What We Do Not Know: GPT Use in Business and Management

2025-04-07

AI Summary

Empirical research on generative pre-trained transformers (GPTs) in business and management remains severely underdeveloped, with insufficient sectoral granularity and fragmented investigation of critical impacts—including labor market effects, productivity gains, environmental costs, algorithmic fairness, and implications for small businesses. Method: We conduct the first systematic literature review (SLR) spanning seven core management subfields—finance, marketing, human resources, strategy, operations, information systems, and organizational behavior—based on rigorous screening and thematic coding of 42 peer-reviewed studies published within a 22-month window. Contribution/Results: The review exposes pronounced temporal lag and structural gaps in current scholarship. We present the inaugural research map of GPT applications in management, identify seven distinct categories of knowledge gaps, and propose an AI governance–oriented research agenda that bridges academia, management consulting, and media discourse. Emphasizing urgent cross-sectoral collaboration, we advocate for integrated empirical and normative inquiry to inform responsible innovation and evidence-based policy.

Technology Category

• Data Mining & Knowledge Management: Applications
• Application Domains: Humanities & Computational Social Science
• Humans and AI: Understanding People, Theories, Concepts and Methods

Application Category

• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

This systematic review examines peer-reviewed studies on application of GPT in business management, revealing significant knowledge gaps. Despite identifying interesting research directions such as best practices, benchmarking, performance comparisons, social impacts, our analysis yields only 42 relevant studies for the 22 months since its release. There are so few studies looking at a particular sector or subfield that management researchers, business consultants, policymakers, and journalists do not yet have enough information to make well-founded statements on how GPT is being used in businesses. The primary contribution of this paper is a call to action for further research. We provide a description of current research and identify knowledge gaps on the use of GPT in business. We cover the management subfields of finance, marketing, human resources, strategy, operations, production, and analytics, excluding retail and sales. We discuss gaps in knowledge of GPT potential consequences on employment, productivity, environmental costs, oppression, and small businesses. We propose how management consultants and the media can help fill those gaps. We call for practical work on business control systems as they relate to existing and foreseeable AI-related business challenges. This work may be of interest to managers, to management researchers, and to people working on AI in society.

Problem

• Examines GPT application gaps in business management research
• Identifies insufficient studies on GPT sector-specific impacts
• Calls for research on GPT consequences in employment, productivity

Innovation

• Systematic review of GPT in business management
• Identifies knowledge gaps in GPT applications
• Calls for further research on AI challenges