The promise of Generative AI (GenAI) often sounds like this: give any employee access to AI tools, and they’ll suddenly be able to perform tasks outside their domain of expertise with remarkable proficiency and speed. As discussed in the new working paper “The GenAI Wall Effect: Examining the Limits to Horizontal Expertise Transfer Between Occupational Insiders and Outsiders,” the reality of AI’s ability to balance the scales across occupational skillsets is far more nuanced. Written by a team of six authors, including two Principal Investigators and a Research Associate in the Data Science and AI Operations Lab at the Digital Data Design Institute at Harvard (D^3), the article reveals surprising answers about the transformative power of AI in the workplace through a comprehensive study of 78 employees at a UK-based global trading company.
Key Insight: The GenAI Wall
“[W]e predict a ‘GenAI wall effect’ […] the emergence of a point at which GenAI can no longer meaningfully reduce the expertise gaps between insiders and outsiders because of the wider knowledge distance between their jobs.” [1]
While most research has focused on how AI helps lower-performing individuals catch up to their higher performing colleagues within the same job, this study instead focused on whether GenAI could help people from different occupations take on tasks that aren’t typically part of their role. To do so, the authors defined three types of participants: insiders (those who already perform certain tasks as part of their jobs), adjacent outsiders (whose roles are related but don’t directly perform those tasks), and distant outsiders (whose roles have little overlap in tasks). The study then introduces ideas of “knowledge distance” and “expertise gaps,” how far apart two roles are in terms of the skills they use, and the authors claim that GenAI can close the distance for adjacent outsiders, but hits a ‘wall’ with distant outsiders where its benefits stop.
Key Insight: An AI Field Experiment
“[W]hen assisted by GenAI, marketing specialists and technology specialists produced article conceptualizations on par with web analysts.” [2]
To find out where GenAI helps and where it hits limits, the researchers ran a large experiment with employees at the UK-based firm IG, using web analysts who regularly write marketing articles (insiders), marketing specialists from the same department who don’t write articles (adjacent outsiders), and software developers and data scientists (distant outsiders). Each worker had to complete two parts of the web analyst role: (1) conceptualization, building a structured article brief with keywords, headings, and FAQs, and (2) execution, writing the full article. Some participants had access to custom GenAI tools, and others did not. The results of the conceptualization task showed that GenAI can be a powerful equalizer: not only did it improve quality, but also speed, and the gains were especially large for lower-performing employees.
Key Insight: When the Wall Appears
“In short, GenAI levels the playing field in article execution only for marketing specialists.” [3]
The picture changed when participants moved to the execution task. With GenAI support, the web analysts (insiders) and marketing specialists (adjacent outsiders) both produced strong articles, but the technologists (distant outsiders) lagged behind. In other words, AI narrowed the gap for marketers, but a wall appeared for developers and data scientists. Why did this happen? The study’s interviews offer a clue: web analysts and marketers approached the task with the shared foundation of sensitivity to audience needs, conversion strategies, and the rhythms of effective marketing copy. That background let them use GenAI’s suggestions wisely, keeping what worked, editing what didn’t, and shaping the writing into something publishable.
Why This Matters
For business leaders deciding how to employ AI, this study offers a new operational map based around adjacency. Employees can likely expand into related domains, but may struggle with distant ones. AI-assisted cross-training might work best for conceptual and strategic work, while specialized roles with complex execution tasks will still likely call for narrow-focused experts. Most importantly, capitalize on where AI aids human knowledge the most, allowing you to redesign roles and career paths around the skills and strengths that remain uniquely human and critical to your organization.
Bonus
This study was also recently discussed in Charter, the business reporting section of Time. Read their analysis here.
References
[1] Luca Vendraminelli et al., “The GenAI Wall Effect: Examining the Limits to Horizontal Expertise Transfer Between Occupational Insiders and Outsiders,” Harvard Business School Technology & Operations Mgt. Unit Working Paper No. 26-011, Harvard Business School Working Paper No. 26-011 (September 08, 2025): 3, https://ssrn.com/abstract=5462694.
[2] Vendraminelli et al., “The GenAI Wall Effect,” 26.
[3] Vendraminelli et al., “The GenAI Wall Effect,” 30.
Meet the Authors
Luca Vendraminelli is a Postdoctoral Researcher at the Digital Economy Lab and the Stanford Institute for Human-Centered Artificial Intelligence (HAI) at Stanford University.
Matthew DosSantos DiSorbo is a PhD student in the Technology and Operations Management Unit at Harvard Business School.
Edward McFowland III is an Assistant Professor in the Technology and Operations Management Unit at Harvard Business School and Principal Investigator at the D^3 Data Science and AI Operations Lab hosted within the Laboratory for Innovation Science.
Arvind Karunakaran is an Assistant Professor at Stanford University in the Department of Management Science and Engineering.
Iavor Bojinov is an Associate Professor of Business Administration at Harvard Business School and Principal Investigator at the D^3 Data Science and AI Operations Lab hosted within the Laboratory for Innovation Science.