Generative AI-Enabled Dynamic Workforce Training: A Conceptual and Cross-Industry Framework for Employee Skill Development and Customer Delight

Abstract

Purpose:
This paper examines how generative artificial intelligence can be used to redesign workforce training from a static, course-centric activity into a dynamic, role-specific and performance-linked capability. The paper focuses on how AI-supported training can improve employee competence and, through that competence, improve customer experience and customer delight.

Design/methodology/approach:
The paper uses a conceptual review and multiple-case synthesis approach. It integrates literature on human capital development, adaptive learning, service-profit chain logic, human-AI augmentation, and AI governance with industry examples from retail, manufacturing, hospitality, financial services and telecommunications.

Findings:
Generative AI can improve workforce training through five linked mechanisms: personalized learning paths, scenario-based simulations, real-time work support, knowledge capture from experts, and continuous skill analytics. These mechanisms can reduce time-to-competence, improve consistency of service delivery, preserve tacit knowledge, and enable more responsive customer interactions. However, value is achieved only when AI training is governed, grounded in trusted enterprise knowledge, integrated with work systems, and supported by managers.

Originality/value:
The paper contributes a practical framework connecting generative AI-enabled training, employee capability development and customer delight. It also proposes implementation controls that reduce risks related to hallucination, privacy, bias, dependency on AI, and low workforce adoption.

Practical implications:
Organizations should treat generative AI training as a workforce capability platform rather than a content-generation shortcut. The recommended roadmap starts with high-value use cases, builds a governed knowledge layer, pilots role-based AI coaches, measures skill and customer outcomes, and scales only after governance and adoption risks are controlled.