AI-Assisted Decision Support for Field Service Operations: A Technical Review

Abstract

Field service organizations face increasing challenges in managing operational complexities spanning scheduling optimization, workforce allocation, equipment maintenance, and cost containment. The integration of artificial intelligence-driven decision support systems within field service management platforms offers transformative potential to address these challenges through predictive analytics, intelligent automation, and real-time optimization capabilities. This article examines how AI-powered technologies enable service organizations to transition from reactive, crisis-driven operations toward proactive, insight-driven service delivery models that anticipate customer needs and prevent equipment failures before they occur. The human-AI collaboration framework represents a fundamental operational paradigm shift, where AI systems handle data-intensive pattern recognition and routine optimization while human operators provide contextual interpretation, empathetic customer engagement, and strategic decision-making. Current implementations demonstrate human-in-the-loop architectures that preserve human judgment and accountability while leveraging AI capabilities for enhanced efficiency and productivity. However, organizations must navigate significant challenges, including algorithmic bias, transparency concerns, workforce resistance, and ethical governance requirements. The future trajectory points toward increasingly sophisticated human-AI partnerships characterized by generative AI copilots, augmented reality integration, and autonomous scheduling capabilities, all underpinned by robust ethical frameworks ensuring fairness, accountability, and transparency in automated decision-making processes.