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Enterprise network operations are harder when teams must piece together incidents across specialized monitoring tools, cloud and data-center systems, applications, and service workflows. AI can help correlate events and reduce alert noise, but it cannot by itself repair fragmented integrations or unreliable telemetry. The practical goal is shared incident context, governed data, and clear human oversight—not a promise that one dashboard will replace every specialist tool.
Why does tool sprawl slow network operations?
Specialized observability tools can give teams useful detail about particular domains. The friction starts when an incident crosses those boundaries: engineers switch interfaces, translate between data sources, and manually assemble a view of what happened. That makes tool sprawl a workflow and organizational problem as much as a software-count problem.
In its October 8, 2026 report, Network World cited an Enterprise Management Associates (EMA) survey in which respondents described several connected burdens:
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| Reported burden | Share |
|---|---|
| Skills and staffing demands from operating multiple tools | 45% |
| Integration and API complexity consuming engineering time | 44% |
| Context switching slowing investigation and response | 41% |
| Increased manual effort | 40% |
| Alert noise and cognitive overload | 34% |
These are findings from EMA’s 2026 observability-unification survey, as reported by Network World; they are not proof that tool count alone causes each problem. In practice, weak integrations can make a domain-specific tool harder to use alongside others, while unclear ownership can leave teams responsible for maintaining overlapping pipelines and interfaces.
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How widespread is the effort to unify observability?
EMA announced The Reality of Observability Unification in Modern IT Operations on September 15, 2026. Its survey covered 356 enterprise IT professionals. The findings point to broad interest in unification, alongside a substantial implementation workload:
| Finding | Share |
|---|---|
| Use 4–12 observability tools across network, cloud infrastructure, and service environments | 75% |
| Say observability tool unification is very important | 62% |
| Switch tools 3–5 times per incident | 55% |
| Have unification efforts under way | 51% |
| Are planning or evaluating an approach | 32% |
| Report unification completed | 17% |
The first three figures are from EMA’s September 2026 announcement; the implementation-status breakdown was reported by Network World from that study. Together, they describe a priority that many organizations are still working through, not a settled end state.
Unification need not mean discarding every specialist product. It can instead mean consistent telemetry, useful integrations, and a shared operating view that lets network, cloud, application, and service teams follow the same incident. EMA vice president of research for network infrastructure and operations Shamus McGillicuddy cautioned in Network World: “One of the first things I can say is no one gets a single pane of glass.”
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What other pressures are changing network operations?
EMA’s Network Management Megatrends 2026 is a separate study, not the observability-unification survey. Its May 18, 2026 announcement describes 352 IT professionals in North America and Europe who were directly involved in enterprise network management or oversaw network operations. The findings show why teams may be reconsidering both their tools and their operating models:
| Finding | Share of respondents |
|---|---|
| Reported completely successful network-operations strategies | 31% |
| Were completely satisfied with their network monitoring and troubleshooting tools | 32% |
| Expected to replace some network monitoring or troubleshooting tools within two years | 73% |
| Considered hiring and retaining network-technology experts a significant challenge | 52% |
| Expected their organization to run AI application workloads on-premises or in the cloud within two years | 97% |
These results are from EMA’s May 2026 study announcement. They indicate expectations and reported experience among that study’s respondents; they should not be treated as outcomes for all enterprises or combined with the other EMA survey populations.
Where can AI help—and what does it depend on?
Useful observability tasks
AI is being applied to correlate related events, reduce alert noise, detect anomalies, summarize incidents, forecast capacity, and support root-cause analysis. Those functions can help engineers find relevant signals sooner, particularly when an incident spans several technical domains. They are aids to investigation, not evidence that an AI system can infer a reliable end-to-end picture from disconnected or inconsistent sources.
