AI-driven automation could help close staffing gaps
Research firm Enterprise Management Associates (EMA), too, cites the infrastructure complexity that has resulted from hybrid and multi-cloud networks and the need for more advanced automation.
“EMA research finds that hybrid clouds are particularly problematic for network operations teams today. They’re struggling with complexity there. Better automation could help,” said Shamus McGillicuddy, vice president of research at EMA.
Today’s vendors are developing AI and ML algorithms that can detect anomalies, identify the root causes of problems, and predict changes in network utilization, McGillicuddy said.
“By integrating these AI insights with network automation tools, IT organizations can trigger automated workflows that resolve problems, adjust capacity, and make other proactive changes to ensure infrastructure resilience.”
That’s more important than ever, especially given that IT organizations are often understaffed, particularly in network engineering, McGillicuddy said. “They’re also tasked with supporting new initiatives and reducing costs. AI-driven automation can help them close staffing gaps, reduce costs, and be more responsive to changing business need,” McGillicuddy said.
There are dozens of automation tools available today – including Yang, Ansible, Terraform and Puppet, to name just a few – and vendors are in different stages of development in terms of AI-enabled capabilities.