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Technology & Operations Management

Technology & Operations Management

  • Faculty
  • Curriculum
  • Seminars & Conferences
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Overview Faculty Curriculum Seminars & Conferences Awards & Honors Doctoral Students
    • 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 Bojinov

    As 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 Bojinov

    As 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...

    • 2025
    • Working Paper

    Reliable Switchback Experiments with Rerandomization for Auction Environments at Procter & Gamble

    By: Tu Ni, Eleni Kalfountzou and Iavor Bojinov

    Auction 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 Bojinov

    Auction 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...

    • September–October 2025
    • Article

    Addressing Gen AI’s Quality-Control Problem

    By: Stefan Thomke, Philipp Eisenhauer and Puneet Sahni

    For 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 Sahni

    For 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

By: Luca Vendraminelli, Matthew DosSantos DiSorbo, Annika Hildebrandt, Edward McFowland III, Arvind Karunakaran and Iavor Bojinov
  • 2025 |
  • Working Paper |
  • Faculty Research
As 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.
Keywords: GenAI; Human Expertise; Labor Demand; Randomized Experiments
Citation
Read Now
Related
Vendraminelli, Luca, Matthew DosSantos DiSorbo, Annika Hildebrandt, Edward McFowland III, Arvind Karunakaran, and Iavor Bojinov. "The GenAI Wall Effect: Examining the Limits to Horizontal Expertise Transfer Between Occupational Insiders and Outsiders." Harvard Business School Working Paper, No. 26-011, September 2025.

Reliable Switchback Experiments with Rerandomization for Auction Environments at Procter & Gamble

By: Tu Ni, Eleni Kalfountzou and Iavor Bojinov
  • 2025 |
  • Working Paper |
  • Faculty Research
Auction 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.
Keywords: A/B Testing; Switchback Experiments; Rerandomization; Auctions
Citation
Read Now
Related
Ni, Tu, Eleni Kalfountzou, and Iavor Bojinov. "Reliable Switchback Experiments with Rerandomization for Auction Environments at Procter & Gamble." Harvard Business School Working Paper, No. 26-012, September 2025.

Addressing Gen AI’s Quality-Control Problem

By: Stefan Thomke, Philipp Eisenhauer and Puneet Sahni
  • September–October 2025 |
  • Article |
  • Harvard Business Review
For 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.
Keywords: AI and Machine Learning; Technology Adoption; Quality
Citation
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Related
Thomke, Stefan, Philipp Eisenhauer, and Puneet Sahni. "Addressing Gen AI’s Quality-Control Problem." Harvard Business Review 103, no. 5 (September–October 2025): 60–67.

How Digital Integration Is Reconfiguring Value Chains

By: Antonio Moreno
  • September–October 2025 |
  • Article |
  • Harvard Business Review
While companies have been “unbundling” their operations and outsourcing tasks for decades, advances in IT are now helping them take that strategy to a whole new level. These technologies make it possible to digitally integrate workflows across organizations, letting firms easily distribute complex chains of activities among multiple entities, including customers. They not only slash collaboration costs but give all players instant access to capabilities that only large firms could once afford. Consider logistics, where thanks to cloud-based services like ShipBob, small brands can fulfill orders just as quickly as retail giants can. In traditional outsourcing, businesses just handed off tasks, but now they’re embedding third-party services into their own operations. This development has given rise to a multitude of hyperspecialized services that firms can tap. It has also inspired the emergence of “orchestrators” that coordinate all the tasks needed to create and deliver offerings. Hyperspecialists and orchestrators offer firms new opportunities to monetize assets, grow revenues, and create markets. But they’re also blurring industry boundaries, and some have started competing directly with their clients.
Keywords: Operations; Technology Adoption; Digital Transformation
Citation
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Moreno, Antonio. "How Digital Integration Is Reconfiguring Value Chains." Harvard Business Review 103, no. 5 (September–October 2025): 50–59.

The Toyota Production System in Action: TPS at Toyota Motoro Manufacturing Kentucky

By: Willy C. Shih
  • August 2025 |
  • Supplement |
  • Faculty Research
This is a supplememtary video that shows the Toyota Production System in action at Toyota Motor Manufacturing Kentucky, Georgetown, KY in August 2024
Keywords: Toyota Production System; Production; Auto Industry; United States
Citation
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Related
Shih, Willy C. "The Toyota Production System in Action: TPS at Toyota Motoro Manufacturing Kentucky." Harvard Business School Multimedia/Video Supplement 626-702, August 2025.

