AIOps emerged as a response to modern IT systems’ overwhelming volume and complexity of data. These tools were needed to help keep up with the demands of large-scale distributed systems.
AIOps offered a promising way to automate routine tasks, detect anomalies, and provide proactive insights, but its limitations have become increasingly apparent. Many AIOps solutions rely heavily on machine learning algorithms that require extensive training data and can be prone to overfitting. Additionally, the black-box nature of some AI models can make it difficult to understand how they arrive at their conclusions, hindering troubleshooting efforts.
