A Foresight Model for Adopting Artificial Intelligence in Financial Reporting: Emphasizing Perceived Usefulness and Ease of Use

Objective
Accounting aims to provide useful information to support users’ decision-making. Enhancing both the usefulness and ease of use of financial reporting is crucial for improving users’ decision-making processes. However, the mechanisms for improving financial reporting quality through AI-driven technologies, focusing on perceived usefulness and ease of use, remain unclear. This study aims to present a financial reporting quality model based on the application of artificial intelligence, emphasizing perceived usefulness and ease of use.
 
Methods
This applied research adopts a mixed-methods approach. In the qualitative phase, open and semi-structured interviews were conducted with 12 experts and academic professionals specializing in auditing and artificial intelligence. Data were analyzed using grounded theory through open, axial, and selective coding. In the quantitative phase, a sample of 200 auditors employed in Iranian organizations and auditing firms participated. Data analysis in the qualitative phase identified 80 basic themes and 27 axial codes, resulting in the development of a comprehensive model for AI-based financial reporting quality.
 
Results
Results indicate that causal conditions, including big data analytics, task automation, predictive analysis, and anomaly detection, combined with contextual factors such as perceived usefulness and ease of use, significantly impact the core category of financial reporting quality. The core category encompasses enhancing transparency, real-time financial data analysis, reducing discretionary accruals, improving profit and cash flow forecasting, and mitigating fraud and material misstatements. Strategies such as improving efficiency, analytical precision, speed, and financial decision-making result in outcomes such as reducing human error, saving time, boosting auditors' reputation, and enabling continuous auditing. However, intervening conditions such as integration costs, security concerns, and regulatory compliance present challenges. Addressing these factors is essential for achieving more efficient and higher-quality financial reporting.
 
Conclusion
The results indicate that artificial intelligence has the capability to automate and enhance the accuracy of financial reporting processes. By using AI in data collection, analysis, and predictive modeling, companies can achieve a higher standard of reporting quality. This is reflected in real-time insights, trend identification, and improved decision-making capabilities. Accounting aims to provide useful information to support users’ decision-making. Enhancing both the perceived usefulness and ease of use of financial reporting plays a crucial role in improving these decision-making processes. Moreover, integrating artificial intelligence into reporting practices can pave the way for more efficient, accurate, and transparent financial reporting. This process, in addition to improving the quality of reports, helps businesses make better financial decisions and reduce potential risks, ensuring a more robust framework for organizational growth and financial stability. Furthermore, AI can greatly contribute to the automation of routine tasks, allowing human auditors and financial professionals to focus on more strategic and complex issues. By analyzing large data sets at a faster pace than traditional methods, AI reduces the likelihood of errors and inconsistencies in financial reporting. This not only improves the overall reliability of financial statements but also enhances compliance with regulatory requirements. Given AI's potential to increase the accuracy and speed of financial reporting, it is recommended that policymakers design frameworks and regulations that facilitate the use of AI in this area while preventing misuse and security threats. Clear guidelines should be established to ensure ethical use, data protection, and transparency in AI-powered financial reporting systems. Such measures will help mitigate any potential risks associated with the adoption of AI in the sector while allowing for more effective and informed decision-making. This will not only improve the quality of financial reporting but also foster knowledge, development, and innovation in this field, contributing to the evolution of financial practices and the broader economic landscape.