DevOps 2026 predictions: what the experts really think

The first half of the year will see teams experimenting. Picture wiring up something that watches your Kubernetes events, spots the actual buried errors, and provides sample remediations. Instead of scrolling through hundreds of lines to find that pesky ImagePullBackOff, AI identifies whether it's a typo in your YAML deployment or a missing secret.

But the second half will be different. AI in DevOps will stop being an optional experiment and become an actual organisational initiative. It will become part of how individuals and teams improve and deliver business value.

As 2027 rolls around, AI won't be "early" any more. By then, the gaps between teams that treated 2026 as their launch year and those who sat on the bench will start showing.

What this means for you: Treat 2026 as your window of opportunity. Start experimenting now while the learning curve is forgiving. By mid-year, move your successful experiments into real initiatives. The teams that wait until 2027 will be playing catch-up.

In 2026, that model starts to collapse under the weight of scale, compliance demands, and financial accountability. The shift I expect is from hand-crafted IaC to intent-driven infrastructure.

What this means for you: Instead of focusing on how every resource is built, teams will focus on what outcomes must be true: secure by default, compliant by default, cost-aware by default, and continuously verifiable. The platform then enforces those outcomes over time, not just at deploy time.

What this means for you: Start identifying tasks in your pipeline that could benefit from autonomous execution. Start building documentation that AI can use as context for DevOps tasks and identify a set of tools you can provide to AI agents to enable them to fix simple DevOps issues. Have a plan for expanding both the available context and the guardrails within these tools to gradually allow AI agents to take on more DevOps tasks. Ensure that logging and auditing of all actions is in place so that we can continue learning from incidents and their fixes. Begin experimenting with AI agents in controlled environments now, so you're ready when they become mainstream.

The key is implementing and inspecting SDLC controls within pipelines. Differential performance comes from expressing your organisation's policies and controls as code, then implementing them within CI/CD pipelines with simple pass/fail/escalate rules. But implementation is only half the battle. Organisations also need to securely retain linked evidence of these control tests together with their context, rather than just storing CI logs that require manual interpretation later.

When done correctly, this stored evidence of control inspection can significantly reduce the joint burden on both audit and application development teams. These actions will also mature your risk and compliance posture from a point-in-time assessment to a continuous process.

What this means for you: Start working with your GRC team now to identify which controls can be codified and automated in your pipelines. Build the evidence retention systems that will make audits painless and transform compliance from a periodic disruption into a continuous, automated advantage.

I anticipate that we will see the first public major AI-related incident that will severely test, and likely reveal significant weaknesses in, at least one company's resiliency and business continuity plans. This incident could occur suddenly, such as a model mistakenly dropping a mission-critical production database without proper backups, or it could be a more gradual process, where flawed instructions corrupt or modify valid data with incorrect information, leading to a loss of customer trust upon discovery.

What this means for you: Determine a reasonable set of guardrails for models to use. As part of your implementation of new technologies and ways of working, it's important to ensure that there is an audit trail of actions happening, especially those not being supervised, and similarly, a reversal process to ensure that a slip-up by something less deterministic than what software developers are used to does not become an avalanche.