COGNITIVE LOAD MANAGEMENT IN CONTACT CENTERS: REDESIGNING AGENT EXPERIENCES FOR THE AGE OF AI-AUGMENTED WORK

Abstract

Automation and artificial intelligence copilots have transformed operational efficiency in contact centers while simultaneously intensifying mental demands on human agents. Modern agents navigate multiple applications, process constant notifications, and respond to compliance prompts, creating conditions where cognitive overload reduces accuracy and accelerates burnout. This article proposes a Cognitive Load Management Framework that senses, predicts, and mitigates cognitive strain through real-time workload analytics, adaptive user interface simplification, and predictive task orchestration. Drawing on Cognitive Load Theory, human-factors engineering, and workforce analytics literature, the framework aims to balance productivity imperatives with employee well-being. Evidence from industry sources and academic literature indicates that framework implementation can substantially raise accuracy, considerably reduce stress indices, and significantly decrease turnover. The framework consists of five related elements: Load Sensors that assess cognitive strain through interaction telemetry, a Cognitive Orchestrator that establishes priorities for task presentation, an Adaptive User Interface Layer that modifies complexity dynamically, and a Feedback Engine that provides micro-breaks and moments of reflection. The framework also reimagines artificial intelligence as a cognitive assistant, not just an automation method, positioning attention capacity as the primary currency of performance in human-AI working environments. The transformation positions cognitive ergonomics as essential infrastructure for sustainable service delivery.