Research
My research examines how firms build and renew capabilities as technological change alters both the expertise they need and the organizational systems through which that expertise is deployed. I study this challenge in the context of data science and artificial intelligence, focusing on how firms sustain collaboration across knowledge domains, embed professionals whose skills are highly portable, and reallocate authority as new forms of expertise gain market value.
My doctoral research draws on ten years of proprietary project, personnel, and client-engagement records from a global management consultancy as it developed its data science and artificial intelligence capabilities. The resulting longitudinal dataset covers more than 45,000 projects and approximately 15,000 employees serving roughly 1,200 clients.
Working Papers
Job Market Paper
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 and partially accounts for the performance advantage of hybrid-inclusive teams, suggesting that epistemic hybrids create value not only within individual projects but also by shaping the relational structures through which collaboration is sustained over time.
Making Portable Expertise Stick: Retention and Advancement among Data Scientists in Professional Service Firms
Abstract
Professional service firms are hiring data scientists to incorporate artificial intelligence into client work. Yet the same technical expertise that makes these employees strategically valuable is also highly portable across organizations and industries, complicating retention. Using detailed work histories of 243 junior data scientists at a global management consultancy, I examine whether exposure to the firm’s core non-AI consulting work is associated with exit and advancement. I find that data scientists with greater exposure to non-AI assignments are significantly less likely to exit, with no corresponding penalty to advancement. I argue that participation in core consulting work gives technically specialized employees access to firm-specific opportunities embedded in the firm’s client work and career system that outside employers may struggle to replicate. These findings show how staffing decisions can help firms embed employees with highly portable expertise, making strategically valuable human capital a more durable organizational resource.
Shifting Boundaries: External Valuation and the Reshaping of Internal Jurisdiction
Abstract
To develop artificial intelligence for complex problems, professional service firms are increasingly hiring data scientists, introducing a new expert group into organizations historically governed by established professionals. Using seven years of data from a global management consultancy, I analyze 6,825 first-time partner–client engagements to examine how the external valuation of expertise reshapes internal jurisdiction between partners with and without data science training. I find that clients are more likely to reengage partners with data science backgrounds, including for subsequent work unrelated to AI. This client preference is accompanied by an asymmetric shift from an initially entrenched division of labor: data science partners gain access to traditional consulting engagements, while traditional partners do not gain comparable access to AI-related work. These findings reveal an audience-driven process of jurisdictional drift through which external audiences can alter occupational boundaries even in the absence of direct contestation between expert groups.
Publications
Sako, M. & Peo, J. (2025). “Leveraging or Overcoming Distance? Global Strategy and Structure of Professional Services Firms.” The Journal of Applied Behavioral Science, 61(2), 225–250. Read↗︎
Journal Articles
Smets, M., Rodgers, I. & Peo, J. (2023). “Innovation in Professional Service Firms.” In Elgar Encyclopedia of Services. Edward Elgar Publishing.