Dr. Hila Lifshitz

Professor of Management @ WBS, Faculty Associate @Harvard LISH, Head of AiiN

About Me

Academic Background

Doctor of Business Administration, Management
Harvard University, Harvard Business School

Hila Lifshitz is a Professor of Management at Warwick Business School and a visiting faculty at Harvard University, at the Lab for Innovation Science (LISH). She is heading the Artificial Intelligence innovation Network at WBS.

Professor of Management at Warwick Business School and Faculty Associate at Harvard’s Lab for Innovation Science (LISH, AI Institute). She is the head of the Artificial Intelligence Innovation Network (AiiN) that connects industry leaders and academic researchers to lead together on the forefront of knowledge on AI “in the wild”, in complex, hybrid, and meaningful real-world human-machine interactions. Her AI-related research focuses on two main knowledge work processes: the creative process of innovation (new product development for sustainable futures, content and music generation, scientific research…) and the risky process of critical decision making (business model decisions, medical diagnosis decisions, financial investment decisions…). She investigates human-machine collaboration (when and how humans engage with AI), and how we can amplify the innovation outcomes of knowledge work both in new ventures and large organizations. She works with leaders, managers, and professionals (engineers, physicians, analysts, musicians, new product designers) in organizations such as NASA, P&G, and BCG, to deeply understand their world and design field studies with them to transform and redesign the future of work & life. She has produced more than a decade of award-winning research on scientific and technological innovation and knowledge sharing in the digital age and how to accelerate it. Her recent work was awarded for the ISSIP Excellence in Service innovation award, the NSF’s prestigious INSPIRE grant, the Industry Studies Association Frank Giarrantani Rising Star award, and the Industry Research Institute’s grant for research on R&D.

Prior to academia, Professor Lifshitz worked as a strategy consultant for seven years, specializing in growth and innovation strategy in telecommunications, consumer goods and finance.

Selected Publications

The Cybernetic Teammate: A Field Experiment on Generative AI and Teamwork

Fabrizio Dell’Acqua, Charles Ayoubi, Hila Lifshitz, Raffaella Sadun, Ethan Mollick, Lilach Mollick, Yi Han, Jeff Goldman, Hari Nair, Stew Taub, Karim R. Lakhani (2026)

Organization Science

We examine how artificial intelligence (AI) impacts three core pillars of collaboration—performance enhancement, expertise integration, and social engagement—through a preregistered field experiment with 791 professionals at Procter & Gamble, a global consumer packaged goods company. Working on real product innovation challenges, professionals were randomly assigned to work either with or without AI, and either individually or with another professional in new product development teams. Our findings show that (1) AI significantly enhances performance: individuals with AI matched the performance of teams without AI, suggesting that AI can effectively replicate certain benefits of human collaboration. Moreover, (2) AI helps bridge functional silos: without AI, research and development professionals tended to suggest more technical solutions, whereas commercial professionals leaned toward commercially oriented proposals. Professionals using AI produced more balanced solutions, regardless of their professional background. (3) AI’s language-based interface prompted more positive self-reported emotional responses among participants, suggesting it can fulfill part of the social and motivational role traditionally offered by human teammates.

Fabrizio Dell’Acqua, Edward McFowland III, Ethan Mollick, Hila Lifshitz, Katherine C. Kellogg, Saran Rajendran, Lisa Krayer, François Candelon, Karim R. Lakhani (2026)

Organization Science Vol. 37, No. 2

The public release of Large Language Models (LLMs) has sparked tremendous interest in how humans will use Artificial Intelligence (AI) to accomplish a variety of tasks. In our study conducted with Boston Consulting Group, a global management consulting firm, we examine the performance implications of AI on realistic, complex, and knowledge-intensive tasks. The pre-registered experiment involved 758 consultants comprising about 7% of the individual contributor-level consultants at the company.

Moran Lazar, Hila Lifshitz, Charles Ayoubi and Hen Emuna (2025)

Academy of Management JournalVol. 68, No. 5

Does “eureka” still ring true in the algorithmic age? This paper investigates algorithmic design and its impact on idea generation. The use of algorithms transforms knowledge work processes, redefining experts’ roles across industries. While experts are typically viewed as the cornerstone of knowledge work, an emerging debate is underway about whether expertise is still necessary in the idea generation process for creativity and innovation. Integrating creativity, expertise in knowledge work, and algorithmic design literatures, we theorize on the interplay between expertise and algorithmic design in idea generation. We suggest that the prevailing algorithmic design, focusing on information exploitation, is misaligned with the recombinant innovation processes needed for idea generation, especially for experts. We design an exploration-based modification to the prevalent exploitation-based algorithm—Google Search. We hypothesize that when using exploration-based algorithms, experts can overcome confirmation bias and generate more creative ideas through recombinant innovation. Moving beyond the individual level, experts are central to bursting “ideation bubbles”—clusters of similar ideas—thus enhancing idea diversity. We test our theory in a laboratory study and a field experiment through a global ideation challenge in sustainability launched on Freelancer. Findings offer insights into designing and using algorithms to augment human expertise for innovation.

