Multi-AI Cross-Validation
An original methodology designed to mitigate single-model bias
From conceptualization to completion, this paper took 3 days (≈20 hours) — a timeline that is well worth reflecting upon for academic researchers.
This study tests frontier AI's problem-solving ability on a management case and finds that, in mid-level management analysis, AI surpasses experienced human professionals on two dimensions: speed (solutions within minutes) and depth (more persuasive reasoning and theoretical grounding).
The study employs a two-stage Multi-AI Prompting design. In Stage 1, Gemini and Claude independently diagnose the case using identical prompts. In Stage 2, ChatGPT reviews and compares the two reports and assigns weighted scores against journal-level case discussion and senior management decision-making standards. This is a blind test, and the reviewer ChatGPT was not informed which AI models produced the analytical reports.
Both models complete the full analytical chain of phenomenon — root cause — theory — solution, drawing on Business Process Reengineering (BPR) and the Service Quality Gap Model (SERVQUAL) for root cause reasoning. Systematic differences emerge: Claude excels in quantitative evidence, theory-evidence linkage, and tiered solutions; Gemini delivers sharper problem identification and more direct practical guidance. Overall, AI's problem identification and practical recommendations match mid-level managers, while the stronger model approaches the quality expected from a faculty member drafting a Teaching Note in theoretical mobilization and root cause reasoning.
This study makes three contributions: (1) it establishes a Multi-AI Cross-Validation methodology that mitigates single-model bias in management case research; (2) it shows that management consulting and mid-level managerial functions in corporations face AI-driven replacement pressure; and (3) it reframes the instructor's role in case-based teaching from answer provider to case analysis quality evaluator.