Optimizing corporate financial performance through AI: A literature review

Abstract

This study explores how AI optimizes the financial performance of companies in a turbulent economic environment. The objective is to identify the technologies and mechanisms within this technology that enhance competitiveness. The motivation stems from the necessity for organizations to adapt to technological changes and heightened expectations of efficiency. An integrative review cross-references academic and professional literature, including empirical studies and theoretical models such as Simon’s decision theory and sector reports. The approach incorporates multidisciplinary perspectives, including management, computer science, and economics.The main theoretical conclusions generated by this study are, on the one hand, that AI improves decision-making through big data analysis, with a measurable impact on productivity. Moreover, automation frees managers to focus on strategic tasks instead of repetitive ones, particularly in accounting and auditing. Additionally, integrated features—especially prediction—significantly reduce fraud and anticipate financial risks. On the other hand, debates arise between substantive AI, meaning human replacement, and complementary AI, i.e., collaboration between humans and machines.Furthermore, performance measurement and evaluation indicators distinguish between financial criteria and sustainable criteria, including environmental costs, highlighting the need for an integrated approach. However, some limitations of this theoretical article emerge, such as the neglect of applications to SMEs in favor of large companies, as well as under-researched algorithmic biases and ethical challenges, which lead to discrimination phenomena. Finally, there is insufficient documentation on the long-term organizational resilience effects of AI.Nevertheless, the current field reveals several limitations, for example, the overemphasis on technical aspects—specifically algorithms—at the expense of cultural issues related to resistance to change and the lack of differentiated sector-specific models, whether in banking, industry, etc. AI transforms financial performance through technological innovation, but its optimal deployment requires addressing theoretical gaps, namely resilience, ethics, and SMEs, as well as balancing economic efficiency with social responsibility.

JEL Classification: M21-P17-O36

Paper type: Theoretical Research