Business school reputation is being reshaped by algorithms as much as by people. The authors argue that generative systems have become powerful gatekeepers of trust and visibility, noting that as of July 2025, ChatGPT alone had 800 million weekly active users and handled nearly 2 billion queries daily. Prospective students no longer search only for ranked lists; they ask conversational systems for context-rich recommendations. In this environment, what an institution stands for is increasingly filtered, summarized, and presented by Artificial Intelligence.
The article reframes reputation’s classic pillars of signals, stories, and sentiment for the Artificial Intelligence era. Signals such as rankings and accreditations are now curated by models that interpret and contextualize them. Stories, once owned by schools through alumni narratives and campus histories, are scripted by algorithms assembling fragments across sources. Sentiment, historically driven by media tone and word of mouth, is steered by model framing that can amplify trust or skepticism depending on prompts and inputs. The result is an Artificial Intelligence reforged reputation that schools do not fully control.
The stakes are high. Students and employers increasingly ask systems like ChatGPT, Gemini, and DeepSeek where to study, how to upskill, and which organizations to trust. Visibility and perceived identity hinge on whether a school’s data and narratives are present, structured, and machine readable. A request such as which MBA best prepares someone for a specific career and geography now returns a synthesized explanation of strengths and weaknesses, not a simple list. If a school’s proof points and stories are absent or misinterpreted in the datasets large language models rely on, the institution risks invisibility or distortion.
The authors outline practical lessons. Schools should arm Artificial Intelligence with proof by ensuring rankings, accreditations, achievements, and other structured signals are accessible in public datasets. They should seed narratives through widely shared alumni cases, transformation stories, and faculty thought leadership, then monitor machine sentiment by regularly checking what models say about them. Content audits must span text, video, and audio to ensure consistency and discoverability across formats. For students, reputation management now operates on two fronts: human perception and Artificial Intelligence mediated framing. With recruiters using models to filter résumés and customers consulting algorithms for brand trust, aspiring leaders must learn to seed, curate, and monitor their professional footprint across profiles, articles, podcasts, and public speaking. The future of business education, the authors conclude, will favor schools and leaders whose reputations are legible to both humans and machines, moving beyond search-era tactics to master Artificial Intelligence driven trust.
