Abstract:

Small and medium-sized enterprises (SMEs) remain structurally constrained by limited resources, insufficient digital maturity, and a strong dependence on tacit knowledge embedded in individual
employees. These constraints become particularly visible in quotation and offer creation, which is often manual, weakly structured, and characterized by information gaps between customer requirements and internal assessment capabilities. This study explores how generative artificial intelligence (GenAI) can support the redesign of SME quotation processes through modularity principles to improve transparency, flexibility, and cognitive ergonomics during early requirements work. Following Design Science Research Methodology, the study applies the stages of problem identification, objective specification, and artifact development. The resulting artifact is a modular quotation framework operationalized via a dual-role GenAI assistant: (1) a customer-facing chatbot that elicits and structures requirements through natural-language dialogue and (2) an internal analytical assistant that decomposes requests into reusable modules and supports initial feasibility and estimation work under human oversight. The artifact was iteratively evaluated through structured self-testing and practitioner feedback from roles in software development and
IT project management. Feedback indicates improved requirement clarity and more systematic decomposition of complex requests, while also highlighting limitations related to file processing, budget realism, and interaction guardrails. Overall, the paper contributes a replicable blueprint for AI-enabled modular quotation support in SMEs and derives design implications for human-AI collaboration in software and systems engineering contexts.