2026/01/01 by Tatsunori Hara, Wuyi Chen, Jun Ota
Business, Management and Accounting · Computer Science · #Service and Product Innovation #Advanced Software Engineering Methodologies #Software System Performance and Reliability
paper · doi:10.1016/j.cirp.2026.04.094
Product–service system design methodologies face adoption challenges because of their cross-domain procedural complexity. Large language model (LLM)-based agents can address this. We developed Service LAD (LLM-Agentic Design), a method-enforcing AI framework embedding methodological knowledge into autonomous tools. Using an expert workshop (N = 4) with a crossover design, we compared it with method-flexible AI for conformance and human factors. The method-enforcing approach achieved higher conformance (99 vs. 82% step compliance; 99 vs. 36% output completeness; 87 vs. 51% traceability), whereas practitioner acceptance was moderated by domain familiarity. The conformance–acceptance tradeoff is conditional, providing evidence for methodology-embedded design support.