A Conceptual Model for Human-Centric AI Adoption in Manufacturing Projects: Integrating Socio-Technical Systems Theory and Technology Acceptance Model

The growing implementation of Artificial Intelligence (AI) into manufacturing settings is transforming projectmanagement practices but uptake is still somewhat uneven and theoretically disjointed. Current studies focus on AIimplementation mainly on a technical systems capture or an individual-level technology acceptance model, which leadsto a constrained comprehension of the interplay between organisational, technological, and human aspects in a projectbased environment. In response to this gap, this paper presents a new conceptual model, which combines the SocioTechnical Systems (STS) theory with the Technology Acceptance Model (TAM) in explaining the use of Human-CentredAI (HCAI) in manufacturing projects. This model proposes a multi-level model whereby social subsystem variables(organisational readiness, leadership support, and team capability) and technical subsystem variables (transparency,compatibility, and data infrastructure quality) affect cognitive mediators, which include, perceived usefulness, perceivedease of use, and trust in AI, which, in turn, lead to adoption behaviour and project performance outcomes. The contextualmoderators suggested to influence the adoption performance relationship are project complexity and formal integrationmechanisms. The research has added to the theory by closing a gap between socio-technical alignment on a macro-leveland acceptance mechanisms at a micro-level and expanded TAM by expressly considering the principles of trust andhuman-centric AI. In practical sense, the framework provides an organised diagnostic and implementation tool toorganisations and project management offices intending to match the technological capabilities with human andorganisational preparedness. The paper highlights the fact that sustainable AI-based performance gains require humancentred design, which is interwoven into consistent socio-technical systems by focusing on transparency, augmentation,and joint optimisation.