This contribution deals with a comparison of two traditional approaches and one AI-based data mining tool to collect and interpret data for prospect generation. Traditional prospect generation methods like manual web search or using purchased data from external providers may create high costs and efforts and are subject to failures and waste coverage through outdated and untargeted data. In contrast, AI-based methods claim to provide better results at lower costs. Based on a real case, the authors compare effects of these three prospect generation methods. AI-based data mining tools compensate for some weaknesses of other methods, especially because they do not need pre-defined selection criteria which might bias the results. In addition, they involve less effort from the researcher. However, the results in generating concrete prospects may be still weaker than with traditional methods if web crawling activities are influenced by underlying databases. For academic research in the field of prospect generation, this study provides a fact-based comparison of approaches. Implications for businesses include the advice to combine methods rather than to rely on a single approach. The time available for research and the complexity of the target market have an influence on the selection of the prospect generation approach.