AI Diffusion

This AI adoption and organizational strategy case study examines how artificial intelligence diffuses across an academic institution and the factors that enable or inhibit adoption at scale. While interest in AI tools has grown rapidly, adoption across faculty remains uneven due to differences in incentives, awareness, perceived value, and institutional support. The work analyzed how organizational structure, culture, and enablement mechanisms influence AI uptake, with the goal of identifying strategies that could accelerate responsible and effective adoption across the McCombs School of Business.

The primary goal was to understand the drivers and barriers to AI adoption across faculty and translate those insights into actionable organizational strategies. Secondary goals included identifying gaps in awareness, incentives, and support structures, and outlining approaches that could enable more consistent, responsible, and scalable use of AI across the institution.

The work was conducted as a team-based organizational analysis focused on understanding patterns of AI adoption across the institution. I conducted interviews with faculty and students to gather qualitative insight, which were combined with interview findings from other teams and survey data collected across the class. Our group developed hypotheses around potential drivers of low or uneven AI adoption, and I tailored interview questions to test and refine those hypotheses. I helped synthesize qualitative and quantitative findings into a cohesive view of adoption barriers and contributed to translating those insights into organizational strategy recommendations.

A key decision was to frame uneven AI adoption as an organizational and behavioral challenge rather than a technology capability issue. This shifted the analysis toward incentives, perceived risk, and cultural norms, trading technical depth for a clearer understanding of human and institutional barriers. Another tradeoff involved prioritizing hypothesis-driven qualitative interviews over broader but shallower data collection, favoring depth of insight into adoption resistance over wider coverage of use cases.

The work produced a clear, evidence-backed view of why AI adoption varied across faculty despite growing interest in AI tools. Interview insights and survey data helped validate key hypotheses around incentives, perceived risk, and enablement gaps, and surfaced specific organizational barriers to adoption. The final recommendations provided a practical framework for how institutions could support more consistent and responsible AI adoption through targeted enablement rather than introducing new AI tools.

This work strengthened my ability to approach AI adoption as an organizational change challenge rather than a purely technical one. It sharpened my judgment around forming and testing hypotheses using qualitative interview data, synthesizing insights from both interviews and surveys, and translating research findings into practical, institution-level strategy recommendations.