Taming Tool Sprawl: The Hidden Obstacle to Enterprise Visibility

Key points

Enterprises are struggling with fragmented tools and maturity gaps in their observability efforts, leading to inefficient monitoring and high costs.
Research reports from New Relic and BlueCat emphasize the need for observability technologies and best practices to evolve from reactive firefighting to proactive intelligence, with AI leading the way.
Successful organizations are investing in integration and automation to streamline workflows, and are achieving full-stack observability to reduce downtime and improve efficiencies.

According to recent research reports, enterprises are facing significant challenges in their efforts to advance monitoring across their distributed, hybrid environments. The reports from New Relic and BlueCat highlight the need for observability technologies and best practices to evolve from reactive firefighting to proactive intelligence, with AI playing a key role. The New Relic report, based on a global survey of 1,700 IT and engineering leaders, found that the median cost of a high-impact outage is $2 million per hour, with organizations that have achieved full-stack observability experiencing significantly lower costs.

The reports also emphasize the challenges that network teams face with their network observability tools, with only 46% of respondents believing they are fully successful with these tools. The biggest complaints about these tools include limited scope, high costs, lack of customization, and difficult implementation and maintenance. To address these challenges, EMA has developed a Network Observability Maturity Model, which defines five levels of observability: Ad Hoc/Reactive, Fragmented/Opportunistic, Integrated/Centrally Managed, Intelligent/Automated, and Optimized/AI-Driven.

The reports also highlight the importance of AI in observability, with New Relic reporting a significant increase in AI monitoring adoption from 42% in 2024 to 54% in 2025. AI-assisted troubleshooting, automated root cause analysis, and predictive analytics are cited as the top use cases for AI in observability. The reports also emphasize the need for integration and automation to streamline workflows, with successful organizations investing in unified observability platforms and customizable, role-specific dashboards.

The EMA report found that success correlates with alignment across DevOps, NetOps, SecOps, and business stakeholders, and that unified, AI-enabled observability can reduce downtime, improve efficiencies, and create resilience. New Relic also noted that a cultural shift is necessary, where reliability becomes everyone’s responsibility. Overall, the reports highlight the need for enterprises to prioritize observability and AI to achieve full-stack visibility and reduce the costs and inefficiencies associated with outages and downtime. With Microsoft and Azure playing a key role in the cloud computing and observability landscape, it is essential for enterprises to leverage these technologies to achieve optimized/AI-Driven observability and stay ahead of the competition.