Autonomous IT is likely to arrive through gradual adoption rather than a single disruptive “breakthrough” moment, according to LogicMonitor’s General Manager of AI, Karthik SJ.
In commentary provided by LogicMonitor, Karthik SJ said enterprise technology typically evolves incrementally, arguing that autonomous IT will emerge as organisations build confidence in delegating more responsibility to AI across IT operations.
“The industry tends to focus on breakthrough moments; however, that’s rarely how enterprise technology evolves,” said Karthik SJ, General Manager of AI at LogicMonitor. “Autonomous IT will emerge as organisations build confidence in letting AI take on greater responsibility. This starts by identifying anomalies, then recommending actions before safely executing routine tasks. By the time most organisations realise autonomous IT has arrived, it will already be part of the way they operate.”
He compared the trajectory to earlier shifts such as cloud computing and virtualisation, which became mainstream over years of adoption rather than rapid change. The same pattern is likely for autonomous IT, he said.
The company pointed to its 2026 Observability & AI Outlook for IT Leaders, which it says indicates organisations are continuing to invest in observability despite cost pressures, including efforts to consolidate monitoring tools and unify operational data to support AI-driven decision-making. LogicMonitor said priorities are also shifting from experimentation toward outcomes such as predictive analytics and automated remediation.
“Organisations today have more visibility into their systems than ever before; however, visibility alone doesn’t resolve incidents,” said Karthik SJ. “The real value of AI in observability comes when it helps teams move from understanding what is happening to acting on those insights safely and in real time.”
Karthik SJ described a shift in IT operations from humans reacting to alerts toward AI correlating telemetry, identifying root causes and recommending actions more quickly. He said early stages resemble an advisory model, with humans retaining control over whether recommendations are actioned.
“As systems mature, AI begins to act more like an advisor,” said Karthik SJ. “It not only detects problems; it also recommends potential solutions based on historical incidents and patterns. Human operators remain firmly in control, approving or rejecting these recommendations while benefiting from faster diagnosis and richer context. Organisations may then let AI automate low-risk actions once it has demonstrated reliability.”
According to Karthik SJ, trust and governance will be key constraints on autonomy in IT operations, rather than the availability of data or capability.
“The barrier is not a lack of data or technological capability; it is trust,” said Karthik SJ. “For AI to move from analysing systems to actively operating them, IT leaders need confidence that automated decisions will be safe, transparent, and accountable. Trust mechanisms transform AI from a helpful assistant into a trusted operational capability.”
LogicMonitor’s commentary cited figures it attributed to its research, stating that four per cent of organisations have fully operationalised AI across IT operations, while almost half remain in pilot or experimentation. It argued the issue is less about whether the technology works and more about governance, operational maturity and a unified data foundation.
Karthik SJ said autonomous IT would likely expand through a series of delegated tasks—such as suppressing alert noise, restarting infrastructure, and scaling resources ahead of demand—where each step may appear minor but cumulatively changes IT operations.
Rather than replacing engineers, he said, autonomous IT would shift roles toward defining policies, validating outcomes, and improving the systems AI operates, with success increasingly measured by fewer customer-facing incidents.
Reference: https://www.logicmonitor.com/blog/observability-ai-trends-2026
