Industry Metrics
Navigating Tool Sprawl, Telemetry Silos, and Burnout
Tool sprawl is costing teams time, and surging AI traffic volumes are compounding the challenges.
85%
Surging AI network traffic
Eighty-five percent of surveyed network and cloud leaders report that AI workloads are driving an increase in enterprise network traffic.1
95%
Using Multiple Tools
The vast majority of respondents are juggling multiple tools, yet only 20% say their tools share data.1
69%
Wasting Time
More than two-thirds of respondents indicate that switching between tools is costing them time.1
Executive research insights
Four key findings on AI-era network performance
What 501 network and cloud leaders reveal about AI traffic, tool sprawl, and operational burnout.
The visibility paradox of tool sprawl
Running more monitoring utilities creates deeper blind spots and costly troubleshooting delays.
- Pervasive tool multiplication: Ninety-five percent of organizations rely on multiple tools for network visibility, with 46% deploying five or more separate monitoring systems.1
- Telemetry data isolation: Only 20% of organizations have environments where most or all of their network visibility tools actively share data with one another.1
- Severe productivity losses: When investigating live problems, the need to juggle multiple unintegrated consoles drains critical engineering time for 69% of network teams.1
- Manual swivel-chair correlation: Lacking cross-tool interoperability, engineers must manually cross-reference conflicting dashboards to assemble fragmented evidence by hand.
The operational perception gap
Disconnected telemetry views foster internal friction and blame rather than rapid resolution.
- Senior leadership awareness: Executives and directors are significantly more likely (51%) than front-line engineers (33%) to report that AI traffic has triggered network issues.1
- Conflicting operational lenses: While leadership observes cumulative business and application service degradation, front-line engineers only see localized device and link alarms.
- Widespread issue misdiagnosis: Incomplete operational visibility causes 61% of affected organizations to misdiagnose IT problems during AI traffic surges.1
- Escalating cross-team blame: More than half of surveyed leaders report increased finger-pointing between network, cloud, and application teams during outages.
The human toll and mean time to innocence (MTTI)
Defensive troubleshooting diverts resources and accelerates engineering burnout.
- Widespread engineering burnout: An overwhelming 94% of enterprise leaders report that their network teams are actively suffering from burnout.1
- The innocence defense tax: 83% of network engineers spend valuable operational time proving that complex application slowdowns are not caused by the network.1
- Compounding architectural complexity: Shifting workloads across AI, hybrid clouds, and SD-WAN represent the single largest contributor to operational fatigue.
- Critical skill retention risks: Repetitive manual troubleshooting and disjointed tool maintenance threaten to drive highly experienced senior engineers out of organizations.
Platform consolidation and AI-powered observability
Network operations teams are rationalizing tool portfolios in favor of unified intelligence and automation.
- Decisive tool rationalization: Eighty-eight percent of organizations are actively replacing point tools, while 87% have active or planned tool consolidation initiatives underway.1
- Measurable triage acceleration: Consolidating visibility delivers faster problem identification for 61% of teams and faster incident remediation for 56%.1
- Essential platform requirements: Enterprise buyers rank end-to-end path monitoring (60%), root cause analysis (58%), and AI assistance (52%) as their top platform capabilities.1
- Explainable AI diagnostics: Advanced observability platforms employ self-adjusting baselines to eliminate alarm noise while substantiating every recommendation with verifiable telemetry.
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Network Blind Spots at AI Speed
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FAQ
Frequently asked questions
Key technical questions for evaluating network observability solutions in modern enterprise environments.
Q: Why does AI-driven network traffic cause blind spots for enterprise network operations teams?
A: AI-driven network traffic causes blind spots because it dynamically traverses physical underlays, private clouds, ISP networks, and public cloud APIs that traditional monitoring tools cannot track end-to-end.
Conventional network monitoring was engineered for static, predictable client-server traffic rather than the bursty, multi-domain data access and agentic workflows characteristic of modern AI workloads.
Q: What is Mean Time to Innocence (MTTI) and how does it impact network operations?
A: Mean Time to Innocence (MTTI) is the operational time network engineers spend gathering telemetry evidence to prove that a service slowdown or application fault was not caused by the network.
Because 83% of teams regularly defend the network against misattributed blame, high MTTI wastes valuable engineering capacity and serves as a primary driver of the 94% burnout rate across IT organizations.1
Q: How does tool consolidation improve enterprise network performance?
A: Tool consolidation improves network performance by replacing disconnected point tools with a single source of truth that correlates device, flow, and hop-by-hop path telemetry.
By removing data silos and manual swivel-chair triage, consolidated platforms allow 61% of organizations to identify issues faster and 56% to remediate incidents more efficiently.1
Q: How does Broadcom Network Observability address AI-speed network degradation?
A: Broadcom Network Observability resolves AI-speed network degradation by providing hop-by-hop path monitoring, underlay-to-overlay correlation, and noise-reducing AI assistance in a single unified platform.
It extends visibility across private clouds such as VMware Cloud Foundation, public clouds, and third-party transit paths to deliver objective proof and accelerate root cause analysis.
1 Dimensional Research, "Network Blind Spots at AI Speed."