Adaptive AI in Concurrent Engineering: A Paradigm Shift in Design and Integration

This paper explores the potential of advanced technologies to redefine the way we engage with tools in the domain of Concurrent Engineering (CE). CE represents a paradigm shift from traditional sequential design processes, enabling multidisciplinary teams to work simultaneously on various project aspects. This collaborative approach minimizes delays, enhances integration, and fosters innovation in design methodologies. Central to this work is the transition from the conventional model of “humans adapting to technology” to one where “technology adapts to humans”. This paper focuses on the application of Large Language Models (LLMs) within Concurrent Engineering environments, proposing a conceptual approach to investigate their potential capability to process voice recordings and textual documentation to generate structured and reliable knowledge bases for end-user applications. This work investigates potential approaches for the creation of a versatile framework that, together with specialized applications built on top of it, can effectively manage and store highly unstructured information within a graph-based knowledge representation. Such applications range from requirements elicitation, validation, interpretation, to the integration of autonomously extracted knowledge into Model-Based Systems Engineering (MBSE) tools. This structured repository is thought to be subsequently utilized by the same applications to support advanced reasoning tasks, enhancing their contextual understanding and functionality across the project lifecycle. This research examines the practical implications of integrating LLMs and outlines future research directions to maximize their effectiveness in CE environments. By exploring how innovative tools can alleviate cognitive overload, this study aims to propose a viable solution for enhancing engineering practices while simultaneously developing a generalizable framework applicable to other engineering domains. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.