Organizations undergoing digital transformation increasingly rely on artificial intelligence (AI) to enhance talent management and workforce decision-making. However, many organizations struggle to translate workforce analytics into meaningful behavioral and organizational outcomes. This study investigates the design and implementation of an AI-enabled Talent Management and Engagement Activation Framework within a large logistics organization operating under national economic transformation priorities.

Using an action research methodology, the study examines how predictive talent analytics, skills-first workforce architecture, and AI-supported leadership dashboards influence employee engagement, succession readiness, and strategic workforce alignment. The research was conducted through iterativeintervention cycles involving diagnostic assessment, system design, leadership adoption, and evaluation of workforce outcomes. Multiple data sources were used, including workforce analytics dashboards, leadership decision records, engagement indicators, and capability maturity assessments.

The findings suggest that embedding AI into talent management systems significantly improves visibility intoworkforce capability gaps and accelerates leadership decision- making. The framework enabled managers to move from reactive workforce management toward predictive talent planning while strengthening accountability through transparent data access. Importantly, the research also demonstrates that employee trust and adoption increased when AI governance principles such as transparency, ethical oversight, and human-in-the-loop decision-making were incorporated.

This study contributes a practitioner-oriented model for integrating AI into enterprise talent systems. Theproposed framework offers organizations a replicable approach to aligning workforce strategy, leadership accountability, and employee engagement during large-scale digital transformation.