This study explores how Artificial Intelligence (AI) and Sentiment Analysis can improve stakeholder engagement in decision-making processes and non-financial reporting practices of organizations. Using a mixed method that combines the interventionist approach with case study analysis, this research work demonstrates how these technologies can overcome the limitations of traditional methods by analyzing and incorporating a broader range of stakeholder perspectives. Sentiment analysis, leveraging Natural Language Processing (NLP) and machine learning techniques, proves effective in processing large volumes of textual data from diverse sources, such as social media and blogs, to identify key themes and anticipate emerging trends. Empirical evidence from cases such as the Popular Financial Reporting and the Social Report of organizations selected illustrates the transformative potential of these tools in making reporting documents more transparent, accessible, and inclusive. These tools facilitate meaningful dialogue between organizations and their stakeholders, addressing diverse needs and expectations. Finally, the paper concludes with recommendations for further advancements in AI and sentiment analysis, emphasizing the need for ethically sound and representative approaches to improve non-financial reporting and stakeholder dialogue in diverse organizational contexts.
KEYWORDS: Artificial Intelligence, Sentiment Analysis, Non-Financial Reporting, Dialogic Accounting, CSRD, Sustainability, GRI, ESRS
