Abstract: The advent of AI-powered workforce analytics marks a watershed moment in organizational transparency, one that will fundamentally alter the relationship between management effectiveness and corporate accountability. For generations, high employee turnover has been attributed to compensation structures, market conditions, or cultural misalignment—convenient explanations that deflect attention from a more uncomfortable reality. Machine learning algorithms can now detect what HR professionals have long suspected but rarely proven: specific supervisors consistently drive disproportionate attrition, suppressed engagement, and stunted career progression within their teams. This technological capability forces a reckoning. Organizations face a choice between weaponizing these insights through punitive measures or leveraging them to build managerial competence at scale. The latter path requires reimagining performance data as diagnostic rather than judgmental, establishing psychological safety around developmental feedback, and creating systematic pathways for leadership skill acquisition. Companies that navigate this transition successfully will unlock retention improvements that have eluded traditional interventions, while simultaneously cultivating a management culture grounded in continuous learning. Those that mishandle the moment—either by ignoring the data or deploying it without adequate support systems—will trigger defensive organizational dynamics, potential litigation, and an exodus of talent that recognizes dysfunction long before algorithms confirm it.