How Agentic AI Reshapes Work: The Roles of Cognitive Surplus, Psychological Safety, and AI-Human Task Reallocation

Summary

This protocol describes a survey-based workflow for examining how Agentic AI technological characteristics relate to cognitive surplus, psychological safety, and AI-human task reallocation among 331 employees. Partial least squares structural equation modeling with multi-group analysis reveals role-based differences between leaders and staff.

Abstract

This study examines how the technological characteristics of Agentic AI relate to employees' psychological states and behavioral responses in organizations. Drawing on Job Demands-Resources (JD-R) theory and Conservation of Resources (COR) theory, we conceptualize Agentic AI technological characteristics (AITC) as a higher-order construct comprising autonomous planning, multi-tool orchestration, and proactive feedback, and propose that these characteristics function as job resources associated with greater cognitive surplus and psychological safety, which in turn relate to AI-human task reallocation. We further investigate whether these relationships differ between leaders and staff. A cross-sectional, anonymous online survey was administered via an online survey platform to participants recruited through a research panel using purposive nonprobability sampling; eligibility was restricted to adults with current or prior AI-related work experience, and screened, attention-checked responses yielded a final sample of 331 employees (171 leaders, 160 staff). All constructs were measured with validated scales adapted to the Agentic AI context on 7-point Likert scales. Data were analyzed using partial least squares structural equation modeling (PLS-SEM), applying a two-stage approach for the second-order construct, bootstrapping with 5,000 resamples for direct and indirect effects, and multi-group analysis (MGA) for leader-staff comparisons. The measurement model demonstrated acceptable reliability and validity, and the structural results were consistent with the theorized resource-gain model: AITC was positively associated with cognitive surplus, psychological safety, and task reallocation, with significant mediation pathways and stronger AITC-resource associations among leaders. By focusing on psychological mechanisms rather than technical performance, this study extends emerging Agentic AI research and offers practical implications for role-differentiated AI adoption strategies.

Introduction

The proliferation of Agentic AI is redefining the role of artificial intelligence in industries and organizational life^1,2. Agentic AI can be defined as an advanced form of artificial intelligence that perceives changing environments, sets goals independently through reasoning mechanisms, and orchestrates sequences of actions with minimal human oversight^3,4. It is distinguished from traditional AI, including conventional generative AI tools, in three respects: autonomy, because agentic systems pursue objectives with minimal human intervention rather th....

Protocol

All procedures were performed in accordance with the ethical standards for research involving human participants at aSSIST University and Dong-A University. Since all responses were anonymized upon collection and no personal identifiable information was collected at any stage, the Institutional Review Board of aSSIST University determined that the study was exempt from full review pursuant to Article 15 of the Bioethics and Safety Act of the Republic of Korea and Article 13 of its Enforcement Rule. The survey opened with an information and consent page stating that participation was restricted to individuals currently employed and involved in an AI project, describing....

Results

Participant characteristics

Table 2 presents the demographic characteristics of the final sample (N = 331). The sample comprised 65.0% male and 35.0% female respondents, with a relatively balanced distribution between leaders (51.7%, n = 171) and staff (48.3%, n = 160). The largest age group was under 30 years (46.5%), followed by 31–40 years (32.0%); 76.4% held undergraduate or postgraduate degrees. Respondents were drawn from a range of industries, led by te.......

Discussion

This study examined how the technological characteristics of Agentic AI relate to employees' psychological resources and task-reallocation behavior, and whether these relationships differ across organizational roles. Using a cross-sectional survey of 331 AI-experienced employees and PLS-SEM with mediation and multi-group analysis, the results were consistent with the theorized resource-gain model: AITC was positively associated with cognitive surplus, psychological safety, and AI-human task reallocation; cognitive su.......

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
Prolific	Prolific	https://www.prolific.com/	Online survey pannel provider
Qualtrics	Qualtrics, LLC	https://www.qualtrics.com/	Survey coding
SmartPLS	SmartPLS GmbH	version 4.0.1, https://www.smartpls.com/	PLS-SEM analysis