Patrick Diogenia is a graduate student in Communication Studies at New Mexico State University whose research interests span organizational communication, leadership, decision-making, and the application of artificial intelligence and machine learning to organizational and business problems. His work is particularly concerned with how communication, managerial judgment, and analytical tools shape the quality of decisions within organizations.
Alongside his academic work, Diogenia brings more than two decades of professional experience in marketing, growth strategy, customer analytics, loyalty, and organizational transformation. He has worked across Fortune 500, consulting, entrepreneurial, and public-sector environments, with experience translating customer and organizational data into strategy, building new capabilities, and leading complex initiatives. This practitioner background informs his interest in research that connects theory with problems encountered by leaders and organizations in practice.
His current work includes research on leadership behavior and organizational decision-making, including the concept of Strength Overshoot—the idea that a leadership strength can become counterproductive when it is applied beyond the point at which a situation benefits from it. He is also interested in the organizational implications of AI, particularly the distinction between technical model performance and the broader managerial, behavioral, and communication conditions required for analytical tools to create meaningful value.
Diogenia holds MBA degrees from Indiana University’s Kelley School of Business and Alliance Manchester Business School at the University of Manchester, where he graduated among the top students in his cohort. He earned his undergraduate degree from the University of Pennsylvania, graduating summa cum laude.
His broader interests include organizational behavior, workplace communication, customer and audience segmentation, technology adoption, and the ways leaders interpret and act on information under conditions of uncertainty.
To learn more about me, visit my LinkedIn, personal website, or Bluesky.
