Artificial intelligence (AI) refers to a set of computational techniques, including machine learning, natural language processing, deep learning, and generative AI, that enable systems to perform tasks traditionally requiring human intelligence, such as prediction, pattern recognition, and decision-making. The rapid diffusion of AI into business organizations is transforming how information is processed, decisions are made, and knowledge-intensive work is performed, creating both new opportunities for economic value and new challenges for human judgment, organizational governance, and accountability. The growing adoption of AI across accounting, finance, and management makes it increasingly important to understand not only what AI can do, but also how and under what conditions it affects individuals, organizations, and markets. This paper provides a comprehensive review of the rapidly growing literature on artificial intelligence across these three disciplines. We synthesize existing research to examine how AI is transforming information processing, decision-making, governance, and organizational performance. In accounting, AI enhances auditing, financial reporting, and fraud detection while raising concerns regarding transparency and professional judgment. In finance, AI improves asset pricing, risk assessment, and trading strategies by leveraging large-scale structured and unstructured data. In management, AI reshapes organizational design, human capital, strategic decision-making, and innovation through increasingly sophisticated human–AI collaboration. Across these disciplines, we organize the literature around several unifying themes, including information asymmetry, automation versus augmentation, decision quality, interpretability, and governance. We further identify important research gaps concerning whether AI’s predictive and analytical advantages translate into meaningful economic and organizational outcomes, how AI reshapes human judgment and skills, the emerging risks, and the need for stronger research designs. By integrating evidence across three major business disciplines, this review provides a unified framework for understanding AI’s transformative role in organizations and offers a roadmap for future interdisciplinary research on the economic, behavioral, organizational, and governance consequences of AI.
Artificial intelligence (AI) encompasses a broad range of computational technologies, including machine learning (ML), deep learning, natural language processing (NLP), robotic process automation (RPA), and generative AI. Recent advances in computational power, the availability of large-scale structured and unstructured data, and the development of large language models (LLMs) have dramatically expanded AI’s capabilities, enabling systems to perform tasks that traditionally required human intelligence, such as prediction, pattern recognition, language understanding, and complex decision-making [
1,
2]. As organizations increasingly integrate AI into their operations, these technologies are reshaping how information is generated, analyzed, and utilized, making AI one of the most influential technological developments affecting modern business [
3].
Although AI is transforming virtually every aspect of business, its impact extends beyond improvements in computational efficiency. By lowering the cost of information processing, analysis, and prediction, AI fundamentally changes how organizations collect information, allocate resources, evaluate risk, and make strategic decisions [
1]. Consequently, AI is not merely an operational tool but a general-purpose technology that is reshaping organizational processes, market interactions, and competitive dynamics across accounting, finance, and management.
In accounting, AI has transformed core functions such as auditing, financial reporting, and managerial accounting. Machine learning algorithms enhance fraud detection, risk assessment, and the identification of financial reporting irregularities [
4], while NLP techniques enable researchers and practitioners to extract value-relevant information from annual reports, earnings calls, and other corporate disclosures [
5,
6]. More recently, generative AI has expanded these applications by assisting with audit documentation, financial reporting, and managerial decision support. These developments have the potential to improve efficiency, accuracy, and decision quality, but they also raise important concerns regarding transparency, professional judgment, and governance.
AI has also become a major driver of innovation in finance. A growing body of the literature demonstrates that machine learning methods can outperform traditional econometric models in forecasting stock returns, assessing credit risk, and optimizing portfolios [
7,
8]. Beyond predictive modeling, AI increasingly influences algorithmic trading, investment analysis, and financial decision-making by enabling the processing of vast quantities of structured and unstructured information; in short, AI has fundamentally reshaped all aspects of the capital market by enhancing the speed, scale, and sophistication of information acquisition, analysis, and dissemination [
9]. These advances improve forecasting accuracy, investment decision-making, and market efficiency. However, the growing reliance on complex algorithms introduces important challenges related to model interpretability, robustness, and systemic risk, particularly as AI-driven decision-making becomes more pervasive in financial markets [
10].
