattern between AI-related technologies and productivity outcomes across much of the literature, although the magnitude and consistency of effects vary considerably depending on how AI and productivity are operationalised, as well as across organisational, sectoral, and institutional contexts. Many reviewed studies examine AI as part of broader digital transformation initiatives, including robotics, automation, digital infrastructure, broadband expansion, and related technological systems. Through thematic inductive analysis, the study identifies key enablers of productivity, including organisational capabilities, workforce skills, complementary innovation pathways, and digital infrastructure. It also highlights recurring barriers, such as data limitations, cultural resistance, regulatory constraints, and high adoption costs, demonstrating that gains are neither uniform nor automatic. By synthesising heterogeneous empirical evidence and linking mechanisms to outcomes, the review provides a nuanced understanding of AI’s role in shaping productivity and offers a foundation for future research and policy aimed at maximising AI’s productivity potential.
Cebulla et al. (Thu,) studied this question.