Artificial Intelligence and Financial Performance: A Qualitative Literature Review

Abstract

Purpose: This study examines the relationship between artificial intelligence (AI) integration and organizational financial performance and identifies the conditions that strengthen or weaken this relationship.
Methodology: The study uses a qualitative literature-review design. Twenty peer-reviewed journal articles published between 2019 and 2026 were synthesized through thematic content analysis.
Results: The literature identifies four recurring mechanisms: operational efficiency and cost control, risk and fraud reduction, revenue and investment optimization, and financial reporting and assurance. AI is generally associated with stronger profitability, cost efficiency, risk-adjusted returns, asset allocation, and firm value when supported by reliable data, adequate infrastructure, skilled personnel, explainable models, and effective governance.
Conclusions: AI should be treated as an organizational capability rather than a stand-alone technology because its financial value depends on responsible integration with financial processes and governance systems.
Limitations: The review is limited to twenty articles, uses qualitative synthesis rather than meta-analysis, and includes more evidence from banking, auditing, and investment than from nonfinancial sectors.
Contributions: The study integrates fragmented evidence into a direct conceptual account of the AI-financial performance relationship and provides a basis for future company-level empirical research.