Jeffrey Hans Peo

My research uses quantitative methods to examine how artificial intelligence changes the organization of knowledge-intensive work. Broadly, I study how firms integrate data science expertise into established professional settings and what this process reveals about capability building, organizational performance, and the evolving structure of expertise.

Thesis

​My thesis examines how firms sustain collaboration across management and data science domains, how rising external demand for data science expertise expands access to client work historically staffed by established professionals, and how firms evaluate, develop, and retain workers with portable technical expertise within traditional career systems. I explore these themes across three essay:

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1. Getting Familiar: Epistemic Hybrids and the Reproduction of Collaboration
   
   Abstract: Team-based knowledge work requires collaboration among members with specialized expertise. While sustaining such collaborations is difficult even in conventional settings, the introduction of artificial intelligence intensifies this challenge by bringing divergent approaches to knowledge generation and evaluation into the same team. This paper examines how epistemic hybrids – individuals whose formal training spans professional and data science domains – shape the persistence of cross-epistemic collaboration over time and its consequences for project performance. Using data from 1,331 AI-enabled consulting engagements conducted by a global management consultancy, I find that hybrid-inclusive project leadership teams are more likely to collaborate again in the future and accumulate higher levels of familiarity through repeated interaction across projects. This accumulated familiarity is positively associated with project performance, suggesting that epistemic hybrids improve coordination not only through their contributions within projects but also by shaping the relational structures through which collaboration is sustained over time.
    

2. Shifting Boundaries: External Valuation and the Reshaping of Internal Jurisdiction
   
   Abstract: To develop artificial intelligence to address complex problems, firms are building teams of data scientists. The incorporation of this new occupational group introduces a rival knowledge base into organizations previously governed by domain experts. Using data from 34,644 management consulting engagements across 1,256 clients, this study connects the external valuation of expertise with internal adjustments to workplace jurisdiction between those with and without data science training. I show that in early years, occupational groups were entrenched in work aligned with their specialized knowledge. However, as clients update how they value expertise, I find that consultants with data science training are able to encroach on work traditionally led by non-technical consultants. This audience-driven jurisdictional drift has broad implications as organizations look to incorporate data scientists to work alongside existing occupational groups.
    

3. Making Portable Expertise Stick: Retention and Advancement among Data Scientists in Professional Service Firms​​​