Generative AI Adoption Surges to 98% as OpenTelemetry Redefines Production Environments

by David Hope, February 18, 2026

Explore how generative AI and OpenTelemetry are revolutionizing enterprise observability, leading organizations to leverage advanced telemetry data for transformative insights.

Gen AI Nears 98% Adoption as OpenTelemetry Gains Ground in Production

By David Hope
February 18, 2026

Enterprise observability has evolved significantly, yet the introduction of generative AI (Gen AI) and OpenTelemetry (OTel) is marking a transformative phase in how organizations gather, analyze, and act on their telemetry data. A recent report by Elastic, "Landscape of Observability in 2026," solidifies this trend, confirming that these innovations are no longer mere experimental projects; they are fully deployed, reshaping vendor selection and work dynamics within teams.

Gen AI: The Hype Is Real but So Are Its Limits

Currently, approximately 85% of organizations leverage some form of Gen AI for observability, with projections suggesting that this number will climb to an astonishing 98% within the next two years. As Gen AI becomes a standard component of observability platforms, its rapid adoption is hardly surprising. The sheer scale of data generated during observability is beyond human processing capabilities. Traditionally, engineers manually correlated logs, metrics, and traces during incidents, a task that can overwhelm even the most experienced professionals. Gen AI facilitates automated pattern recognition and enables teams to query complex telemetry data using natural language, simplifying the analysis process.

However, while the promise of Gen AI is significant, the practical implications can be more complex than anticipated.

How Teams Are Implementing Gen AI

Most organizations are embracing a multi-faceted approach by running several Gen AI solutions concurrently, as no single tool addresses all needs effectively. For instance, standalone tools like ChatGPT and Claude each hold a nearly identical adoption rate—53% and 52%, respectively—due to their ease of deployment. However, their adoption trajectories are diverging: while 15% of teams plan to introduce standalone Gen AI solutions, 23% will focus on enhancing built-in features offered by vendors. It’s projected that the use of Gen AI embedded in vendor solutions will hit 75% adoption within two years.

This trend is rooted in context. Standalone tools often lack awareness of service dependencies and historical behaviors, making them more suitable for ad hoc analyses rather than production workflows. Conversely, vendor-integrated solutions are designed to operate within the pre-existing context of telemetry data.

Gen AI Maturity Matters More Than Budget

Interestingly, the maturity of an organization plays a critical role in Gen AI adoption. While early-stage teams report a 71% adoption rate, mature organizations see adoption rates ranging from 85% to 88%. This discrepancy extends to the types of Gen AI tools utilized: early-stage teams tend to gravitate towards standalone solutions, but mature teams are more likely to adopt vendor-integrated options, which can correlate strongly with overall organizational maturity.

Consider the realm of agentic AI: systems that autonomously investigate issues and perform data correlation, with some even executing remediation tasks. Currently, 23% of organizations utilize agentic AI, with another 38% planning to adopt such systems. However, the usage of agentic AI varies significantly with maturity level—expert teams exhibit a 35% adoption rate, while early-stage teams report none.

This gap reveals the necessity for foundational prerequisites, including comprehensive telemetry, consistent data schemas, and documented dependencies. Without these established parameters, automating remediation becomes virtually impossible.

Efficiency Gains: Real, but Modest – for Now

Around 68% of teams utilizing Gen AI report notable increases in efficiency, yet only 14% describe these gains as substantial. The majority are experiencing incremental improvements. Looking ahead, expectations change dramatically: 84% of teams foresee efficiency enhancements, with 56% anticipating substantial gains—a fourfold increase from current experiences.

This gap is logical; the present implementations represent first-generation technologies, and teams have yet to overhaul their processes to fully realize their potential. This phenomenon mirrors historical trends with automation, where initial benefits tended to be modest until workflows adapted.

Where Gen AI Actually Works

The most valuable applications for Gen AI currently focus on correlation and automation. Key use cases include:

• Automated correlation of logs, metrics, and traces: 58%
• Root cause analysis across dependencies and historical patterns: 49%
• Remediation and automated operations with guardrails: 48%
• Detection of unknown unknowns: 47%
• Assistant tasks such as reporting, dashboards, and query optimization: 47%

OTel Is Becoming the Default

The adoption of OpenTelemetry in production has nearly doubled, rising from 6% to 11% year-over-year. While still in its infancy, OTel is rapidly becoming the go-to standard for new instrumentation.

As organizations transition into production phases, their priorities invariably shift. A noteworthy 89% of production users deem vendor OTel compliance as crucial or very important, emphasizing the significance of full specification support, semantic conventions, and seamless ingestion processes.

In a landscape where data collection becomes commoditized, the differentiation will hinge on what happens post-ingestion—specifically, the AI-driven insights generated and the speed of investigations.

A Convergence for the Future

The intersection of generative AI and OpenTelemetry in observability is redefining how platforms are evaluated. Buyers now prioritize integrated Gen AI for swift time-to-value, clear agentic AI roadmaps with safeguards, and comprehensive native OTel support. As both Gen AI and OTel transition from being emerging to foundational technologies, these capabilities are becoming essential, marking a turning point for vendors investing in both.

As we move forward into this new era, organizations that adapt and embrace these technologies will be positioned to thrive, while those that hesitate risk being left behind.

Disclaimer: The release and timing of any features or functionality described in this post remain at Elastic’s sole discretion. There are no guarantees regarding the availability or timely delivery of features currently under development.

In navigating the complexities of Gen AI and observability tools, organizations should remain vigilant about the tools they choose to adopt, especially in terms of security and the handling of sensitive information.

Explore further and stay informed as we dive deeper into the evolving landscape of observability and artificial intelligence!