Gary Convis: Supplement to 625-003 Knowledge Transfer: Toyota, NUMMI, and GM

By: Willy C. Shih
  • August 2025 |
  • Supplement |
  • Faculty Research
Set of supplementaru videos featuring Gary Convis, first Plant Manager of NUMMI
Keywords: Culture Change; Organizational Culture; Organizational Change and Adaptation; Factories, Labs, and Plants; Joint Ventures; Transformation; Selection and Staffing; Knowledge Acquisition; Knowledge Sharing; Labor and Management Relations; Auto Industry; Japan; United States
Citation
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Shih, Willy C. "Gary Convis: Supplement to 625-003 Knowledge Transfer: Toyota, NUMMI, and GM." Harvard Business School Multimedia/Video Supplement 625-715, August 2025.

New Economic Forces Behind the Value Distribution of Innovation

By: Timothy F. Bresnahan, Shane Greenstein and Pai-Ling Yin
  • 2025 |
  • Working Paper |
  • Faculty Research
Advances in a general-purpose technology (GPT) enable many firms to invent complementary inventions, or co-inventions, making the GPT more valuable. This study examines the empirical implications of a straightforward model in which firms choose either incremental or novel co-invention. Incremental co-inventors aspire to small gains at low costs and with less uncertainty. Novel co-inventors introduce new products or services with the potential for large returns, but do so at high costs and with uncertain outcomes. Similar firms investing in incremental co-invention will create value proportional to their existing business, a benchmark we illustrate with the experiences at local radio and newspapers. The study then examines the value of co-inventions for the World Wide Web and mobile ecosystems, focusing on success in 2013, using data from many sources. This data supports analysis comparing the incremental and novel regimes. The latter should display a distinctly different upper tail of the distribution of returns. We show that the value distributions for incremental and novel co-invention are far apart. Incremental co-invention is more widely distributed across regions, industries, and firms. Success from novel co-invention is rare, challenging, and the source of the largest value. In the aggregate, novel co-invention creates the most value, so the overall value distribution remains concentrated in a few industries, regions, and firms.
Keywords: Innovation and Invention; Value Creation; Technology Industry
Citation
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Bresnahan, Timothy F., Shane Greenstein, and Pai-Ling Yin. "New Economic Forces Behind the Value Distribution of Innovation." NBER Working Paper Series, No. 34090, August 2025.

Difference-in-Differences Subset Scan

By: Will Stamey, Sriram Somanchi and Edward McFowland III
  • 2025 |
  • Article |
  • Proceedings of the ACM SIGKDD Conference on Knowledge Discovery and Data Mining
Difference-in-differences (DiD) has been extensively applied in the literature to elicit the average causal effect of an intervention or policy. Though researchers explore heterogeneity in the treatment effect with respect to time or some observed covariate (usually driven by domain knowledge), there is limited work on a principled, algorithmic approach to discovering the subgroups that exhibit heterogeneity in the treatment effect in DiD settings. In this research, we propose the Difference-in-Differences Subset Scan (DiD-Scan), which finds subregions of the observed covariate space corresponding to anomalous patterns in the conditional mean treatment effect. We mold our method to the DiD setting by also enabling a scan for dynamic effects over time and across multiple outcome variables. This supports the discovery of patterns where a subset of outcomes are highly impacted for a limited time window. We develop a generalized likelihood ratio-based scoring function to quantify the treatment effect of a given subgroup and propose a computationally efficient method to discover the subgroups with the largest treatment effect. We extend the method to consider correlations across time, a common condition in difference-in-difference settings that increases the difficulty of subset identification. Lastly, we demonstrate the efficacy and interpretability of the method with both simulations and applications to real datasets, replicating and extending two published studies.
Keywords: Research; Analytics and Data Science
Citation
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Stamey, Will, Sriram Somanchi, and Edward McFowland III. "Difference-in-Differences Subset Scan." Proceedings of the ACM SIGKDD Conference on Knowledge Discovery and Data Mining 31st (2025): 2656–2667.
More Publications

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Harvard Business Publishing

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    • Article

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    By: Stefan Thomke, Philipp Eisenhauer and Puneet Sahni
    • July 2025 (Revised August 2025)
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Seminars & Conferences

Sep 16
  • 16 Sep 2025

Stefan Wager, Stanford Graduate School of Business

Technology & Operations Management (TOM) Seminar
→More Seminars & Conferences

Faculty Positions

Harvard Business School seeks candidates in all fields for full time positions. Candidates with outstanding records in PhD or DBA programs are encouraged to apply.
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Contact Information

Technology & Operations Management Unit
Harvard Business School
Morgan Hall
Soldiers Field
Boston, MA 02163
tomunit@hbs.edu

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