The Impact of AI in Reducing Biases in Managerial Decision-Making: Towards Organizational Agility and Evidence-Based Leadership

Abstract

Cognitive biases, such as confirmation bias, anchoring and overconfidence, represent major challenges for managerial decision-making by impairing rational judgment and leading to suboptimal organizational outcomes. Artificial intelligence (AI) systems are emerging as a transformative tool for mitigating these biases through objective, transparent, data-driven analysis. Leveraging machine learning, predictive analytics and explainable artificial intelligence (XAI), Decision-making can be improvise by these systems reducing reliance on heuristics and offering transparent alternatives that promote evidence-based leadership. Empirical studies demonstrate the effectiveness of AI in a variety of areas, including recruitment, strategic forecasting and supply chain management, where it corrects bias and improves efficiency. Despite this potential, challenges remain, such as algorithmic bias and ethical dilemmas relating to accountability. AI adoption could be this successful as it requires ethical frameworks, hybrid decision models and organizational readiness, including robust data governance and AI literacy. This article synthesizes existing literature to assess the role of AI in reducing managerial bias, its practical applications and implications for promoting an evidence-based leadership culture in dynamic business environments.