2026 Observability Predictions: What Lies Ahead?

What remains of the 2025 AI hype?

After a year of “AI will fix everything” promises, engineering teams in 2025 hit a wall of reality: AI is a tool, not a magic bullet. 

We’re now seeing a more practical approach: identifying broken workflows and tasks where AI can help and leveraging AI strengths like data analysis at speed and scale to derive meaningful, valuable insights.

Looking ahead, 2026 will reward organizations that combine AI innovation with a practical approach.

Where is AI adoption headed? What steps should organizations take in 2026? And how can teams make the most of AI in observability?

Here are my predictions for the year ahead.

1. AI in dev and observability is post the first hype

Engineering teams understand that AI is not going to do everything anytime soon. But in 2026, we expect to see a steady adoption of AI for coding, testing, debugging and surfacing insights from observability data.

2. AI in observability workflows

Customers will be looking to use AI to solve key observability workflows, like alerts, performance, and cost. No more the 2025 notion of “AI is going to do everything for everyone”. Instead, in 2026 AI is going to help solve the 50% of the mundane work and be supervised by people,

3. Observability intelligence layer

Customers need an observability intelligence layer on top of their fragmented observability tools. In 2026, workflows that cross tools will require AI Agents that imitate humans and work through the different tools, analyzing data, understanding broad context and generating insights.

4. Observability expands to new roles

Organizations understand that AI lowers the barrier to understanding and analyzing data. In 2026, AI will be used to explore data, ask questions, build dashboards, and surface insights – no query language understanding needed. This makes observability more accessible to junior engineers, new team members, and business stakeholders.

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As an observability veteran and more than a decade of leading Logz.io, I can confidently say that the biggest mistake teams can make in 2026 is treating AI as a layer of magic on top of broken processes.

The real gains will come from pairing AI with clear ownership, strong observability fundamentals, and human judgment in the loop. Embedding AI into the right workflows will reduce noise, speed up root cause analysis, control costs, and help make better decisions faster.