Artificial intelligence is rapidly transforming human resource management by enabling advanced workforce analytics and data-driven decision support (Davenport & Ronanki, 2018; Tambe et al., 2019). Despite these developments, many organizations struggle to integrate AI insights into leadership decision-making in ways that maintain ethical accountability and human judgment (Wilson & Daugherty, 2018). This research-in-progress study examines how AI-enabled decision-support systems influence strategic HR capability building in large organizations undergoing digital transformation.

The study introduces the concept of Algorithmic Humility, a leadership approach that integrates data-driven intelligence with ethical reasoning and human-centered judgment. Algorithmic humility proposes that leaders should neither blindly rely on algorithmic recommendations nor dismiss analytical insights, but instead develop the capability to interpret, question, and responsibly apply AI-generated information.

Using an action research methodology within a large logistics organization, the study investigates how AI-driven talent analytics influence leadership decision processes related to succession planning, bias reduction, and skills-first workforce architecture (Marler & Boudreau, 2017). The research involves iterative intervention cycles where predictive workforce analytics and leadership dashboards are introduced into talent management processes, followed by observation and reflection on leadership behavior and organizational outcomes.

Preliminary insights suggest that while AI systems improve transparency and analytical rigor in HR decision-making, they simultaneously require new leadership competencies related to data interpretation, ethical oversight, and collaborative human–AI decision processes (Wilson & Daugherty, 2018). This research aims to bridge the gap between AI system design and practical human capital strategy implementation by developing a practitioner-oriented framework for integrating AI into HR leadership practice.