Decentralized Service Operations and Hybrid AI Oversight: A Human-in-the-Loop Decision Framework for Intelligent Service Systems

Abstract

As artificial intelligence and platform-based work models increasingly shape service and industrial operations, organizations face a dual challenge: achieving operational efficiency through automation while preserving trust, quality, and human judgment. This study develops a human-in-the-loop decision framework for intelligent service systems by integrating insights from decentralized service operations and hybrid AI oversight models. Drawing on two complementary empirical domains - remote freelance co-hosting in short-term rental platforms and AI chatbot deployment in customer service - the paper demonstrates how over-centralized agency structures and fully automated decision systems often generate inefficiencies, trust deficits, and quality degradation at the operational level.

Using qualitative synthesis of practitioner evidence, industry cases, sentiment analysis insights, and decision support system (DSS) literature, the study conceptualizes service operations as modular decision units distributed across digital platforms. The framework highlights how AI-driven analytics can support routine, data-intensive tasks, while human oversight remains essential for emotionally complex, ethically sensitive, and context-dependent decisions. By mapping service interactions across the operational lifecycle-task allocation, communication, escalation, recovery, and feedback-the paper illustrates how decentralized human agents and AI systems can be orchestrated within a structured decision-support architecture.

The proposed framework extends traditional DSS research by shifting focus from manufacturing-centric lifecycle decisions to service-oriented, real-time operational governance, emphasizing social and human dimensions often neglected in automated systems. While hospitality-based examples are used as illustrative cases, the framework is designed to be transferable across industries including digital platforms, customer support services, fintech operations, and knowledge-based outsourcing. The study contributes to industrial and information systems research by offering a practical, scalable model for designing intelligent service systems that balance efficiency, accountability, and human-centered decision-making.