Transforming Management Decision Making: An Artificial Intelligence Experiment in Executive Context <sup>1</sup>
This study examines the integration of a document-interpreting digital tool into the learning environment of an Executive MBA program at a premier business school in Latin America. While digital tools are increasingly introduced into professional education, little experimental evidence exists on their measurable effects in executive settings, especially in emerging-economy contexts. Using a randomized controlled design, we analyze how structured exposure to such a tool affects performance, preparation time, and evaluation time across a cohort of experienced professionals. Our findings show no statistically significant differences in test performance between treatment and control groups, but participants using the tool complete both preparation and test activities in significantly less time. These efficiency gains, particularly strong among first-year students, suggest that such technologies may improve time use without compromising measured outcome quality in this case-based setting. Because the study did not directly measure mental effort, user experience, or qualitative features of reasoning, the mechanisms behind these gains should be interpreted cautiously. The study contributes rare experimental evidence from Latin American executive education and highlights the value of analyzing AI’s impact not only through outcome quality but also through process efficiency. The implications are most directly relevant to comparable executive-learning environments operating under time pressure.
Authors
- Sergio Pardo‐Jaramillo (ORCID: https://orcid.org/0000-0002-6342-3583)
- Jose E. Gomez-Gonzalez
- Juan Manuel Vicaría
- Miguel I. Gómez
Institutions
- Universidad de La Sabana (CO)
- Lehman College (US)
- City University of New York (US)
- College of Charleston (US)
- Cornell University (US)
Publication Details
- Journal
- Latin American Business Review
- Published
- 2026-07-27
- DOI
- https://doi.org/10.1080/10978526.2026.2699965
- Primary Topic
- AI and HR Technologies
- Type
- article
- Field-Weighted Citation Impact
- 0.00