Artificial intelligence has been driving new industrial solutions for challenging problems in recent years, with many companies leveraging AI to enhance business processes and products. Automated anomaly detection emerges as one of the top priorities in AI adoption, sought after by numerous small to large-scale enterprises. Despite the huge benefit brought by adopting anomaly detection, operationalizing them remains a formidable challenge due to inherent issues in dynamic datasets, diverse business contexts, and the dynamic interplay between human expertise and AI guidance in the decision-making process. Extending beyond domain-specific applications like software log analytics, where anomaly detection has perhaps garnered the most interest in software engineering, this work delves into the more holistic view on the complexities of adopting effective anomaly detection models from requirement engineering perspective. For example, validating anomalies requires human-in-the-loop, though working with human experts is challenging due to uncertain requirements about how to elicit valuable feedback from them. In this study, we provide an experience report on the challenges associated with operationalizing anomaly detection and emphasize the need to address these challenges for a more universally applicable anomaly detection approach.