Human-Centered Design of AI-Enabled Fulfillment Systems: Integrating Human Factors for Optimal Performance and User Acceptance

Abstract

The integration of artificial intelligence (AI) in fulfillment systems has revolutionized supply chain operations, yet the success of these systems heavily depends on human-centered design principles. This study examines how human factors, including customer expectations, employee adoption, and decision-making trade-offs, can be effectively integrated into AI-enabled fulfillment systems. Through a mixed-methods approach combining surveys (n=547), interviews (n=28), and case studies from 12 organizations, we developed a comprehensive framework for balancing automation with human oversight to prevent service breakdowns. Our findings reveal that successful AI implementation requires a 70:30 automation-to-human ratio for optimal performance, with key success factors including transparent decision-making processes, adaptive interfaces, and continuous feedback loops. The Human-Centered AI Fulfillment Framework (HCAIFF) developed in this study provides practical guidelines for organizations seeking to implement AI while maintaining human agency and service quality. Results indicate that human-centered approaches increase system adoption rates by 43% and reduce service breakdowns by 57% compared to purely automated systems.