Artificial Intelligence and Accounting Research: A Framework and Agenda
📝 Abstract
Recent advances in artificial intelligence, particularly generative AI (GenAI) and large language models (LLMs), are fundamentally transforming accounting research, creating both opportunities and competitive threats for scholars. This paper proposes a framework that classifies AI-accounting research along two dimensions: research focus (accounting-centric versus AI-centric) and methodological approach (AI-based versus traditional methods). We apply this framework to papers from the IJAIS special issue and recent AI-accounting research published in leading accounting journals to map existing studies and identify research opportunities. Using this same framework, we analyze how accounting researchers can leverage their expertise through strategic positioning and collaboration, revealing where accounting scholars’ strengths create the most value. We further examine how GenAI and LLMs transform the research process itself, comparing the capabilities of human researchers and AI agents across the entire research workflow. This analysis reveals that while GenAI democratizes certain research capabilities, it simultaneously intensifies competition by raising expectations for higher-order contributions where human judgment, creativity, and theoretical depth remain valuable. These shifts call for reforming doctoral education to cultivate comparative advantages while building AI fluency.
💡 Analysis
Recent advances in artificial intelligence, particularly generative AI (GenAI) and large language models (LLMs), are fundamentally transforming accounting research, creating both opportunities and competitive threats for scholars. This paper proposes a framework that classifies AI-accounting research along two dimensions: research focus (accounting-centric versus AI-centric) and methodological approach (AI-based versus traditional methods). We apply this framework to papers from the IJAIS special issue and recent AI-accounting research published in leading accounting journals to map existing studies and identify research opportunities. Using this same framework, we analyze how accounting researchers can leverage their expertise through strategic positioning and collaboration, revealing where accounting scholars’ strengths create the most value. We further examine how GenAI and LLMs transform the research process itself, comparing the capabilities of human researchers and AI agents across the entire research workflow. This analysis reveals that while GenAI democratizes certain research capabilities, it simultaneously intensifies competition by raising expectations for higher-order contributions where human judgment, creativity, and theoretical depth remain valuable. These shifts call for reforming doctoral education to cultivate comparative advantages while building AI fluency.
📄 Content
These divergent patterns reflect journals’ complementary missions rather than a hierarchy of research value. Indeed, both types of research should be welcomed and published in both AIS and non-AIS journals. AI-centric research provides essential methodological foundations and critical evaluations that enable and inform accounting-centric research. Similarly, accountingcentric research often grapples with technical challenges that motivate AI innovation. These bidirectional flows demonstrate that both research streams are necessary for the advancement of accounting research. This systematic classification of the AI-accounting literature reveals not only where research has concentrated, but also where different research communities bring complementary strengths. The patterns we observe-particularly the divergence between AIS and non-AIS journals-reflect different but equally valuable approaches to AI-accounting research. These patterns matter because progress at the intersection of AI and accounting increasingly depends on combining the distinctive capabilities of accounting scholars, industry practitioners, and computer scientists. Recognizing these complementary strengths transforms our classification from a purely descriptive exercise into a framework for identifying productive collaborations.
Beyond organizing and synthesizing existing literature, we address a more fundamental question: How should accounting researchers strategically position themselves in an AItransformed research landscape? The integration of AI into accounting creates both opportunities and collaborative imperatives. On one hand, AI provides powerful tools for idea generation, measurement, prediction, and analysis that can enhance traditional accounting inquiries and help researchers communicate insights effectively to different audiences. On the other hand, industry researchers possess superior computational resources and access to proprietary data, while computer science researchers maintain advantages in algorithmic innovation and technical sophistication.
While prior work has analyzed these dynamics through the lens of comparative advantage (Alles and Gray, 2025;Alles et al., 2008), we reframe this as collaborative advantage to recognize that progress at the intersection of AI and accounting benefits from the complementary strengths of different research communities. Through this collaborative advantage perspective, we identify research domains where accounting scholars’ distinctive capabilities, such as deep institutional knowledge, theoretical grounding, causal inference expertise, and independent evaluation capacity, are most valuable. These capabilities position accounting scholars not only to make contributions that neither industry nor computer science researchers would naturally pursue, but also to collaborate effectively with industry and computer science researchers on problems that require both domain expertise and technical innovation.
We identify strategic opportunities in each research domain where accounting scholars’ specialized expertise positions them for unique contributions. These range from developing specialized AI algorithms that prioritize transparency and regulatory compliance over proprietary advantage, to conducting independent critical evaluations that reveal AI’s limitations and implementation challenges, to leveraging AI-generated measurements within rigorous causal inference designs for testing fundamental accounting theories, to exploiting AI-related events as natural experiments for studying core accounting phenomena.
We further recognize that AI’s impact extends beyond research topics and methods to transform the research process itself fundamentally. Our analysis systematically compares human researchers and AI agents across the entire research workflow, from idea generation and literature review through data analysis and manuscript drafting, revealing their advantages. This comparison exposes a critical tension: while GenAI models can now assist with numerous research tasks, potentially democratizing access to specific capabilities, they simultaneously intensify competition by raising expectations for higher-order contributions where human judgment, creativity, and theoretical depth remain valuable, at least for the short term (i.e., 4 to 5 years). These shifts redistribute comparative advantages not only between academic, industry, and computer science researchers, but also across career stages within the accounting academic community itself. The implications are particularly acute for junior scholars and doctoral students, whose traditional roles as research apprentices are being disrupted by technologies that can now perform many tasks that once constituted their primary contributions to research teams.
In response to these challenges, we propose strategic responses at both the individual and institutional levels. For doctoral programs, we advocate adapting the traditional apprenticeship m
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