Only 9% of enterprise software applications today provide complete end-to-end observability, according to new insights from a technology position paper by Neurones IT Asia.
The paper, titled "From Monitoring to Intelligence: How Observability and AI Redefine IT Operations," reveals that data signals remain fragmented across various tools and platforms. This fragmentation leads to increased alert noise and slows down investigation processes.
To enhance observability, it is essential to improve the signal-to-noise ratio, correlate telemetry across infrastructure and applications, and accurately identify the true root cause of issues across distributed services and dependencies.
AI-driven observability
AI-driven observability can help teams correlate events across systems, detect anomalies earlier, and accelerate root-cause analysis through context-driven insights, rather than merely reacting to symptoms after users are affected.
Research indicates that when properly implemented, AI-driven observability can help organisations reduce mean time to resolution by up to 70%, enhance decision-making, and minimise unnecessary escalations.
Guillaume Zaplana, Deputy Director at Neurones IT Asia, said: "Our findings highlight a 'telemetry paradox': Companies have more data than ever, but less clarity. While many invest heavily in tools, only a fraction achieves true end-to-end visibility, which keeps operational costs high. At Neurones IT Asia, we close this gap by applying AI to filter the noise and automate root-cause analysis, transforming telemetry into intelligence."
