Enterprise network operations teams are being asked to manage increasingly complex environments with an expanding stack of monitoring and observability tools. A recent survey of 356 enterprise IT professionals found that most organizations run six to eight observability tools, creating integration work, staffing pressure, context switching, and visibility gaps. At the same time, teams are turning to artificial intelligence to correlate alerts, detect anomalies, summarize incidents, and support capacity planning. The research suggests that AI can help operations teams cope with complexity, but it does not eliminate the underlying problems caused by fragmented tools, inconsistent data, and weak governance.

That finding matters because observability has become a core operational capability rather than a niche monitoring function. Network, cloud, application, security, and service management teams all rely on telemetry to understand whether digital services are healthy. When those teams use separate toolchains, each group may see a narrow slice of reality. The result can be a frustrating paradox: every dashboard reports that its domain is healthy, while users still experience delays, outages, or degraded performance. Closing that gap requires more than adding another tool or layering AI on top of the existing stack.

Key Facts From the Research

• The survey included 356 enterprise IT professionals.
• Most organizations use six to eight observability tools.
• 51 percent had tool unification efforts under way, 32 percent were planning or evaluating unification, and 17 percent had completed it.
• 45 percent cited skills and staffing burdens from operating multiple tools.
• 44 percent said integration and API complexity consume engineering time.
• 41 percent said context switching among tools slows investigations and response.
• 40 percent reported increased manual effort.
• 34 percent said alert noise creates toil and cognitive overload.
• 32 percent use AI extensively across observability tools, 51 percent use it in selected areas, and 13 percent are still piloting it.
• 24 percent reported fully centralized observability tool governance, while 48 percent said ownership was centralized for the most part with some domain-specific exceptions.

Tool Sprawl Strains IT Operations

Tool sprawl is more than a procurement problem. Every additional platform introduces its own data model, interface, alerting logic, access controls, and upgrade cycle. Engineers must learn how each tool behaves, how to query it, and how to interpret its output. Integration work expands as teams try to connect dashboards, ticketing systems, automation workflows, and security controls. Even when individual tools are effective within their domains, the seams between them can slow incident response and obscure the end-to-end customer experience.

The survey captured several of these operational costs directly. Forty-five percent of respondents cited skills and staffing burdens from operating multiple tools. Forty-four percent said integration and API complexity consume engineering time. Forty-one percent pointed to context switching among tools as a drag on investigations and response. Forty percent reported increased manual effort, and 34 percent said alert noise creates toil and cognitive overload. These are not abstract concerns. They translate into longer mean time to detect and resolve incidents, more repetitive work, and less time for engineering teams to improve reliability.

Sprawl also affects cost and data quality. More tools mean more licenses, more training, more integration maintenance, and more overlapping capabilities. Data may be duplicated, delayed, or defined differently across platforms. Coverage gaps can emerge when one team assumes another tool is monitoring a dependency. End-to-end visibility suffers when no single group can correlate signals across network, cloud, application, and user experience domains. In one common scenario, network operations can show that connectivity is healthy, cloud teams can show that infrastructure is operating normally, and application teams can show that services are available, yet users still report a problem. The tools may all say everything is fine, but the organization lacks the means to connect the dots.

Why the Single Pane of Glass Remains Elusive

The idea of a single pane of glass has long appealed to IT leaders. In practice, it has been difficult to achieve because observability is not one market or one workflow. Network performance monitoring, application performance monitoring, cloud infrastructure monitoring, log analysis, security analytics, digital experience monitoring, and service management each have specialized requirements. Large organizations often inherit tools through mergers, regional operations, or domain-specific purchasing decisions. Central IT may standardize on one platform while application teams adopt another, and security teams may insist on separate controls for compliance reasons.

The survey reflects that reality. Half of respondents had unification efforts under way, but only 17 percent had completed unification. Nearly one-third were still planning or evaluating their approach. Larger organizations reported using more observability tools across their environments, which is consistent with the scale and diversity of their technology estates. Unification, therefore, is less about finding one product that does everything and more about creating a governed architecture in which data, workflows, and ownership are connected across tools. The goal is a coherent operating model, not necessarily a single platform.

AI Takes On More of the Workload

Enterprises are already applying AI to operational data. Thirty-two percent of respondents said they use AI extensively across observability tools, 51 percent use it in selected areas, and 13 percent are still piloting it. Common use cases include incident summaries and explanations, alert correlation and noise reduction, capacity forecasting, anomaly detection, predictive analytics, and root-cause analytics. These capabilities can reduce manual effort and help engineers make sense of large volumes of telemetry. AI can also help correlate information across tools when data is fragmented, surfacing patterns that would be difficult to spot manually.

But AI is not a substitute for fixing bad data or broken processes. If the underlying telemetry is incomplete, inconsistent, or poorly governed, AI models may produce confident but inaccurate conclusions. Organizations that reported extensive AI use also said they had greater confidence in their observability unification strategies. That correlation makes sense: teams with cleaner data, stronger integration, and clearer ownership are better positioned to train, operate, and trust AI systems. At the same time, respondents cited concerns about inaccurate outputs and security and compliance risks. Those concerns grow as AI moves from summarizing alerts to recommending actions and eventually taking action on infrastructure.

Governance Before Simplification

Tool governance has emerged as a prerequisite for successful unification. About one-quarter of organizations reported fully centralized observability tool governance, while 48 percent said ownership was centralized for the most part with some domain-specific exceptions. Organizations with centralized ownership reported greater confidence in their unification strategies, deeper integration between observability and IT service management, more effective incident-impact analysis, and more mature use of AI-powered observability.

Centralization, however, is not simply a matter of putting one group in charge. The research found that understaffed tool governance and engineering teams had less success with unification strategies. A centralized group needs authority, funding, engineering capacity, and executive support to define standards, manage integrations, and resolve conflicts between domain-specific needs. Without those resources, governance becomes a bottleneck rather than an enabler. The practical lesson is that organizations should establish clear ownership and then equip that owner to deliver. Simplification efforts are more likely to succeed when governance precedes consolidation rather than following it.

When AI Starts Acting on Infrastructure

AI governance becomes even more important when AI systems begin taking actions across network infrastructure. As enterprises give AI more autonomy, they need controls that match the probabilistic nature of the technology. Agentic AI systems can generate responses and take actions that are not always predictable, while many traditional cybersecurity controls are designed to operate deterministically by following predefined rules. That mismatch creates risk.

Consider a firewall rule that appears unnecessary to an AI system. The rule might allow a particular IP address to access a database process for one day each quarter in support of a legitimate business process. An AI system scanning for unused or redundant rules might identify it as a mistake and remove it without understanding the quarterly workflow it supports. What looks like cleanup could become an outage. This example illustrates why network teams must determine where human review remains necessary as AI takes on more operational functions.

AI governance requires human oversight for unusual exceptions, business-critical rules, and potentially harmful or inaccurate outputs. Controls should be tailored to an organization’s risk profile, with clear boundaries around what AI systems can and cannot do. For network operations teams, the increasing use of AI and automation requires integration of observability data and workflows, stronger tool governance, and controls over autonomous actions. AI is becoming another tool in the operations tool chest, and it will continue to evolve. The organizations that benefit most will be those that treat it as part of a governed operating model rather than as a shortcut around fragmentation. The survey’s central lesson is that AI can make complex environments more manageable, but it cannot make poor data, weak processes, or unclear ownership disappear.

Source: Network World News