Exploring the Integration of Artificial Intelligence into Multidisciplinary Engineering Workflows
Keywords:
artificial intelligence, multidisciplinary engineering, engineering workflows, qualitative research, human–AI collaboration, Muscat, thematic analysis, digital engineeringAbstract
This study aimed to explore how artificial intelligence is being integrated into multidisciplinary engineering workflows and how engineers perceive its effects on collaboration, design decision-making, professional roles, and organizational readiness. A qualitative descriptive design was employed using semi-structured interviews with 24 engineering professionals working in multidisciplinary engineering organizations in Muscat, Oman. Participants included civil, mechanical, electrical, software, systems, and project engineers, as well as engineering managers with direct experience of AI-supported tools in design, simulation, project coordination, documentation, quality assurance, or decision support. Participants were recruited purposively to capture variation in engineering discipline, professional experience, and organizational role. Data collection continued until theoretical saturation was reached. Interviews were audio-recorded, transcribed verbatim, anonymized, and analyzed using thematic analysis. NVivo software was used to organize transcripts, code interview data, compare patterns across disciplinary groups, and develop final themes. Four main categories emerged from the analysis: AI as a boundary-spanning coordination mechanism, AI-enabled acceleration of design exploration and technical decision-making, human oversight and trust in AI-generated outputs, and organizational readiness for sustainable AI integration. Participants described AI as useful for connecting fragmented disciplinary knowledge, reducing repetitive documentation work, supporting early-stage design alternatives, improving simulation preparation, and facilitating cross-functional communication. However, they also emphasized concerns regarding data quality, model explainability, accountability for engineering decisions, software interoperability, and uneven AI literacy across teams. The integration of AI into multidisciplinary engineering workflows is not merely a technical transition but a socio-technical transformation that reshapes collaboration, professional judgment, and organizational learning. Sustainable AI adoption requires high-quality engineering data, transparent governance, domain-specific validation, continuous professional training, and human-centered workflow redesign.
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