Thomas Hormaza Dow, Vinay Kumar, Sébastien Favre, Ann Lockquell, Banafsheh Peyrovian, Ahmed MS Hegazy, Martin Berezaga, Viviane Paul, Lyndon Johnson, Naomi Tessier, Christine Gerard, Amin Ranj Bar, Aboubakar Samake, Hichem Benzair, Jon Schlaich, Annabelle Roy · Zenodo (CERN European Organization for Nuclear Research) 2026 · 2026
DOI: 10.5281/zenodo.23123641
Counts differ because each database indexes a different set of publications. We treat OpenAlex as the canonical count; Google Scholar is not shown (no API, and crawling it violates its ToS).
Generative and agentic artificial intelligence (AI) can now produce market analyses, forecasts, and client presentations in minutes. Polished output therefore reveals little about what a business student understands. This conceptual article asks which human capabilities business education must deliberately develop by 2030 so that AI extends professional competence while judgment, agency, and responsibility stay with the person. Sixteen members of the Business Physics AI Simulation Lab answered this question from their own fields, which include agentic AI, AI reliability, market research, analytics, leadership, public relations, project management, cybersecurity, and marketing automation. Two AI agents also responded. A synthesis of the eighteen perspectives identifies six capabilities: accountability and oversight; independent thinking and problem framing; evidence, verification, and critical evaluation; human communication and relationships; professional agency and resilience; and AI and technical literacy. All six rest on knowledge of the field and experience of doing the work. Ten patterns recur across the contributions, most often that foundations must come before delegation and that oversight should be proportional to risk. The article introduces the principle of safety of learning by design, and proposes four levels of human responsibility (Must Perform, Must Understand, Must Verify, Must Own) for deciding how AI should enter a learning activity. It applies the REACT framework (Reason, Evidence, Accountability, Constraints, Trade-offs) as a decision habit for students. Ten recommendations for business programs follow, including assessing judgment alongside output and making resource awareness part of AI judgment. The article argues that the goal is human-AI complementarity.
No comments yet — start the discussion below.