The Role of Generative Artificial Intelligence in Modern Business Decision-Making: Applications, Opportunities, and Future Challenges

Abstract

Generative artificial intelligence (GenAI) has moved rapidly from an experimental technology to a tool actively reshaping how managers gather information, evaluate options, and reach decisions across strategic, operational, and customer-facing business functions. This paper reviews the theoretical and empirical literature on GenAI's role in business decision-making, situating recent large language model (LLM)-based applications within the older organizational decision-making and bounded-rationality literature that predates generative AI by decades. The review synthesizes controlled productivity experiments, large-scale economic-potential estimates, organizational decision-structure theory, and the emerging literature on human-AI complementarity and its limits, including evidence that AI assistance can degrade performance when applied outside a model's effective capability frontier. Particular attention is given to the distinction between GenAI's demonstrated value in well-structured, information-synthesis-heavy decision tasks and its more contested role in tasks requiring novel judgment or accountability. Comparative tables summarize reported productivity effects, business function applications, and organizational barriers to adoption across the reviewed literature. The paper concludes that GenAI's current business value is concentrated in augmenting, rather than automating, decision-making, with the strongest evidence for productivity gains among relatively lower-skilled or lower-performing decision-makers, and identifies the mapping of AI capability boundaries within specific decision domains as the central future research prospect. Generative artificial intelligence (GenAI) has moved rapidly from an experimental technology to a tool actively reshaping how managers gather information, evaluate options, and reach decisions across strategic, operational, and customer-facing business functions. This paper reviews the theoretical and empirical literature on GenAI's role in business decision-making, situating recent large language model (LLM)-based applications within the older organizational decision-making and bounded-rationality literature that predates generative AI by decades. The review synthesizes controlled productivity experiments, large-scale economic-potential estimates, organizational decision-structure theory, and the emerging literature on human-AI complementarity and its limits, including evidence that AI assistance can degrade performance when applied outside a model's effective capability frontier. Particular attention is given to the distinction between GenAI's demonstrated value in well-structured, information-synthesis-heavy decision tasks and its more contested role in tasks requiring novel judgment or accountability. Comparative tables summarize reported productivity effects, business function applications, and organizational barriers to adoption across the reviewed literature. The paper concludes that GenAI's current business value is concentrated in augmenting, rather than automating, decision-making, with the strongest evidence for productivity gains among relatively lower-skilled or lower-performing decision-makers, and identifies the mapping of AI capability boundaries within specific decision domains as the central future research prospect.