In management, AI is transforming organizational design, strategic decision-making, and human capital management. By reducing the cost of prediction and information processing, AI enables organizations to make more data-driven decisions while redefining how work is coordinated across individuals and organizational units [
1]. At the same time, AI changes the nature of work by automating routine tasks, augmenting knowledge-intensive activities, and shifting demand toward analytical, technological, and interpersonal skills [
11,
12]. These developments have intensified scholarly interest in understanding the extent to which AI substitutes for human labor or complements human expertise, and how organizations can design effective systems of human–AI collaboration.
Despite the rapid growth of AI research, the existing review literature remains relatively fragmented. Prior reviews have made important contributions by synthesizing applications of machine learning in accounting and finance and by discussing the implementation of machine learning techniques in accounting research and practice [
13,
14]. However, these reviews were developed during a period when ML represented the dominant AI paradigm and therefore primarily emphasize predictive modeling and algorithmic performance. Recent advances in large language models, generative AI, and human–AI collaboration have substantially expanded both the scope and organizational implications of AI, creating opportunities that extend well beyond prediction and classification. Consequently, there is an urgent need for a broader synthesis that reflects this rapidly evolving AI landscape and explains its implications across business functions.
This review addresses these limitations by providing an integrated and cross-disciplinary synthesis of AI research spanning accounting, finance, and management. The integration of these disciplines is purposeful because AI does not affect business functions in isolation. Rather, its adoption simultaneously influences how information is produced and communicated, how financial decisions are made, how employees and managers respond to AI, and how organizations structure decision-making, governance, and resource allocation. Examining these studies together therefore allows us to identify common mechanisms and interdependencies that may be obscured when AI is studied within individual functional domains.
The intended outcome of this synthesis is therefore more than a comprehensive summary of existing studies. We use the cross-disciplinary evidence to develop an overarching framework for understanding how AI creates opportunities and risks at the individual, functional, and organizational levels. We also use this framework to identify where findings converge or diverge across disciplines and to highlight important boundary conditions that should guide future research. In doing so, the review connects the technological evolution from predictive AI to generative AI with broader questions concerning human–AI collaboration, organizational transformation, and responsible AI adoption.
As an Encyclopedia entry, this paper adopts a narrative, cross-disciplinary literature synthesis rather than a formal systematic literature review. We draw on foundational and recent scholarly research on artificial intelligence and its applications in accounting, finance, and management, with particular attention to machine learning, natural language processing, deep learning, and generative AI. The literature was identified using combinations of terms related to AI and its applications within these three domains, supplemented by topic-specific terms addressing decision-making, automation, human–AI interaction, employment, skills, and organizational change. Rather than applying a formal inclusion and exclusion protocol, we organize and synthesize the literature around recurring cross-disciplinary themes to highlight common mechanisms, differences across functional domains, and emerging research opportunities.
The review also has practical implications. By synthesizing evidence across accounting, finance, and management, the paper provides managers and business professionals with a broader understanding of how AI can affect decision quality, efficiency, professional judgment, organizational design, and value creation, while also highlighting risks related to explainability, bias, privacy, security, and accountability. These insights can inform organizations’ decisions regarding AI adoption and governance and can help accounting and finance professionals anticipate how their roles and required skills may evolve as AI becomes increasingly embedded in business processes. For researchers, the synthesis provides a roadmap for studying AI not simply as a technological tool but as an organizational capability with consequences that extend across functional boundaries.
This review makes three primary contributions. First, it extends existing review studies by integrating evidence from accounting, finance, and management into a unified conceptual framework that explains how AI transforms business organizations beyond individual functional domains. Second, it synthesizes recent advances in generative AI, large language models, and human–AI collaboration, thereby updating earlier machine learning-focused reviews to reflect the rapidly evolving AI landscape. Third, it develops a comprehensive research agenda that emphasizes causal inference, responsible AI governance, explainability, AI security and ethics, and the long-term organizational consequences of AI adoption, providing a roadmap for future interdisciplinary research.
The remainder of the paper is organized as follows.
Section 2 provides a conceptual background on AI and its theoretical foundations.
Section 3,
Section 4 and
Section 5 review the literature in accounting, finance, and management, respectively.
Section 6 synthesizes the major research gaps and outlines directions for future research. Finally,
Section 7 concludes by summarizing the principal insights and discussing the broader implications of AI for business research and practice.
This entry is adapted from the peer-reviewed paper https://doi.org/10.3390/encyclopedia6090198
