This thesis explores how generative AI can be implemented as first-line support in a small or medium-sized enterprise (SME), focusing on practical strategies for adapting large language models (LLMs) to company-specific needs. Conducted in collaboration with BM System, the study evaluates three adaptation methods: fine-tuning, Retrieval-Augmented Generation (RAG), and prompt engineering, using open-source models, with benchmarking against the closed- source GPT-4o-mini.

Through a combination of automatic metrics (ROUGE), LLM-based assessments, and manual user surveys, the study finds that RAG, especially when paired with prompt engineering, pro- vides the most scalable and effective solution for SMEs with limited technical resources. Fine- tuning enables domain adaptation but is constrained by data quality and hardware demands, while closed-source models outperform open-source alternatives but raise issues regarding cost, privacy, and control.

The results lead to three implementation strategies, tailored to different organizational require- ments. Importantly, the study emphasizes that creating value with AI in customer support is not only a technical challenge but also requires a critical understanding of the technology’s role and limitations, acknowledging that human expertise remains central to effective support. With a care- fully chosen approach and critical reflection, generative AI can serve as a valuable complement to customer support in SMEs.