This paper explores the applicability of Generative AI (GenAI), particularly Large Language Models (LLMs), in modern call center environments. It investigates how GenAI can support key tasks such as speech-to-text transcription, call summarization, sentiment analysis, and process mining. Based on a systematic literature review and a practical proof-of-concept (PoC), the study identifies key challenges such as hallucinations, bias, and compliance risks, while also highlighting the potential of tailored GenAI solutions. The PoC demonstrates how dialect-specific models like Google’s Gemini 1.5 Pro and tools such as sentiment analysis, Named Entity Recognition (NER), and interactive chatbots can enhance operational efficiency and service quality in call centers. Comparison factors such as accuracy, customization, cost, compliance, and task applicability are used to evaluate both theoretical and practical findings. The results emphasize the value of domain-specific, modular GenAI systems for transforming customer service processes and provide a foundation for future research into scalable, trustworthy AI integration.