Artificial intelligence disclosure practices in US 10-K filings: accounting standards, signalling incentives and reporting quality

Purpose As artificial intelligence (AI) becomes increasingly integrated into accounting and corporate operations, the need for frameworks to guide AI-related disclosures has grown. This study examines how US-listed AI-driven companies disclose AI implementations in Form 10-K filings. Drawing on the resource-based view, signalling theory and accounting standards frameworks, it identifies factors that influence AI disclosure practices and assesses their alignment with accounting and regulatory expectations. Design/methodology/approach Using logistic regression and qualitative content analysis, the study analyses AI-related disclosures. The analysis evaluates the extent to which companies' AI disclosures align with relevant US Accounting Standards Codifications, regulatory specificity and overall reporting quality. Findings Results show significant variation in the scope and quality of AI disclosures. Companies with larger intangible assets and higher research and development intensity disclose more extensively and younger, less profitable companies also report more frequently, suggesting both compliance and strategic signalling motives. However, disclosure quality remains inconsistent, with some companies providing transparent, standards-aligned information and others relying on vague or promotional language indicative of “AI-washing.” Research limitations/implications The study focuses on US-listed, technology-oriented companies that mention AI in their 10-K filings. Findings underscore the need for more precise accounting guidance, enhanced comparability and AI-specific disclosure standards. Practical implications The proposed framework supports standard-setters, regulators and accounting professionals in improving the transparency, consistency and assurance of AI-related reporting. Originality/value This study advances understanding of AI disclosure behaviour by extending signalling theory and the resource-based view to corporate reporting, explaining how companies use AI disclosures to convey innovation, capability and reporting credibility.