The Heterogeneous Effects of Artificial Intelligence on Enterprise Total Factor Productivity: Key Mechanisms and Strategic Implications

While Artificial Intelligence (AI) is recognized as a catalyst for productivity, prior research often assumes homogeneous effects or views heterogeneity in one dimension, overlooking interdependencies among firm traits. Using panel data of 3366 Chinese A-share listed firms from 2015 to 2023, this study examines how AI adoption affects Total Factor Productivity (TFP) and explores heterogeneous effects through a multidimensional clustering based on firm size, age, market competitiveness, and digital infrastructure. Our findings challenge the notion of a universal technological dividend and show that AI adoption yields asymmetric productivity gains depending on firms resource constraints and competitive environments. Firms constrained by limited intangibles, outdated hardware, or weak human capital benefit most when AI mitigates bottlenecks, while technologically advanced firms in hypercompetitive markets gain little, reflecting diminishing returns from capability saturation. Mechanism tests reveal two key pathways: efficiency gains via automation in constrained firms and innovation stagnation in mature firms with redundant AI. These findings underscore AI's contingent productivity effects and the need for digital strategies aligned with firm resources and market context.