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

TL;DR

Problem: There is limited empirically grounded evidence on how GPT is used in business management and its outcomes. Approach: The authors conduct a systematic review of post-2023 peer‑reviewed studies, coding for study type, contributions, subjects, and themes, and applying content analysis due to a small sample. Findings: Only 42 studies are found, many are non-empirical, with just 7 experiments involving business stakeholders, revealing substantial gaps in employment impact, productivity effects, environmental costs, bias, SME experiences, and benchmarking. Significance: The work highlights critical knowledge gaps and issues a call for interdisciplinary, transparent, and governance-focused research to guide managers, consultants, policymakers, and journalists in navigating GPT's business implications.

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.

Figures (2)

• Figure 1: PRISMA Diagram of the Literature Search for Management and GPT
• Figure 2: Studies on GPT per management sub field