We investigate whether the adoption of conversational artificial intelligence (CAI) translates corporate complexity into clearer financial narratives. CAI, representing AI-driven innovation, can bolster operational efficiency and performance, thereby leading to better financial transparency and readability of financial statement information.
We construct a firm-level CAI measure from CAI-related disclosures in 10-K filings using Sent-latent Dirichlet allocation (LDA)-variational expectation maximization (VEM) topic modelling. Our measure captures CAI applications related to service and operations, and decision-making and governance. We also use patent-based measures based on natural language processing and speech-recognition technologies as alternative proxies in robustness tests.
We find that CAI adoption is associated with lower Bog Index scores, indicating more readable 10-K reports. Mediation tests show that financial performance and operational efficiency partially mediate this relationship. The results remain robust after addressing endogeneity concerns and using alternative specifications.
Using unsupervised machine learning topic modelling, we observe that CAI is predominantly employed in corporate operations, notably in service-related activities, decision-making processes and corporate governance.
