Abstract

Motivation. Generative AI (GenAI) is reshaping knowledge work by shifting value creation from task execution toward delegation, verification, orchestration, and judgment with agentic information systems. As GenAI becomes embedded in daily work, organizations face an investment–capability gap: adoption outpaces employee skills and safeguards. Prior IS research shows that agentic IS artifacts can initiate action, assume partial responsibility, and operate under uncertainty (Baird & Maruping, 2021). Yet less is known about converting intense GenAI adoption into workforce resilience rather than ambiguity, skill erosion, and AI-related stress. This TREO asks: How do training and governance capabilities build workforce resilience under GenAI-enabled work redesign? Conceptual Framework. GenAI adoption intensity creates new demands for employees to evaluate AI outputs, coordinate human–AI workflows, and exercise judgment when recommendations are incomplete, biased, or incorrect. To meet these demands, organizations need an AI workforce enablement capability with two mutually reinforcing dimensions. Training Design Quality captures experiential, role-relevant learning for prompting, verification, evaluation, escalation, and responsible use. AI Governance Maturity captures routines that make GenAI use transparent, accountable, predictable, and monitorable, such as AI-use policies, sign-offs, audit trails, workflow libraries, and human-in-the-loop protocols. This aligns with IS research on human-AI ecologies emphasizing guardrails, norms, monitoring, and intervention (Grisold et al., 2025). Together, training quality and governance maturity build AI skill readiness, which supports workforce resilience: employees’ capacity to adapt skills, maintain confidence, and function effectively as GenAI changes task boundaries, routines, and accountability expectations. Proposed Method. We propose a quantitative-first study of enterprise IT and software professionals who regularly use GenAI tools. A PLS-SEM survey will test relationships among GenAI adoption intensity, training design quality, AI governance maturity, AI skill readiness, and workforce resilience. Follow-up interviews and job-description analysis will contextualize how these capabilities appear in everyday AI-augmented work. Expected Contribution. This TREO contributes a workforce capability view of GenAI adoption. Rather than evaluating success only through short-term automation or productivity, we argue that organizations should assess whether GenAI strengthens employees’ capacity to adapt, verify, and remain confident under AI-enabled work redesign. For managers, the framework suggests resilient GenAI adoption requires not only AI tool access, but also experiential training, mature governance, and employee skill readiness.

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