Systematic Review and Thematic Synthesis of the Relationship Between Artificial Intelligence and Financial Performance
DOI :
https://doi.org/10.5281/zenodo.21002621Mots-clés :
Artificial intelligence, financial performance, systematic literature review, accounting-based performanceRésumé
This study provides a systematic synthesis of the empirical literature examining the relationship between artificial intelligence (AI) adoption and firm-level financial performance. Using a systematic literature review (SLR) methodology, the study analyzes 67 peer-reviewed articles published between 2000 and 2025 that meet predefined inclusion criteria. The selection process followed the PRISMA protocol, and the findings were synthesized through thematic analysis. Overall, the reviewed literature indicates a predominantly positive association between AI adoption and financial performance. In particular, AI investments tend to generate more pronounced benefits in the medium to long term, especially in accounting-based performance indicators such as return on assets (ROA), return on equity (ROE), and operating profit margins. Market-based performance measures also frequently respond positively to AI initiatives; however, these effects appear to be more contingent on contextual and firm-specific conditions. Nevertheless, several studies report limited or even negative short-term outcomes, largely due to high implementation costs, organizational alignment challenges, and learning-curve effects associated with AI deployment. By integrating the empirical evidence through the lenses of the Resource-Based View, Dynamic Capabilities Theory, and Information Processing Theory, this study highlights the conditional nature of financial value creation from AI adoption and provides implications for managers and policymakers.
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