Lebovitz S., Lifshitz H., and Levina N. (2022)

Organization Science. 33(1):126-14

Artificial intelligence (AI) technologies promise to transform how professionals conduct knowledge work by augmenting their capabilities for making professional judgment. We know little, however, about how human-AI augmentation takes place in practice. Yet gaining this understanding is particularly important when professionals use AI tools to form judgments on critical decisions. We conducted an in-depth field study in a major US hospital where AI tools were used in three departments by diagnostic radiologists making breast cancer, lung cancer, and bone age determinations. The study illustrates the hindering effects of opacity that professionals experienced when using AI tools and explores how these professionals grappled with it in practice. In all three departments, this opacity resulted in professionals experiencing increased uncertainty because AI tool results often diverged from their initial judgment without providing underlying reasoning. Only in one department (of the three), did professionals consistently incorporate AI results into their final judgments, achieving what we call engaged augmentation. These professionals invested in AI interrogation practices – practices enacted by human experts to relate their own knowledge claims to AI knowledge claims. Professionals in the other two departments did not enact such practices and did not incorporate AI inputs into their final decisions, which we call un-engaged “augmentation.” Our study unpacks the challenges involved in augmenting professional judgment with powerful, yet opaque, technologies and contributes to literatures on AI adoption in knowledge work.

Arazy O., Daxenberg J., Lifshitz H., Nov O., and Gurevych I. (2016)

Information Systems Research, 27(4), 792–812.

Increasingly, new forms of organizing for knowledge production are built around self-organizing co-production community models with ambiguous role definitions. Current theories struggle to explain how high-quality knowledge is developed in these settings and how participants self-organize in the absence of role definitions, traditional organizational controls, or formal coordination mechanisms. In this article, we engage the puzzle by investigating the temporal dynamics underlying emergent roles on individual and organizational levels. Comprised of a multi-level large-scale empirical study of Wikipedia stretching over a decade, our study investigates emergent roles in terms of prototypical activity patterns that organically emerge from individuals’ knowledge production actions. Employing a stratified sample of a thousand Wikipedia articles, we tracked two hundred thousand distinct participants and seven hundred thousand coproduction activities, and recorded each activity’s type. We found that participants’ role taking behavior is turbulent across roles, with substantial flow in and out of co-production work. Our findings at the organizational level, however, show that work is organized around a highly stable set of emergent roles, despite the absence of traditional stabilizing mechanisms such as pre-defined work procedures or role expectations. This dualism in emergent work is conceptualized as “Turbulent Stability”. We attribute the stabilizing factor to the artifact-centric production process and present evidence to illustrate the mutual adjustment of role taking according to the artifact’s needs and stage. We discuss the importance of the a↵ordances of Wikipedia in enabling such tacit coordination. This study advances our theoretical understanding of the nature of emergent roles and self-organizing knowledge coproduction. We discuss the implications for custodians of online communities, as well as for managers of firms engaging in selforganized knowledge collaboration.

Lebovitz S., and Levina N. Lifshitz H. (2021)

Management Information Systems Quarterly, , 45(3), 1501-1526

Organizational decision-makers need to evaluate AI tools in light of increasing claims that such tools outperform human experts. Yet, measuring the quality of knowledge work is challenging, raising the question of how to evaluate AI performance in such contexts. We investigate this question through a field study of a major US hospital, observing how managers evaluated five different machine-learning (ML) based AI tools. Each tool reported high performance according to standard AI accuracy measures, which were based on ground truth labels provided by qualified experts. Trying these tools out in practice, however, revealed that none of them met expectations. Searching for explanations, managers began confronting the high uncertainty of experts’ know-what knowledge captured in ground truth labels used to train and validate ML models. In practice, experts address this uncertainty by drawing on rich know-how practices, which were not incorporated into these ML-based tools. Discovering the disconnect between AI’s know-what and experts’ know-how enabled managers to better understand the risks and benefits of each tool. This study shows dangers of treating ground truth labels used in ML models objectively when the underlying knowledge is uncertain. We outline implications of our study for developing, training, and evaluating AI for knowledge work.

Lifshitz H. (2018)

Administrative Science Quarterly, 63(4), 746–782.

Using a longitudinal in-depth field study at NASA, I investigate how the open, or peer-production, innovation model affects R&D professionals, their work, and the locus of innovation. R&D professionals are known for keeping their knowledge work within clearly defined boundaries, protecting it from individuals outside those boundaries, and rejecting meritorious innovation that is created outside disciplinary boundaries. The open innovation model challenges these boundaries and opens the knowledge work to be conducted by anyone who chooses to contribute. At NASA, the open model led to a scientific breakthrough at unprecedented speed using unusually limited resources; yet it challenged not only the knowledge-work boundaries but also the professional identity of the R&D professionals. This led to divergent reactions from R&D professionals, as adopting the open model required them to go through a multifaceted transformation. Only R&D professionals who underwent identity refocusing work dismantled their boundaries, truly adopting the knowledge from outside and sharing their internal knowledge. Others who did not go through that identity work failed to incorporate the solutions the open model produced. Adopting open innovation without a change in R&D professionals’ identity resulted in no real change in the R&D process. This paper reveals how such processes unfold and illustrates the critical role of professional identity work in changing knowledge-work boundaries and shifting the locus of innovation.

Arazy O., Lifshitz H., and Balila A. 2018.

Journal of the Association for Information Science and Technology, 70(1), 3-15.

Roles provide a key coordination mechanism in peer-production. Whereas one stream in the literature has focused on the structural responsibilities associated with roles, the another has stressed the emergent nature of work. To date, these streams have proceeded largely in parallel. In seeking to enhance our understanding of the tension between structure and agency in peerproduction, we investigate the interplay between structural and emergent roles. Our study explored the breadth of structural roles in Wikipedia (English version) and their linkage to various forms of activities. Our analyses show that despite the latitude in selecting their mode of participation, participants’ structural and emergent roles are tightly coupled. Our discussion highlights that: (I) participants often stay close to the “production ground floor” despite the assignment into structural roles; and (II) there are typical modifications in activity patterns associated with role-assignment, namely: functional specialization, multi-specialization, defunctionalization, changes in communication patterns, management of identity, and role definition. We contribute to theory of coordination and roles, as well as provide some practical implications.