The use of artificial intelligence to manage cloud operations has significantly changed how businesses design and oversee their IT systems. The consensus is that using AI for automation provides greater scalability, reliability, and consistency, as well as reduced manual mistakes and resolves common issues faster. However, a closer look reveals that greater dependence on AI could introduce vulnerabilities in cloud setups that are often overlooked.
Based on my experience with businesses adopting cloud services and technologies, I’ve observed a consistent pattern emerging. Cloud operation experts are increasingly relying on AI-powered automation tools for their tasks and processes. Although these technologies effectively streamline operations, organizations might be delegating too much authority to machines, potentially overlooking knowledge and risking essential operational checks.
Issues with oversight and budgets
One of the benefits often highlighted in AI-driven cloud operations is the concept of “set and forget.” It is now common to enable AI-driven processes for tasks such as resource allocation and anomaly detection, trusting these mechanisms to manage systems smoothly without constant supervision. However, being hands-off can create unintended problems with awareness and vigilance, as automated systems heavily depend on the quality of their training data and algorithms, as well as understanding the environments in which they operate. If an AI misses important contexts during analysis, it could easily overlook issues within the system.