Technology & Operations Management
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- 2025
- Working Paper
The GenAI Wall Effect: Examining the Limits to Horizontal Expertise Transfer Between Occupational Insiders and Outsiders
By: Luca Vendraminelli, Matthew DosSantos DiSorbo, Annika Hildebrandt, Edward McFowland III, Arvind Karunakaran and Iavor BojinovAs firms continue to seek efficiency by deploying Generative AI (GenAI) tools across various organizational functions, a critical question emerges: When and why does GenAI enable (versus constrain) individuals from one occupational group (i.e., outsiders) to perform tasks assigned to another occupational group (i.e., insiders), with equivalent speed and quality? And does GenAI’s effect diminish as the “knowledge distance” between occupational groups increases? In an experiment conducted at a large UK firm, we examine these questions. Three groups—occupational insiders, adjacent outsiders, and distant outsiders—attempted to both conceptualize as well as execute tasks that are “core” to occupational insiders and were randomly assigned to receive support from a bespoke GenAI tool. We found that a “GenAI wall”—that is, the point at which GenAI can no longer meaningfully reduce the expertise gap between occupational insiders and outsiders—emerged for the joint effect of knowledge distance and task characteristics. Specifically, we found that GenAI is more effective at bridging expertise gaps between near (rather than distant) occupations, and more so for conceptualization (as opposed to execution) tasks. We discuss the implications of these findings for scholarship on occupations, learning, and division of labor in the wake of emerging technologies such as GenAI.
- 2025
- Working Paper
The GenAI Wall Effect: Examining the Limits to Horizontal Expertise Transfer Between Occupational Insiders and Outsiders
By: Luca Vendraminelli, Matthew DosSantos DiSorbo, Annika Hildebrandt, Edward McFowland III, Arvind Karunakaran and Iavor BojinovAs firms continue to seek efficiency by deploying Generative AI (GenAI) tools across various organizational functions, a critical question emerges: When and why does GenAI enable (versus constrain) individuals from one occupational group (i.e., outsiders) to perform tasks assigned to another occupational group (i.e., insiders), with equivalent...
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- 2025
- Working Paper
Reliable Switchback Experiments with Rerandomization for Auction Environments at Procter & Gamble
By: Tu Ni, Eleni Kalfountzou and Iavor BojinovAuction environments play an increasingly central role across industries such as transportation, commodities, and media. Companies like Procter & Gamble (P&G) rely on advanced digital solutions to optimize auction performance and must rigorously validate algorithmic changes before wide-scale deployment. Yet standard A/B testing methods are frequently infeasible in these settings because user randomization is not under the control of the auction seller. Switchback experiments, which randomize treatment assignment over discrete time periods, offer a practical alternative but often suffer from low statistical power and are further complicated by potential carryover effects, where previous treatments can affect current outcomes. This paper introduces a new rerandomization-based design that addresses both the issue of achieving adequate covariate balance and the challenges posed by carryover effects. Building on assignment paths from a completely randomized design, we measure covariate imbalance and only accept assignment schedules below a specified threshold, thus enhancing precision. We then propose an analytic adjustment for the practical scenario when the experiment has to be paused, treating disruptions—such as business parameter constraints or technical failures—as fixed yet unknown events that can be incorporated into the variance calibration. Through extensive numerical simulations mimicking real data from the auction environment, we show that our approach substantially boosts efficiency, remains robust to different choices of covariate sets and imbalance thresholds, and adapts well under assignment non-compliance. Since Jan 2025, P&G has integrated the new rerandomization strategy into its experimentation workflow—applying it to 70 experiments across five auction hypotheses in eight global markets. The results indicate a potential 10–20% sales lift and 5–15% cost savings. Beyond its immediate application to switchback experiments at P&G, the methodology underscores a broader principle in data-driven decision-making: carefully engineered randomization, combined with robust analytic strategies that account for real-world implementation challenges, can yield reliable and actionable insights even in complex operational contexts.
