Zavádění umělé inteligence do zákaznických operací v malých a středních podnicích: Výzvy a příležitosti

This bachelor’s thesis explores the implementation and impact of Artificial Intelligence (AI) in Small and Medium Enterprises (SMEs) to enhance customer operations, overcome structural bottlenecks, and achieve operational scalability. Despite the growing democratization of technology through low-code platforms and Large Language Models (LLMs), SMEs frequently encounter integration barriers, including technical debt, administrative friction, and the absence of foundational digital infrastructure. Employing a qualitative multiple-case study methodology, this research analyzes three distinct businesses: a B2B service company in the United States, alongside a premium aesthetic education clinic and a beauty sector enterprise in the European Union. The study examines the practical integration of AI-driven solutions, such as automated Customer Relationship Management (CRM) workflows and Retrieval-Augmented Generation (RAG) systems designed to mitigate AI hallucinations. The findings demonstrate that tailored AI implementations successfully bridge customer engagement gaps, automate repetitive customer-facing processes, and facilitate direct revenue capture. Furthermore, based on a cross-case analysis, the thesis identifies a critical market demand for long-term technological partnerships. Consequently, it proposes a novel business model centered around the "Solution Architect" role, offering a strategic roadmap for professional consultancies to transition from one-off implementations to scalable, recurring revenue streams.