Gartner has identified the top AI trends in infrastructure and IT operations (I&O) for 2027.
Heads of I&O are increasingly being tasked with improving IT operations while enabling AI initiatives across the business. A Gartner survey of 272 heads of I&O conducted between April and May 2026 found that 58% believe scaling proof of AI concepts to I&O adoption will be an important priority in the next 12 months.
“AI is creating one of the most significant shifts the I&O function has ever faced,” says Jonathan Forest, VP analyst at Gartner. “Rather than trying to address every aspect of AI simultaneously, heads of I&O should focus on a small number of strategic investments that deliver meaningful impact.”
Gartner identified five AI trends that heads of I&O should focus on now to improve operational efficiency with AI, optimise costs, build the hybrid infrastructure needed to support AI inference, address converging operational and cybersecurity risks, and strengthen I&O’s role as the enterprise AI platform orchestrator.
AI agents accelerate autonomous IT operations
Agentic AI in I&O marks a shift to goal-driven software agents that can execute complex IT workflows with minimal human intervention. As operational complexity, scale and velocity increase, organisations are deploying AI agents to automate tasks such as incident triage, change validation and remediation.
By 2030, Gartner predicts that 50% of organisations will use AI agents with minimal human involvement in IT operations, up from nearly 0% in 2026.
“AI agents are driving a shift from reactive, human-in-the-loop operations to proactive, human-on-the-loop operations,” says Forest. “Heads of I&O who fail to restructure their organisation around agentic AI risk higher costs, poor operational agility and performance, and ultimately, irrelevance.”
Agentic AI creates a token economics challenge
The transition from conversational AI to autonomous agentic AI workflows is driving a significant increase in token consumption. Unlike traditional AI interactions, agentic systems perform complex multistep tasks that require substantially more tokens, creating new cost management challenges for I&O teams.
Through 2027, Gartner predicts that enterprises implementing AI agents in IT operations using uncapped variable pricing models tied to token usage will exceed initial cost expectations by more than 50%, compared with organisations using predictable subscription and bundled pricing models.
Gartner recommends a strict “no telemetry, no deployment” policy to eliminate blind spots in AI spending and token usage.
AI inference drives the rise of hybrid AI infrastructure
Most enterprises are consuming existing foundation models through inference rather than training models from scratch. Because enterprise data resides across a mix of public clouds, private data centres and edge environments, AI infrastructure is becoming increasingly hybrid – a shift that is being accelerated by privacy, cost, performance and data sovereignty requirements.
By 2029, Gartner predicts that 80% of large enterprises will operate production AI workloads across at least three infrastructure environments, including public cloud, neoclouds, on-premises data centres and edge locations.
AI converges IT operational risk and cybersecurity risk
As AI becomes increasingly embedded in enterprise infrastructure, operational and cybersecurity risks are becoming more interconnected. Shared AI tooling, telemetry and the growing use of AI agents are driving closer collaboration between I&O and security teams to improve resilience, risk management and operational efficiency.
By 2030, Gartner predicts that 25% of enterprises will merge their network operations centre (NOC) and security operations centre (SOC), up from 5% in 2026, realizsng an over 25% reduction in total spend on tooling and staff resources.
I&O emerges as the enterprise AI platform orchestrator
AI deployments across cloud, data centre and edge environments are increasing the need for a coordinated approach to managing AI infrastructure, governance, security and operations.
Consequently, I&O is evolving from a traditional gatekeeper role to the orchestrator of the enterprise AI platform, enabling the business to deploy and scale AI in a reliable, secure and cost-effective manner.
Through 2030, Gartner predicts that organisations without a strong I&O function driving AI infrastructure design, architecture and support will be over twice as likely to fail in their AI initiatives.
“The high cost of AI infrastructure leaves little room for error, placing I&O at the centre of enterprise AI success,” says Forest. “Heads of I&O must elevate their teams’ capabilities, reduce technical debt, and shift from reactive operations to proactively enabling enterprise AI.”