A new year is around the corner, and we can't resist the temptation to gaze into our crystal ball.

2025 saw a dramatic shift in appetite for AI solutions in Site Reliability Engineering (often abbreviated as AI SRE) and, more broadly, in production operations. From being viewed by practitioners with skepticism at the beginning of the year, this once-embryonic market category has evolved to the point where Gartner analysts advised attendees at the Gartner IT Infrastructure, Operations & Cloud Strategies Conference in December 2025 to "select an AI SRE tool and begin trialing it in IT Operations against targeted use cases." In fact, AI-driven root cause analysis is the first real proof point for AI in production operations. Faster detection of root cause and clearer "why" materially reduce MTTR, improving both productivity and reliability.

So what does 2026 hold for this space? Here's what we predict.

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1. Forward feedback loops prepare systems for change before impact

Change remains the single greatest source of reliability risk in production environments. Configuration updates, feature releases, scaling events driven by business demand, and infrastructure migrations are inevitable but all of them also increase the probability of failure.

In 2026, AI SRE platforms will be increasingly leveraged before change is deployed, using historical incident data, current system context, and underlying knowledge graphs to reason about expected impact and potential blast radius. Instead of discovering problems after rollout, teams will use AI to explore "what-if" scenarios and prepare mitigation strategies in advance. Reliability engineering shifts decisively from reactive correction to proactive readiness.

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2. Operational feedback loops unlock step-function efficiency gains

As AI dramatically shortens investigation cycles, organizations will redirect that leverage toward operational efficiency rather than simply faster firefighting. AI-driven feedback loops will reduce ticket handoffs, collapse unnecessary organizational layers, enable more effective self-service access to operational intelligence, and improve alert hygiene by learning which signals truly matter.

Teams will be empowered to act earlier in the operational lifecycle by resolving issues without escalation. In 2026, many enterprises will realize that the most immediate ROI from AI SRE comes not just from lower MTTR, but from fewer interruptions, reduced toil, and materially higher throughput per engineer.

We also anticipate customers using AI in SRE as a retrospective feedback loop that reconnects operations with development, informing architectural decisions, backlog prioritization, and reliability investments. Operations data becomes a first-class input into engineering strategy, narrowing the gap between how systems are built and how they behave in production.

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3. Robust AI SRE architectures unlock entirely new use cases

The next wave of AI SRE value in 2026 will be driven less by reasoning models alone and more by the underlying system architecture of these solutions. State-of-the-art AI SRE platforms will be multimodal, combining multiple techniques to reason about known knowns, known unknowns, and unknown unknowns.

A strong underlying architecture of the underlying AI SRE platform will enable new use cases such as training junior engineers, dramatically improve L0 and L1 support effectiveness, and democratize access to root cause analysis in DevOps, platform engineering teams, and IT Operations teams. These capabilities will also expose the limitations of systems based exclusively on RAG (retrieval-augmented generation), pushing enterprises to seek solutions that can efficiently incorporate existing data sources-including tribal knowledge-while reasoning dynamically in incomplete or evolving environments.

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4. Production operations roles evolve toward augmentation and oversight

The early AI narrative in SRE framed the conversation as humans versus machines, but by the second half of 2025 the industry increasingly accepted AI teammates as a path towards augmentation. In 2026, this transition becomes operational reality.

Existing roles in SRE, IT operations, and platform engineering will require significant upskilling to work effectively alongside AI systems. New responsibilities for reliability architects will emerge around supervising AI output, validating correctness, defining governance boundaries, determining what tasks can be delegated to AI, and deciding which discoveries should flow back to development. Organizations that rely solely on labor arbitrage will struggle, while those that invest in effective human-AI collaboration will achieve disproportionately higher reliability.

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5. AI SRE emerges as an abstraction layer for enterprise operations

For decades, operations teams have struggled with "swivel-chair" workflows-manually stitching together data across an ever-growing set of tools and dashboards during incident response. We even coined a term for it: click operations. One of the earliest use cases that resonated with customers in 2025 was AI SRE's ability to perform this stitching on behalf of human operators, immediately reducing toil.

In 2026, AI SRE is poised to become an abstraction layer for enterprise operations, cutting across observability tools, collaboration platforms, knowledge bases, systems of record, ticketing systems, cloud platforms, CI/CD pipelines, and even infrastructure components. This abstraction gives enterprises the flexibility to adopt best-in-class tools without the cost and risk of aggressive tool consolidation or loss of vendor leverage.

Natural language further accelerates this shift, allowing AI to operate across domain-specific languages (DSL) on behalf of humans. Much as coding agents do for developers today, AI SRE frees operators from needing deep expertise in every underlying system's DSL.

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Final Thoughts

AI SRE is no longer about novelty or experimentation. In 2026 and beyond, it will become an integral part of production operations by becoming deeply embedded in how modern enterprises design, operate, and evolve reliable systems. The winners will not be those with the flashiest models, but those who build systems that reason well, learn continuously from the people who run production, and amplify enterprise capability by orders of magnitude.

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This article was originally published on https://vmblog.com/archive/2025/12/29/2026-predictions-ai-in-site-reliability-engineering.aspx .

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