Artificial intelligence and the historical performance expectation gap: Evidence from the moderating role of monetary easing

This study examines whether and how artificial intelligence engagement affects firms’ deviations from historically expected performance. Using data from non-financial A-share listed firms in China, this study constructs a firm-level indicator of AI engagement based on annual report text and empirically examines its impact on the historical performance expectation gap. The analysis further investigates the underlying transmission channels and the moderating role of the macro policy environment. The results show that higher AI engagement is associated with smaller deviations from historical performance expectations, suggesting that AI enhances firms’ ability to manage expectations and align outcomes with historical benchmarks. Mechanism analysis reveals that this effect operates indirectly through improvements in both supply chain stability and top management team consistency, highlighting the dual governance value of AI across internal and external dimensions. Moreover, the moderating effect analysis finds that the role of AI becomes more pronounced under accommodative monetary policy conditions, indicating that supportive policy environments amplify the effectiveness of AI-based governance. This study contributes to the literature by offering a micro-level governance perspective on the economic consequences of AI, and provides empirical insight into the dynamic interaction between technological integration, expectation management, and institutional conditions.