- 2025
- Working Paper
Reliable Switchback Experiments with Rerandomization for Auction Environments at Procter & Gamble
By: Tu Ni, Eleni Kalfountzou and Iavor BojinovAuction environments play an increasingly central role across industries such as transportation, commodities, and media. Companies like Procter & Gamble (P&G) rely on advanced digital solutions to optimize auction performance and must rigorously validate algorithmic changes before wide-scale deployment. Yet standard A/B testing methods are...
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- September–October 2025
- Article
Addressing Gen AI’s Quality-Control Problem
By: Stefan Thomke, Philipp Eisenhauer and Puneet SahniFor all the enthusiasm around generative AI, a hurdle is limiting its adoption: the technology’s tendency to make things up, leave things out, and create so many possibilities that it is hard to figure out which will be effective. That’s why the vast majority of companies employ human reviews and stand-alone testing tools, but these quality-control methods are expensive, and they can handle only a fraction of gen AI’s total output. Amazon has developed a better approach for its massive product catalog operation: a gen AI–based system named Catalog AI that can automatically detect and block unreliable data, produce ideas for new product pages and test their effectiveness, and improve itself with feedback from quality checks and experiments. In this article Harvard Business School’s Stefan Thomke and Amazon’s Philipp Eisenhauer and Puneet Sahni describe Amazon’s system for performing quality control on AI-generated content at scale. Although Amazon considers Catalog AI to be a work in progress, the authors believe that it is far enough along that managers at other organizations can benefit from learning about it now.
- September–October 2025
- Article
Addressing Gen AI’s Quality-Control Problem
By: Stefan Thomke, Philipp Eisenhauer and Puneet SahniFor all the enthusiasm around generative AI, a hurdle is limiting its adoption: the technology’s tendency to make things up, leave things out, and create so many possibilities that it is hard to figure out which will be effective. That’s why the vast majority of companies employ human reviews and stand-alone testing tools, but these...
About the Unit
As the world of operations has changed, so have interests and priorities within the Unit. Historically, the TOM Unit focused on manufacturing and the development of physical products. Over the past several years, we have expanded our research, course development, and course offerings to encompass new issues in information technology, supply chains, and service industries.
The field of TOM is concerned with the design, management, and improvement of operating systems and processes. As we seek to understand the challenges confronting firms competing in today's demanding environment, the focus of our work has broadened to include the multiple activities comprising a firm's "operating core":
- the multi-function, multi-firm system that includes basic research, design, engineering, product and process development and production of goods and services within individual operating units;
- the networks of information and material flows that tie operating units together and the systems that support these networks;
- the distribution and delivery of goods and services to customers.
Recent Publications
The GenAI Wall Effect: Examining the Limits to Horizontal Expertise Transfer Between Occupational Insiders and Outsiders
- 2025 |
- Working Paper |
- Faculty Research
Reliable Switchback Experiments with Rerandomization for Auction Environments at Procter & Gamble
- 2025 |
- Working Paper |
- Faculty Research
Addressing Gen AI’s Quality-Control Problem
- September–October 2025 |
- Article |
- Harvard Business Review
How Digital Integration Is Reconfiguring Value Chains
- September–October 2025 |
- Article |
- Harvard Business Review
The Toyota Production System in Action: TPS at Toyota Motoro Manufacturing Kentucky
- August 2025 |
- Supplement |
- Faculty Research
Gary Convis: Supplement to 625-003 Knowledge Transfer: Toyota, NUMMI, and GM
- August 2025 |
- Supplement |
- Faculty Research
New Economic Forces Behind the Value Distribution of Innovation
- 2025 |
- Working Paper |
- Faculty Research
Difference-in-Differences Subset Scan
- 2025 |
- Article |
- Proceedings of the ACM SIGKDD Conference on Knowledge Discovery and Data Mining
Harvard Business Publishing
Seminars & Conferences
- 16 Sep 2025