When it comes to IT monitoring, many picture graphs and logs. But over the last couple of years, this field has undergone a major shift – and it is not just about new tools, but how specialists approach the work itself.
Elastic – the company behind data search and analytics solutions – conducted a survey among observability leaders. The results show that in 2026, two directions stand out: generative AI (GenAI) and the OpenTelemetry standard.
What is Observability and Why It Is Important for IT Monitoring
What is observability anyway, and why does it matter?
Observability is the ability to understand a system's internal state based on its external data, even when it consists of many complex components. Simply put, it is not just monitoring in the vein of «up or down», but the ability to figure out why something went wrong, even if the problem is not obvious.
In the past, specialists collected logs, metrics, and traces (execution paths) using different tools that were often incompatible with one another. This created problems: data was stored in different silos, it was hard to correlate, and implementing a new solution could require rewriting parts of the code.
OpenTelemetry as a Standard for System Performance Data Collection
OpenTelemetry: An attempt to bring order to the chaos
OpenTelemetry is an open standard for collecting telemetry (data about system performance). The idea is simple: instead of every vendor coming up with their own format, the industry has agreed on a common approach. This means a developer can set up data collection once and then send it to various monitoring systems without any retooling.
The survey showed that in 2026, OpenTelemetry has become a top priority. This makes sense: standardization makes life easier, especially as architectures become increasingly distributed.
For engineers, this means less time spent on tool integration and more time spent analyzing the actual behavior of applications. Furthermore, moving to a single standard reduces «vendor lock-in»: if you need to switch monitoring platforms, you will not have to rewrite your code from scratch.
Role of Generative AI in Observability and Incident Diagnostics
GenAI enters monitoring – but why?
The second major trend is the use of generative AI in observability tasks. This is not about replacing specialists with neural networks, but about helping them cope with the growing volume of data and speeding up incident diagnostics.
Imagine a system goes down in the middle of the night; the on-call engineer opens the logs and sees thousands of lines. They need to quickly understand exactly where the error occurred. GenAI can help by analyzing anomalies, suggesting hypotheses for the root cause, drafting search queries, or even pointing out which metrics to check first.
The survey indicates that companies are actively experimenting with such tools. For now, it is more of a supporting role – AI does not make decisions on its own, but it significantly accelerates human performance. This is critical in an environment where systems are getting more complex, while headcounts do not always grow in proportion to the load.
Impact of Standardization and AI on the Future of Observability
What this means for the industry
Looking at the bigger picture, these two trends reflect a general shift in the approach to system operations.
OpenTelemetry is about standardization and long-term sustainability. Instead of choosing a tool and being tied to it forever, companies gain the flexibility to adapt to changes. This is especially relevant for organizations working with cloud platforms and microservices, where the number of «moving parts» can reach the hundreds.
GenAI, in turn, is responsible for efficiency. Modern systems generate colossal volumes of data, and parsing them manually is becoming increasingly difficult. AI does not solve every problem, but it takes over the drudgery: preliminary analysis, identifying patterns, and assisting with complex queries.
It is important to understand that neither direction is a «silver bullet». OpenTelemetry requires investment at the implementation stage, and not all legacy tools support it equally well. GenAI in observability is still a relatively young field, and engineers are only just finding their footing to see where it is truly useful and where it creates unnecessary complexity.
Challenges and Unresolved Questions in Modern Observability Adoption
What remains unclear
The survey captured the trends but left several practical questions open. For example, how are companies managing the migration to OpenTelemetry if they already have established processes? What specific tasks does GenAI handle best, and in which cases does it only complicate the situation?
Furthermore, there is the question of how technology evolves. Standards may update, and AI tools can both improve and reveal their limitations in real-world conditions. Right now, the industry is in a phase of active experimentation.
In any case, the Elastic data confirms that observability is ceasing to be a purely infrastructural task. It is a field where standardization, automation, and intelligent data processing intersect – and that is exactly what makes it one of the most interesting areas to watch in the coming years.