The effects of artificial intelligence on management education

Felipe A. Csaszar; Michael G. Jacobides; Peter Zemsky

doi:10.1177/14761270251385484

Citation: Csaszar, F. A., Jacobides, M. G., and Zemsky, P. (2026). The effects of artificial intelligence on management education. Strategic Organization 24(2) 440–462. doi:10.1177/14761270251385484

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Paper highlights

Generative AI can already perform many assignments used to teach and assess management students. The issue extends beyond classroom integrity. It puts pressure on the value proposition, activities, costs, and competitive boundaries of business schools.

The paper analyzes management education as a strategy problem. Students pay for more than content: selection, credentials, networks, career access, confidence, and a structured learning experience also matter. AI affects each component differently, so predictions that it will either replace or leave business schools untouched are too simple.

What may change

AI tutors, simulations, and feedback can make learning more personalized and allow faculty to spend less time transmitting standard content.
The same tools strengthen low-cost substitutes, including self-study, education technology firms, and employers’ internal programs.
Traditional assignments and admissions materials become weaker signals when AI can produce them, increasing the need for observed performance and new forms of assessment.
Research can become a stronger source of differentiation if schools use it to understand AI’s effects on management and turn that knowledge into distinctive teaching.
Technology, data, faculty skills, and course redesign require investment even if automation lowers some delivery costs.

Strategic implication

Incremental additions—an AI elective or an optional chatbot—will not address the underlying change. Schools need to reconsider what should be learned, what students should practice themselves, which work should be delegated to AI, and how learning should be demonstrated. Their durable advantages are likely to rest on research capability, trusted certification, peer interaction, and ties to management practice.

Why it matters

If AI makes standard analysis abundant, business schools may enter a third epoch centered on how humans and AI divide managerial work.
AI can improve teaching through tutoring and simulation while also strengthening substitutes for formal education.
When essays and case analyses can be delegated to an LLM, they reveal less about individual capability; assessment may need to become more observed, interactive, and process based.

How to use this paper

Cite this for

A strategic analysis of how generative AI may affect management education.
The claim that AI changes demand, teaching, assessment, and business-school operating models at the same time.
The idea of a possible third epoch of management education centered on human–AI managerial work.

Useful for teaching

Business schools as organizations facing their own AI strategy problem.
Why AI-assisted assignments force schools to rethink assessment rather than only police misconduct.
How research, certification, peer interaction, and practice ties may become more important differentiators.

Careful claim

The paper maps pressures and options for business schools under generative AI; it does not predict a uniform collapse of management education or a simple replacement of schools by AI tools.

Abstract

The emergence of generative AI tools capable of matching human performance in business school assignments challenges fundamental assumptions about management education. This paper explores how AI could fundamentally reshape business schools, suggesting we may be entering a “third epoch” of management education following the practice-oriented era of the early 1900s and the research-focused transformation of the 1960s. As AI begins to rival core analytical capabilities taught in business programs, schools must reconsider their unique value proposition and educational approach.

Through the primary lens of a value-based strategy framework, we analyze how AI could reshape demand patterns, teaching methods, and operational models. The paper identifies key uncertainties and strategic priorities, exploring how business schools could leverage their research strengths to guide this transformation. The potential decline of traditional business education could weaken the foundation of informed and ethical business practices, making adaptation imperative. While AI presents significant challenges to current educational models, it also offers compelling opportunities for schools to reinvent management education for an AI-augmented future.