Observability unification demands coordinated teams

Observability unification starts with operational coordination

Buying a consolidated observability platform leaves an operational question unresolved: can teams use their data and processes together? As businesses pursue automated operations during AI transformation, observability, the use of system data to understand how technology behaves, has become a strategic priority. Platforms and AI can help, but observability unification also depends on how teams assign responsibilities, coordinate processes and share information.

The surveyed one-tool goal remains unmet

The gap between consolidation ambitions and reported outcomes shows why those operational decisions matter. In EMA’s report, “The reality of observability unification in modern IT operations.”, 62% of surveyed enterprise IT organizations rated observability-tool consolidation “very important.” Yet EMA found that “not one surveyed organization” had achieved a “single pane of glass” by consolidating into one tool. The goal was a single view of operations, but reducing the toolset to one product had delivered that outcome for none of the respondents.

To understand that gap, leaders need to separate consolidation from unification. Consolidation means reducing or combining tools; unification means making visibility and operational coordination work across the enterprise. Hathcock’s recommendation is to approach unification as a change to how operations work, which puts responsibilities, processes and data at the center of the effort. Tool removal follows that assessment.

Map responsibilities, processes and data before consolidating

The operational assessment begins with an accurate understanding of the enterprise, including “who does what,” the data involved and the processes teams follow, Hathcock said. These elements need to be considered together: responsibilities identify who performs the work, processes describe how it happens, and data supplies the information used along the way. Connecting them gives leaders a basis for examining how separate IT teams operate.

With that picture established, Hathcock recommends assessing processes across siloed IT teams to identify and eliminate tool redundancy. Looking across teams matters because the assessment concerns the work spread between them, including the tools used to perform it. The sequence puts an understanding of that work ahead of decisions about which products to remove.

The same cross-team assessment should lead to common goals and the ability to share data, Hathcock said. Common goals give teams an agreed purpose for working together, while data sharing makes information available across team boundaries. This makes consolidation part of a broader operational change: teams need to coordinate the work that remains after redundant tools are removed.

AI adds visibility and monitoring responsibilities

AI can support that operational change by generating insights and improving visibility across existing tools, according to EMA’s report. Its role therefore includes helping teams understand an environment that still contains multiple products. Alongside decisions about which tools to consolidate, leaders can consider how AI helps connect and interpret the information those tools provide.

Vendors are responding to concerns about tool sprawl by adding AI to their observability offerings. In January, Snowflake acquired Observe, an AI-powered observability platform, to improve visibility and transparency for enterprises using AI. The acquisition brings together two related needs: using AI to understand technology operations and giving businesses greater visibility as they adopt AI.

But AI also becomes another tool that operations teams must observe, Hathcock warned. If implemented poorly, it can increase tool sprawl, the spread of tools across an organization, even when the reason for introducing it is to help unify observability. Teams therefore need to account for the AI tool itself when planning the change, alongside the insights they expect it to provide.

Monitoring AI activity is also part of Cisco’s observability offering. Cisco acquired Splunk, a data, AI and observability platform, in 2024 and added new observability capabilities on Sept. 15. According to Cisco’s press release, Splunk Agent Observability provides a live view of AI-agent performance and AI token spending. AI agents are software systems that use AI to carry out tasks, while tokens are the units of data AI models process.

That monitoring capability addresses the additional responsibility that comes with introducing AI: operations teams need visibility into how it performs and what its processing costs. Hathcock’s advice is to prepare before adoption: “Taking a measured and planned road to using AI and automation is the right way to do it,” he said. He places data preparation ahead of pressure to deploy: “Get the data straight first. Don’t try to pressure yourself just to put AI in there.”

Main highlights

Consolidation needs an operational goal: EMA found that 62% of surveyed enterprises rated observability-tool consolidation very important, yet none had achieved a single operational view through one tool. IT leaders need to define how visibility and coordination will work across teams before selecting a consolidation target.
Map the work before removing tools: IT operations leaders need a shared picture of responsibilities, processes and data across siloed teams. Use that assessment to identify redundant tools, establish common goals and enable data sharing.
Plan for AI’s monitoring needs: AI can improve visibility across existing tools while adding another system to manage. Operations teams need reliable data before adoption and monitoring for AI performance and token spending after deployment.