The Hospitality Management program at The Ohio State University, part of the College of Education and Human Ecology, is a large, established academic unit training students for leadership roles in the global hospitality and tourism industry. It combines business fundamentals with specialized knowledge in hotel, restaurant, event, and tourism management. As part of a major public research university (size band 10,001+), it operates within a complex ecosystem of teaching, research, and industry partnership.

Why AI matters at this scale

For a program of this size within a massive university, AI presents a dual opportunity: to radically improve educational outcomes and operational efficiency, and to future-proof the curriculum itself. The hospitality industry is being transformed by data analytics, dynamic pricing, and personalized guest experiences—all powered by AI. A leading academic program must not only teach about these tools but also intelligently leverage them in its own operations to manage large student cohorts, personalize learning pathways, and strengthen its value to industry partners. At this scale, even marginal improvements in student retention or job placement generate significant financial and reputational returns.

Concrete AI Opportunities with ROI

1. Adaptive Learning & Simulation Platforms: Implementing an AI-driven platform that personalizes case studies and simulations based on individual student progress can increase engagement and mastery. ROI is realized through higher course completion rates, better student evaluations, and stronger graduate competency, making the program more attractive to prospective students and employers. 2. Predictive Student Advising Analytics: Deploying machine learning models to identify students at risk of dropping out or underperforming allows for targeted academic support. For a large program, reducing attrition by even a few percentage points preserves significant tuition revenue and improves graduation metrics, directly impacting university funding and rankings. 3. AI-Powered Industry Intelligence Hub: Creating a system that uses natural language processing to aggregate and analyze real-time hospitality industry news, job postings, and market research provides unparalleled value. It keeps curriculum relevant, informs faculty research, and demonstrates thought leadership. ROI comes from enhanced industry partnership opportunities, grant funding, and elevated program prestige.

Deployment Risks for Large Institutions

Deploying AI in a large university setting carries specific risks. Integration Complexity is high due to legacy student information systems (SIS) and learning management systems (LMS) that may not easily connect with new AI tools. Data Governance and Privacy is a paramount concern under regulations like FERPA; establishing clear protocols for using student data in AI models requires careful legal review. Organizational Inertia is significant; securing buy-in across departmental, college, and central IT silos can slow pilot programs to a crawl. Finally, Talent Gap exists—hiring or developing data scientists and AI-literate instructional designers within traditional academic salary structures can be challenging. A successful strategy requires phased pilots with strong executive sponsorship, clear ethical guidelines, and partnerships with external ed-tech providers.