The increasing complexity of contemporary business data poses substantial challenges to classical machine learning methods, particularly in classification problems involving nonlinear interactions among multiple variables. This research seminar presents an innovative approach based on Quantum Machine Learning, with a specific focus on quantum kernel methods and their application to business decision-making. Following a conceptual introduction to quantum computing tailored to scholars in economics and management, the seminar explains how qubits enable data representation in exponentially large feature spaces through quantum feature maps. These mappings facilitate the identification of complex patterns that often remain undetected by classical kernels. Within this framework, the use of Quantum Support Vector Machines (Q-SVMs) is examined as an advanced classification tool. Empirically, the seminar reports results from several business-relevant case studies, including customer churn prediction, Customer Lifetime Value (CLV) classification, financial risk assessment, and supply chain risk evaluation. The findings indicate consistent improvements over classical models, with recall gains of up to 18% and an AUC of 0.83, highlighting a superior ability to capture critical patterns in complex datasets. Finally, the seminar discusses the current limitations of Noisy Intermediate-Scale Quantum (NISQ) technology and proposes a practical roadmap for its adoption in business environments. In particular, it is shown that the use of quantum simulators and open-access platforms enables the development of pilot projects without requiring dedicated quantum hardware. The seminar concludes that quantum methods already provide measurable competitive advantages in specific classification tasks and that organizations investing early in quantum literacy will be better positioned to benefit from future technological developments.