Generative Artificial Intelligence to Support Frontline Employees
DOI:
https://doi.org/10.63282/3050-922X.IJERET-V7I1P102Keywords:
Generative Artificial Intelligence, Frontline Workers, Large Language Models, Decision Support Systems, Human–AI Collaboration, Workplace Automation, Digital Transformation, Ethical AI, Productivity EnhancementAbstract
Background: Frontline workers, such as healthcare staff, emergency responders, social workers, retailers, logistics people, and agents of the public services work in high-pressure settings with time limits, cognitive overload, and ever-changing information. Irrespective of the digital transformation, most frontline positions are still not sufficiently served by intelligent decision-support systems which can adjust to the real time. The recent advances in Generative Artificial Intelligence (GenAI), especially large language models and multimodal generative systems, offer new possibilities to enhance frontline work with the help of contextual reasoning and automation as well as human-oriented assistance.
Objective: This study examines the role of Generative AI in enhancing productivity, decision-making accuracy, and operational resilience for frontline workers. The primary objective is to analyze how GenAI-driven tools can support task execution, reduce cognitive burden, and improve service delivery across frontline domains while maintaining ethical, organizational, and human-centered considerations.
Methodological Approach: The study adopts a qualitative–conceptual research design supported by an integrative literature review of recent peer-reviewed studies (2019–2025) indexed in Google Scholar. The analysis synthesizes existing implementations of Generative AI in frontline contexts, including conversational AI assistants, real-time decision support systems, automated documentation tools, and adaptive training platforms. Key dimensions evaluated include system architecture, deployment models, human–AI interaction mechanisms, and measurable performance outcomes.
Key Findings: The findings indicate that Generative AI significantly enhances frontline efficiency by enabling real-time knowledge retrieval, automated reporting, natural language interaction, and context-aware recommendations. Across sectors, GenAI adoption is associated with reductions in task completion time, improved compliance with operational protocols, and increased worker confidence. However, challenges remain regarding data privacy, model hallucination risks, bias propagation, and organizational readiness for AI integration.
Contribution and Significance: This paper contributes a structured and cross-sectoral perspective on Generative AI for frontline workers, bridging technical capabilities with practical deployment considerations. By synthesizing current evidence and identifying design and governance gaps, the study provides a foundation for future empirical research and policy development. The findings are relevant to researchers, system designers, policymakers, and organizations seeking scalable and responsible GenAI adoption in frontline environments.
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