The Changing Cognitive Role of Product Managers in the Age of AI: Why Human Judgment Alone Is No Longer Sufficient

Purpose: As the environments in which products operate become ever more data-rich, dynamic, and interconnected, PMs must balance customer telemetry, experimentation, and market intelligence with stakeholder requirements, making decisions that are critical and time-sensitive at the same time. Using bounded rationality and cognitive theory, this study investigates the evolving PM role from individual sense-making to managing human-AI systems for decision-making. This study posits that the adoption of AI technology is no longer merely an enabler of efficiency gains but a cognitive necessity for effective decision-making, considering the detrimental effects of information overload on decision-making performance and well-being (Arnold et al., 2023). Materials and Methods: The research design is based on a descriptive and explanatory research model that integrates literature from decision theory, cognitive science, and knowledge management with survey data from practicing product managers in technology-driven organizations (n=174). The key areas of focus in the research include the cognitive load, decision fatigue, prioritization, speed of execution, and the use of AI-based decision support. Findings: The results reveal that Product managers (PMs) who lack AI-based decision support systems are likely to experience cognitive overload, decision fatigue, unclear prioritization, and slower execution cycles. Conversely, using AI-based systems can lead to better information triage, improved pattern detection, and increased confidence in trade-off decisions. In line with previous research on human-AI collaboration, the results reveal that task-type and design-based effects of using AI-based systems can lead to coordination losses when poorly designed (Vaccaro et al., 2024). Implications to Theory, Practice, and Policy: The study contributes to the development of the theory of bounded rationality by placing artificial intelligence as a cognitive augmentation layer in product decision systems, rather than replacing human judgment. In practice, this means that the study reframes artificial intelligence literacy, evaluation discipline, and decision support design as core competencies of Product Management. The policy implication of this study is to place artificial intelligence decision support within product governance structures, ensuring transparency, bias mitigation, and accountability, and in line with emerging principles for artificial intelligence-enhanced decision making (Herath Pathirannehelage et al., 2025). Keywords: Product Management; Artificial Intelligence; Cognitive Load; Bounded Rationality; Knowledge Management; Decision Support Systems