Human-Centric, AI-Enabled Concurrent Engineering: Towards a cross-domain framework

The convergence of Artificial Intelligence (AI) and Concurrent Engineering (CE) has begun to reshape how industrial systems are designed, validated, and operated. We argue that the next, and arguably more consequential, step is to embed this convergence inside a coherent Industrial Information Integration (III) substrate that is explicitly human-centric and that generalises beyond the classical manufacturing core. This paper develops such a framework. We propose the H-AICE architecture (Human-centric AI-enabled Concurrent Engineering), which couples (i) a five-dimensional taxonomy of AI interventions in CE, (ii) a time-dependent overall-risk model Roverall(t) that integrates internal and external risk along the design lifecycle, and (iii) an integration layer based on shared ontologies, knowledge graphs, and digital twins. The framework is then instantiated and critically discussed in six application scenarios that exhibit very different data regimes, regulatory contexts, and human stakes: precision agriculture (Agriculture 5.0), biotechnology and life sciences, climate-aware industrial sustainability, sustainable forestry (Forestry 5.0), medical device development, and livestock breeding. We show that the same III primitives—data quality, semantic interoperability, explainable models, and human-in-the-loop oversight—recur across all six scenarios, and that the principal failure modes are organisational rather than algorithmic. We conclude with a research agenda for human-centric, AI-enabled CE that emphasises explainability, governance, and sector-specific certification, and that positions III as a load-bearing rather than a cosmetic discipline.