Gartner Identifies the Top AI Trends in Infrastructure and IT Operations for 2027
Gartner, Inc., a business and technology insights
company, 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,” said 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 now to improve
operational efficiency with AI, optimize 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:
Top AI Trends in Infrastructure and IT Operations
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, organizations are deploying
AI agents to automate tasks such as incident triage, change validation and
remediation.
By 2030, Gartner predicts that 50% of organizations
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,” said Forest. “Heads of I&O who fail to restructure their
organization 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 organizations 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 centers 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
centers 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 center (NOC) and security operations center (SOC), up from
5% in 2026, realizing an over 25% reduction in total spend on tooling and staff
resources.
Leave A Comment