Intelligent organizational engineering driven by human-AI collaboration and explainable Al to increase productivity

Abstract

The rapid adoption of artificial intelligence (AI) in organizational engineering promises significant productivity gains, yet evidence shows a persistent “AI productivity paradox” in which task-level efficiencies fail to translate into measurable economic benefits. This paper examines how explainable AI (XAI) and human-AI collaboration (HAIC) can address this gap by aligning algorithmic capabilities with human expertise, trust, and organizational design. Drawing on recent empirical studies, we analyse the structural, cognitive, and socio-technical barriers that limit AI‟s value realization, including inadequate integration, overreliance on automation, and inherent system opacity. We propose that XAI, embedded as both a technical and organizational capability, enables transparency, accountability, and adaptive collaboration across diverse stakeholder groups. Using a simulated organizational engineering scenario, we show how XAI-informed HAIC can enhance decision quality, redistribute cognitive workload, and foster iterative learning. The analysis underscore that AI‟s real productivity potential lies not in automation alone, but in deliberate, human-centred integration that treats AI as a collaborative partner within resilient socio-technical systems driving intelligent organizational engineering to increase productivity.