“Increased Integration of Artificial Intelligence (AI) and Machine Learning (ML) in Infrastructure Management”
• AI and ML are being increasingly embedded into virtual infrastructure management platforms to automate routine tasks, manage workloads, and reduce human dependency in high-scale environments. These technologies help streamline complex processes, improve response times, and ensure consistency in performance management across large digital infrastructures
• These technologies enhance system reliability through real-time analytics, automated diagnostics, and anomaly detection. Such capabilities help organizations detect and resolve issues proactively, significantly reducing infrastructure downtime and improving end-user service continuity
• AI-driven workload balancing enables dynamic allocation of compute, storage, and network resources based on changing demand across cloud ecosystems. This ensures cost efficiency, prevents resource underutilization or overload, and improves system responsiveness for critical applications
• Predictive maintenance powered by ML algorithms is helping enterprises identify performance bottlenecks, hardware degradation, and failure trends before they impact operations. This proactive approach improves infrastructure stability, reduces emergency repairs, and enhances long-term business continuity planning
• For instance, IBM’s Turbonomic platform uses AI to analyze application performance in real time and automatically manage infrastructure resources. It helps enterprises ensure that applications always receive the compute and memory they need, without manual intervention or over-provisioning
