AI-Assisted Narrative Reporting in U.S. Filings Audit Quality, Disclosure Readability, and the Emergence of Algorithmic Boilerplate

The fast adoption of generative artificial intelligence (AI) in corporate reporting systems has led to quicker development of narrative disclosures which appear in U.S. SEC filings through Management’s Discussion and Analysis (MD&A) and risk factor disclosures. The use of AI for drafting helps create error-free documents which read well but it could lead to the development of standardized legal language which becomes less effective for decision-making purposes. The research develops a framework which studies AI implementation in narrative reporting to determine its effects on three essential variables which include audit quality results and disclosure clarity and corporate narrative content uniqueness and semantic alignment. The research uses difference-in-differences and event-study methods to analyze AI reporting adoption through firm-specific signals which are extracted from 10-K section text and audit-quality indicators (restatements and internal control material weaknesses and audit fees and audit report lag). The research indicates that AI implementation leads to better understanding of plain-English content but simultaneously boosts the similarity between peers and the duration of information retention within organizations which results in a separation between text clarity and information value. The research provides findings which affect how auditors evaluate risks and how audit committees monitor activities and what regulatory bodies should do to prevent companies from making vague risk statements.