At New Relic, we're well-known Data Nerds. So when we want to understand how our industry is changing and how people are doing, we want data. New Relic's 2025 Observability Forecast analyzes data from a survey of over 1,700 IT and engineering leaders and team members across 23 countries and 11 industries. This year's report offers key insights into growth areas, challenges, and trends that are influencing observability investments, like the growing adoption of enterprise AI.

In particular, it found that with a median annual cost of high-impact IT outages reaching $76 million, organizations are investing in AI-strengthened observability to detect and resolve issues faster.

Here are 5 key takeaways from this year's report:

1. AI monitoring is on the rise

As organizations turn AI pilot programs into permanent tools for day-to-day functions, monitoring is more important than ever. Leaders need real-time insight into how complex, distributed systems behave and interact in order to avoid hidden failures. Using AI-powered observability platforms in real time reveals how AI models interact with pipelines, APIs, and downstream applications.

The report found that 54% of IT professionals use the AI monitoring feature of observability, up from 42% last year, representing double-digit growth. Meanwhile, only 4% of businesses say they have no plans to adopt AI monitoring. In addition, executives and technology leaders cite AI-assisted troubleshooting as the number one AI feature they think would improve  their organization's incident response practice.

2. Observability significantly reduces downtime

Downtime has evolved from an IT concern into a board-level risk. In our survey, organizations reported a median cost of $2 million per hour for high-impact outages, a figure that highlights the considerable financial exposure that comes with even brief disruptions.

The root causes of these costly outages reflect the complexity of modern digital environments. Network failure (35%) and third-party provider issues (28%) remain the leading culprits, underscoring the fragility of interconnected systems. The adoption of observability minimizes the duration of those outages. Nearly seven in 10 of the organizations surveyed reported measured improvements in mean time to detect (MTTD) since investing in observability, confirming that visibility and speed are both technical wins and financial imperatives.

3. AI-enhanced observability makes time for innovation and improves efficiency

Engineers report that they spend 33% of their time firefighting or addressing technical disruptions rather than developing new features or innovating. Streamlined observability workflows, especially with AI-powered assistance, allow engineers to identify issues quickly, reducing the cognitive load and frustration associated with debugging complex systems.

When less of their time is consumed by reactive work, engineering teams can dedicate more effort to building new features, improving existing products, and innovating. Developers say that observability leads to faster troubleshooting and root cause analysis (58%) and improved collaboration across teams (52%).

4. Organizations still lag in their adoption of full-stack observability

While the benefits of monitoring are clear, 73% of organizations still lack full-stack observability, leaving broad segments of their technology infrastructure and applications more likely to disrupt operations or customer experiences. Consumer complaints, manual checks, or incidents are still used to identify service interruptions 41% of the time. Disconnected monitoring tools and siloed data make diagnosing an incident a more complicated puzzle, slowing down response times and increasing the cost of outages.

Meanwhile, just 23% of teams with full-stack observability report high-impact outages at least weekly, compared with the 40% that don't use it. Additionally, they detect outages seven minutes faster and reduce the average hourly cost by 50%, to $1 million.

5. More organizations are consolidating their observability tools

Organizations are actively working to simplify their digital workflows. The average number of observability tools per organization has dropped 27% since 2023, from 6 to 4.4. Further, 52% of organizations plan to consolidate observability tools onto a unified platform in the next 12 to 24 months. This trend reflects a recognition that an abundance of tools can create as many problems as they solve, requiring users to manually toggle between platforms.

AI-strengthened observability, or what we call "Intelligent observability" is rapidly emerging as the connective tissue that makes AI reliable and useful — enabling organizations to predict and prevent outages, optimize costs, and scale innovation with confidence and responsibility.