Dive Brief:
- Enterprise IT organizations are grappling with observability tool sprawl as individual teams adopt their own specialized tools to monitor complex technology environments, according to research recently published by Enterprise Management Associates. The consulting firm surveyed 356 enterprise IT professionals for the report.
- Three in four enterprise IT organizations use up to 12 observability tools across cloud infrastructure, network and service environments, the report found. Enterprises expect AI to help unify observability, with 32% of organizations applying AI extensively to IT observability, according to the report.
- The technology is still not a magic fix-all for tool fragmentation, Parker Hathcock, research director of ServiceOps at EMA, told CIO Dive. “AI can help, but it also adds a level of complexity,” he said.
Dive Insight:
Unifying the sprawl of observability tools is becoming a strategic priority for businesses as they pursue automated operations amid ongoing AI transformation.
The consolidation of observability tools was identified as “very important” to 62% of enterprise IT organizations surveyed by EMA in its report, “The reality of observability unification in modern IT operations.” However, not one surveyed organization had achieved a “single pane of glass” through consolidation into one tool, the report said.
In response to sprawl concerns, vendors are working to enhance their observability platforms with AI. In January, Snowflake acquired AI-powered observability platform Observe to bolster visibility and transparency for AI-driven enterprises.
On Sept. 15, Cisco added new observability capabilities to Splunk, a data, AI and observability platform the company acquired in 2024. Splunk Agent Observability gives organizations a live view into the performance of AI agents as well as AI token spend, according to a press release.
Companies need to treat observability unification as an operational transformation project, Hathcock said. Organizations should start by gaining an accurate picture of the enterprise, “who does what,” data and processes. The next step is to assess processes across siloed IT teams to eliminate tool redundancy, create common goals and establish the ability to share data, Hathcock said.
AI can play a role in observability unification by providing insights and improving visibility across tools, according to the report. However, it’s still another tool IT teams have to observe and, if not implemented properly, one that can create additional sprawl, Hathcock said.
“Taking a measured and planned road to using AI and automation is the right way to do it,” he said. “Get the data straight first. Don’t try to pressure yourself just to put AI in there.”
