Designing Human–AI Collaborative Decision Analytics Frameworks to Enhance Managerial Judgment and Organizational Performance

The fast spread of artificial intelligence (AI) in the United States organizations has radically altered the managerial decision-making process, but on the other hand, it has augmented the complexity and uncertainty in decision, and the accountability stresses. Despite the high-level predictive and prescriptive potentials of AI-based analytics, most organizations have difficulties converting algorithmic results into sustainable managerial decisions. Low levels of trust, lack of explanation, and poor integration between AI systems and human judgment have been caused by over reliance on automation, weak explain ability, and poor organizational outcomes. Current literature has majorly focused on automation-based views of decision support, with a severe lack of insight into the coordinated manner in which human experience and AI intelligence can be systematically integrated with the assistance of analytics. This paper fills this gap by outlining a Human-AI Collaborative Decision Analytics Framework that could be beneficial to improve managerial decisions and organizational performance. Following a conceptual research design, the study integrates interdisciplinary literature in the field of managerial decision-making theory, business analytics, and governance of AI in its attempt to establish an integrative framework where analytics becomes the focal interpretive intercession between AI outputs and human decision-makers. The framework has five overlapping layers such as data, AI analytics, business analytics interpretation, human judgment, and feedback learning that combine to facilitate transparency, accountability, and contextual decision-making. The framework is depicted in the most important areas of the organization with the main focus on the strategic management and workforce decision-making and the secondary focus on the finance, operations, and marketing. The framework minimizes the effects of the algorithmic bias, automation bias, and enhances workforce confidence through embedding managerial control and ethical reasoning and contextual evaluation frameworks into the workflows of AI-assisted decision-making. The contribution of the study to the theory is that it develops human-grounded decision analytics and to practice by providing practical advice to executives and analytics leaders. The presented framework contributes to the responsible use of AI, productivity, and economic competitiveness in the United States in the long term.