Unlocking the Next Level of Observability

Observability has rapidly evolved from a niche IT concept to an essential business enabler, directly influencing organizational processes, customer experiences, and even revenue  growth. But as systems grow increasingly complex, practices mature, and adoption scales globally, what’s next for observability? Splunk’s State of Observability 2025 Report examines these trends in depth, from the maturing baseline of observability adoption to its growing role as a business driver; the report offers critical insights into the state of modern IT.

In this blog, we’ll unpack the key findings, explore common challenges, and provide actionable strategies to help you navigate the next phase of observability in your organization.

Observability’s Big Shift: From IT Tool to Business Catalyst

Observability has come a long way. A few years ago, it was primarily seen as a tool for IT operations and DevOps teams to monitor systems and troubleshoot issues. Today, it’s no longer just “nice to have” or experimental; observability has matured into a core business enabler.

The data tells the story. According to the report, 74% of respondents believe observability is moderately to very important for monitoring critical business processes. What’s striking is the shift from traditional IT-focused use cases (like logs and metrics monitoring) to broader business-oriented outcomes, such as optimizing user experiences and reducing customer churn.

But with this evolution comes complexity. Distributed systems and microservices, while enabling flexibility and scalability, have introduced new challenges in pinpointing and addressing performance bottlenecks. Moreover, observability is no longer just about IT; it’s for business stakeholders, product owners, and SREs who are aligning technology performance with KPIs like revenue impact and user satisfaction.

Unlocking Observability’s Full Potential: The Role of Data and AI

At its core, observability is about data; collecting it, analyzing it, and acting on it. But as organizations adopt modern architectures, like microservices and serverless, the volume, velocity, and variety of data can be overwhelming. Here’s where AI and smarter tooling enter the picture.

The report revealed that 76% of engineers now use AI tools regularly, with AIOps taking the lead as the most adopted AI-driven capability. Why? Because AI can help make sense of massive data volumes, find patterns, and alert teams before issues occur. Solutions like AIOps automate noise reduction, root cause analysis, and incident prediction, enabling teams to focus on innovation instead of troubleshooting.

However, challenges remain. Respondents cited data quality, associated infrastructure costs, and tooling sprawl as barriers to realizing the full potential of observability. As organizations layer on more tools, the complexity of integrating data across silos increases, leading to inefficiencies and war-room headaches.

The answer? A consolidated, platform-driven approach that combines data pipelines, context-rich telemetry, and AI-powered insights.

Feature Deep Dive: Practical Tools Shaping Observability

Let’s break down some of the core capabilities driving modern observability practice:

1. OpenTelemetry: The Backbone of Observability

OpenTelemetry (or OTEL) is quickly becoming the de facto standard for collecting and instrumenting telemetry data (metrics, events, logs and traces - MELT). Supported by Splunk and AWS among others, OpenTelemetry provides a unified, vendor-neutral way to gather data across distributed environments.

By using OTEL, teams avoid vendor lock-in, maintain better control over their telemetry, and reduce the overhead of managing multiple agents. Plus, its growing adoption across industries means collaboration and knowledge sharing are easier than ever.

2. AIOps and Noise Reduction

The era of alert fatigue is ending, thanks to AIOps. A modern observability platform doesn’t just collect data, it contextualizes it, correlating alerts across multiple tools to pinpoint root causes. For example, one German car manufacturer cited in the report was able to reduce downtime by correlating over 40 applications into a single view for real-time decision-making.

3. Code Profiling for Microservices

In the age of microservices, identifying slowdowns at the infrastructure level isn’t enough. Solutions like code profiling help teams drill down to the specific line of code causing performance degradation. Imagine a scenario where IT teams can quickly determine whether performance issues stem from application logic versus infrastructure load—with profiling, they can take immediate, targeted action.

4. Integrating Observability with Security

A promising emerging trend is the integration of observability and security use cases. By correlating observability data (like application performance logs) with security data (like threat detection events), businesses unlock new layers of insight. For instance, DevOps teams can proactively address vulnerabilities impacting performance KPIs before they escalate into costly incidents.

Real-World Impact: Observability’s Business Case

The business value of observability is clear. In the report, 65% of respondents said their observability practices directly influenced revenue outcomes—whether by improving customer experiences, reducing downtime, or optimizing resource usage.

Take the example of an automotive SRE whose primary KPI shifted from infrastructure uptime to “how many cars we’re selling in real-time.” By correlating IT systems with sales performance metrics, the team reframed observability as a strategic business enabler rather than a technical cost center.

But the challenges of scaling observability are just as real: 11% of respondents mentioned that observability negatively impacted their roadmaps, often due to increased troubleshooting time tied to tooling sprawl. This stat underscores the importance of data consolidation, efficient pipelines, and tools that reduce redundant effort.

Best Practices for Driving Observability Maturity

Want to ensure your observability practice delivers measurable business outcomes? Start here:

• Adopt OpenTelemetry for Standardized Data Collection: Save time, reduce vendor dependency, improve security, and gain better visibility across distributed systems.
• Use AIOps to Cut Through Noise: Focus on solutions that automate root cause analysis and reduce alert fatigue.
• Prioritize Platform Consolidation: Avoid tooling sprawl by centralizing data pipelines, context, and analysis in a single observability back end solution.
• Collaborate Across Teams: Build bridges between DevOps and SecOps to improve data sharing and derive richer insights.
• Focus on Metrics that Matter: Align IT KPIs with business outcomes like user retention, revenue growth, and customer satisfaction.

What’s Next: The Future of Observability

Observability is no longer just about logs and metrics—it’s the foundation for digital transformation. As businesses continue to adopt AI, machine learning, and modern architectures, the ability to combine telemetry data with business intelligence will become a competitive differentiator.

The future lies in even tighter integrations between tools, smarter AI-driven insights, and use cases that extend beyond IT to marketing, finance, and beyond. And for engineers just starting their observability journey, OpenTelemetry will help set the stage for success.

Ready to learn more? Sign-up to our Webinar How to Drive Revenue and Growth with Observability to start turning your observability practice into a business catalyst today

Explore Splunk’s full State of Observability 2025 Report to dive deeper into trends, statistics, and actionable strategies.