AI washing in corporate finance: conceptual foundations, measurement, economic consequences and future directions

Artificial Intelligence (AI) has become the most attractive label in corporate strategy narratives, yet the intensity with which firms talk about AI has begun to diverge systematically from what they actually do. This phenomenon is known as AI washing. Drawing on four core empirical studies, this paper reviews the corporate finance literature on AI washing. This study develops an operational definition—a positive gap between AI Talk (disclosure) and AI Walk (substantive investment)—and delimit it against greenwashing, digital washing and impression management. This study explains why the gap pays through signalling, information asymmetry and image management, and identify cognitive dissonance as a countervailing corrective force. This study catalogues how existing work measures the construct and assess the validity of each proxy. Three outcome chains are synthesized: AI washing increases access to bank loans but not necessarily the quality of the money obtained; it has an inverted U-shaped effect on the technological gap; and it inflates investor optimism, widens analyst disagreement and raises crash risk. This study examines who can detect AI washing and conclude with seven directions for future research, arguing that the field's next step is a systematic answer to why firms wash, who believes them, who detects them, and what the practice buys against what it costs.