What Can a Business School Do When Generative Artificial Intelligence Replaces Entry-Level Graduate Jobs?

Purpose: To suggest how business schools can respond when generative AI automates routine, entry-level tasks and erodes early-career opportunities. The paper addresses a focused question: What can a business school do when graduates’ entry-level jobs are replaced or reconfigured by AI?

Approach: This is a perspective article that synthesises recent empirical studies, labour-market evidence, and international policy guidance. Drawing on this integrative review, the paper develops a practical institutional blueprint for programme design, governance, and university-industry collaboration.

Findings: The existing literature indicates that traditional “first-rung” roles are thinning in AI-exposed occupations while expectations for day-one fluency with AI-augmented workflows rise. To bridge this capability gap, the paper proposes a coordinated blueprint: (1) reframe curricula around human-AI complementarity; (2) redesign assessment to evaluate judgment, verification, and communication; (3) build experiential pipelines that replicate the developmental function of first jobs; (4) co-design early-career roles through university-industry collaboration; (5) invest in student well-being and ethical governance; (6) sustain staff development; and (7) address common concerns (academic integrity, equity of access). Collectively, these actions enable business schools to restore apprenticeship-style learning within and immediately after degree programmes.

Originality: The paper links near-term labour-market disruption from generative AI to concrete, institution-level strategies in business education. It offers an actionable, literature-informed blueprint that moves schools beyond placement facilitation to co-creation of AI-era entry pathways, showing how higher education can rebuild the apprenticeship-like learning once provided by traditional entry-level jobs.