“Without an AI operating model, you cannot survive in this world”: DevOps at Think 2026

At this year’s IBM Think conference held in Boston, DevOps decision makers came together to confront the stark reality of IT in 2026: AI isn’t just one part of their discipline, it is the discipline.Over the past decade, DevOps has evolved to include ever more granular, probing observability practices. The wealth and volume of data those tools collect requires powerful automated solutions — both for making sense of it all, and for acting on the insights the data provide.But while AI-powered observability tools accelerate that process, they also amplify complexity, cost and operational risk.At Think, IBM’s DevOps leaders introduced new tools for cutting through the morass and coordinating action across increasingly complex environments.

As IBM CEO Arvind Krishna said in Tuesday’s keynote speech, “the models don’t really matter unless the foundation is correct” — and the evolving, AI-powered DevOps pipeline is meant to keep that IT foundation strong. Some key takeaways:Hybrid complexity is the default—and AI is accelerating it. An organization’s  environments now span applications, infrastructure, and networks across cloud, on‑prem, and Z systems, and AI workloads intensify scale, cost, and operational risk.As this complexity becomes the default for organizations, consistent standards and policy‑driven automation are critical for keeping systems reliable. For a deeper look at how teams can build this foundation,see our white paper on standardization and policy‑driven automation.AI is becoming the unifying layer for operations. Rather than creating more silos, AI‑driven operations are emerging as the connective tissue that binds hybrid environments.Observability is moving from visibility to accountability. Teams need real‑time, AI‑assisted insights that don’t just surface issues, but explain impact, causality, and where to act.Application telemetry and infrastructure topology must be unified. Continuously reconciled environments that combine app and infrastructure data give practitioners clearer, end‑to‑end visibility and faster root‑cause analysis.Operational scale now depends on coordinated, AI‑driven automation. As complexity grows, AI‑driven operations are becoming essential to reduce toil, optimize performance and cost, and enable teams to scale reliably.“One billion new applications in the next five years will come into enterprises because of generative AI,” Dinesh Nirmal, Senior Vice President for Software at IBM, said during a Tuesday keynote address, adding that “Every application that is containerized is going to have hundreds, if not thousands, of microservices” governed by possibly billions of agents.“How are you going to have a control plane to manage those agents? What is the communication that’s going to happen between those agents, who has access to those agents? What kind of data is being accessed by those agents?”

That same day, IBM automation leaders discussed a new model for observing and controlling agentic IT systems, inspired by one simple truth: The scale and speed of signals have outpaced human response. TheIBM Concert platformprovides a shared operational layer across applications, infrastructure, networks and security, turning an ocean of data into a continuously updated source of truth for applications, infrastructure, and their relationships.Concert brings together a suite of IBM capabilities including IBM Instana, Turbonomic and CloudPak for AIOps, among many others, working with — not in place of — existing tools to provide a 360-degree view of the dizzyingly complex AI-powered business environment.