Talent management in the age of AI: navigating digital dexterity, embeddedness, plateau, and flexibility fatigue

Purpose This study aims to develop and test a theoretical framework for assessing talent management effectiveness in artificial intelligence (AI)-enabled workplaces. Building on Sociotechnical Systems Theory and Conservation of Resources Theory, it advances two hypotheses: digital dexterity as a positive enabler and flexibility fatigue as a negative constraint. Job embeddedness and job plateau are included as control variables to provide additional explanatory power. Design/methodology/approach A survey data from 350 professionals in technology, healthcare, finance and manufacturing were analyzed using multiple regression and structural equation modeling (SEM) to test the hypotheses. Findings Digital dexterity significantly enhances talent management effectiveness, while flexibility fatigue undermines it. Job embeddedness and job plateau contribute as meaningful controls. The SEM model achieved strong fit indices (CFI = 0.94, RMSEA = 0.06), confirming the robustness of the framework. Practical implications Organizations must invest in digital upskilling and AI literacy to enhance employee adaptability, while implementing human-centered policies to reduce cognitive strain and mitigate flexibility fatigue. Originality/value By theorizing and empirically testing the bright (digital dexterity) and dark (flexibility fatigue) sides of AI in talent management, this study advances theory-driven insights into human–AI interaction and provides actionable implications for sustainable workforce strategies.