Exploring the Effects of Generative Artificial Intelligence on the Preparation, Analysis, and Interpretation of Financial Reports in Large and Complex Organizations
Keywords:
Generative artificial intelligence, financial reporting, financial analysis, large organizations, internal control, data governance, intelligent accountingAbstract
This study aimed to explore the effects of generative artificial intelligence on the preparation, analysis, and interpretation of financial reports in large and complex organizations, with emphasis on opportunities, risks, and governance requirements. This qualitative exploratory study was conducted using semi-structured interviews. The participants were 24 financial managers, financial reporting managers, financial analysts, internal auditors, internal control specialists, and financial systems/data experts working in large organizations located in Tehran. Participants were selected through purposive sampling with maximum variation. Data collection continued until theoretical saturation was reached; saturation occurred at the twenty-first interview, and three additional interviews were conducted to confirm thematic stability. The interviews were transcribed, coded, and analyzed through thematic analysis using NVivo software. Five main categories emerged from the data: intelligent acceleration of financial report preparation, deepening of financial analysis and interpretation of complex data, reconstruction of financial narrative and management reporting, emergence of new risks related to reliability, confidentiality, and controllability, and transformation of professional roles and financial competencies. Participants emphasized that generative AI can reduce the time required for drafting reports, preparing explanatory notes, benchmarking, and developing managerial scenarios. However, they also noted that ungoverned use may increase interpretive errors, overreliance, weak auditability, and ambiguity in professional accountability. Generative AI should be viewed not as a substitute for professional financial judgment, but as an augmenting technology that can improve speed, analytical depth, and reporting coherence. Its effective use requires reliable data architecture, AI-specific internal controls, human oversight, specialized training, and transparent accountability mechanisms.
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