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Data quality is a constraint
EMA’s AI-Driven NetOps: How Enterprises are Embracing Intelligent Network Management Solutions, announced January 20, 2026, covered 458 IT professionals. In that separate survey, 44% expressed full confidence in the quality of their network data to support AI initiatives. McGillicuddy said in the announcement, “Network data quality is the AI killer,” and “IT organizations must clean up their network data before they invest in AI.” The figures and comments appear in EMA’s January 2026 announcement.
EMA also reported that 59% of respondents were using AI features provided by network-management vendors, while 52% were training AI models with their own IT and security data. Adoption therefore does not establish that underlying data is complete, consistent, or suitable for every use. Teams should check what telemetry an AI feature consumes, how missing or conflicting records are handled, and whether its output can be traced back to source evidence.
AI adoption is not the same as proven operational success
In the same 458-person EMA AI-NetOps survey, 35% reported complete success with AI-driven network-management initiatives, and 39% said they were completely confident in their organization’s ability to evaluate those solutions. These survey responses do not establish that AI caused better outcomes, nor do they predict results for a particular enterprise.
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How should an enterprise approach unification?
1. Map the incident workflow before selecting a platform
Trace how a network incident is detected, investigated, escalated, and resolved. Record which teams and tools contribute at each point, where engineers switch systems, and which handoffs require manual copying or interpretation. This reveals whether the main gap is missing telemetry, incompatible integrations, unclear ownership, or a lack of shared service context.
2. Set governance and ownership
EMA research director Parker Hathcock told Network World: “All of these issues can compound each other, so that’s why strong tool governance is essential,” adding, “It’s an essential step to get to a better place before you even start trying to simplify what you have.” In that report’s account of the 2026 unification survey, 24% said observability governance was fully centralized, while 48% described ownership as mostly centralized with some domain-specific exceptions. A workable governance model should identify who approves tools and integrations, who maintains telemetry standards, and how domain teams can document legitimate exceptions.
3. Define the shared context teams need
Agree on how systems, services, dependencies, incidents, and ownership are represented across tools. A common view is only useful if teams can connect an alert to the affected service, relevant changes, related events, and the people responsible for follow-up. Keep specialist interfaces where they add needed depth, while reducing the work required to move evidence between them.
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4. Validate data and integrations before relying on AI
Check telemetry coverage, consistency, freshness, and access controls. Test whether integrations preserve timestamps, identifiers, and relationships across sources, and whether the information supplied to AI features is governed appropriately for security and compliance. Evaluate AI outputs against incidents with known causes rather than assuming a fluent summary or correlation is correct.
5. Bound automated actions with human review
Using AI to prioritize alerts or draft an incident summary carries a different operational risk from allowing it to change routing, configurations, or service policies. Define which actions can be automatic, which require approval, and which must remain under operator control. Establish review and rollback procedures for exceptions and business-critical services before granting an AI system authority to change infrastructure.
What should teams compare when evaluating approaches?
EMA’s findings identify relevant evaluation dimensions, not a universal scoring rubric or a recommendation for any vendor. Compare options against the workflow and risk boundaries the organization has actually defined:
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- Integration burden: Which APIs and connectors are required, who maintains them, and what happens when a source changes or becomes unavailable?
- Telemetry quality and coverage: Are data sources sufficiently complete, consistent, and current for the intended troubleshooting and AI tasks?
- Service workflows: Can findings connect to incident management and broader ServiceOps processes, including ownership and escalation?
- Governance and staffing: Are responsibilities clear, and can the organization operate the integrations and workflows without creating an unsustainable support burden?
- AI safeguards: Can teams inspect the evidence behind outputs, control sensitive data, and enforce security and compliance requirements?
- Automation boundaries: Are approval, exception, and rollback rules explicit for changes that could affect network or service availability?
EMA’s announcements are survey sources and do not provide full questionnaire wording or all methodology. The studies also have commercial sponsors, as named in their official announcements; sponsorship is context for interpreting the findings, not validation of any product. Treat the reported percentages as respondent findings from their stated samples, not universal rates or causal proof.
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