2026 NetOps Predictions - Part 1

APMdigest's Predictions Series continues with 2026 NetOps Predictions — industry experts offer predictions on how NetOps and Network Performance Management (NPM) will evolve and impact business in 2026.

Listen to Episode 20 of the MTTI Podcast: 2026 NetOps Predictions

AI-POWERED NPM

In 2026, network performance management (NPM) will be driven by more automation and software-defined approaches, aided by AI-driven predictive analytics and observability. Gartner® forecasts that 30% of enterprises will automate more than 50% of network tasks by 2026. NPM tools will couple with observability platforms to correlate network and application performance, such as in multi-cloud or SD-WAN environments that see a lot of flux. AI will also help proactively track and fix anomalies as well as optimize dynamic configurations to boost security and capacity planning.
Gowrisankar Chinnayan
Director of Product Management, ManageEngine

ANALYST REPORT: 2025 Gartner® Magic Quadrant™ for Digital Experience Monitoring

The next phase of AI in network operations won't be about replacing humans but about operationalizing AI so it provides continuous, trustworthy assistance — instruments that automate routine tasks while surfacing context and uncertainty for humans to act on. IT teams should be thinking about instrumenting telemetry, establishing fast feedback loops, and embedding AI-aware observability so AI becomes an operational advantage rather than an experiment.
Dan Zaniewski
Chief Technology Officer, Auvik

AGENTIC AI IN NETOPS

Agentic AI will play a significant role in automated network change management, including risk assessment, planning, execution, and post-change validation. It will lead to a significant reduction in human errors and network outages. Agentic AI is just now mature enough to perform this task reliably, and the benefits are significant. Roughly 40% of significant network outages are caused by human error (via the Uptime Institute).
Song Pang
CTO, NetBrain Technologies

GAO'S LAW

AI and automation will halve Mean Time to Repair (MTTR) every 12-18 months. I call it Gao's Law. This improvement will come from maturing AI and network automation technology, and improvements in network observability (including applications and security policies). More proactive monitoring and testing of redundancy systems, disaster recovery, and failover capabilities will contribute as well.
Lingping Gao
Founder and CEO, NetBrain Technologies

PREDICTIVE PERFORMANCE MANAGEMENT

From Reactive to Predictive — Networks Start Thinking Ahead: Visibility and automation are merging into proactive intelligence. Top capabilities desired by NetOps teams include intelligent traffic shaping (47%) and predictive analytics (46%). By 2026, predictive performance management will move from elite capability to operational baseline. AI will forecast congestion, latency, and degradation before they affect users, marking the rise of self-healing networks that anticipate, adapt, and act autonomously. Takeaway: The era of firefighting is ending. The next phase of networking is anticipatory — where resilience is built, not recovered.
Jeremy Rossbach
Chief Technical Evangelist, NetOps by Broadcom

DATA FOR AI TOOLS

AI tools for IT infrastructure monitoring will require three types of high-quality data to customize to each environment. First, raw data — the pure digital facts from network devices, topology, and historical records. Second, expert knowledge — the IT team's know-how and the intent for how the network should behave (the "why" behind the "what"). Finally, workflows — how the company operates, including manual processes, runbooks, and incident collaboration. Without all three types of data, AI tools have limited insight and cannot contribute towards a self-healing, autonomous network.
Lingping Gao
Founder and CEO, NetBrain Technologies

DISTRIBUTED END-TO-END OBSERVABILITY

Distributed, end-to-end observability — across cloud, edge, and on-prem — will move from "nice to have" to essential. As networks get more complex and distributed, unified discovery, contextual correlation, and automated remediation will be the capabilities that drive reliable, efficient, and resilient operations.
Douglas Murray
CEO, Auvik

INTEGRATION OF OBSERVABILITY WITH PROACTIVE GOVERNANCE

The next generation of observability platforms will correlate performance anomalies with network configuration drift, access changes, and policy violations, giving teams unified visibility across performance, reliability, and security posture. The organizations that integrate observability with proactive governance will shorten mean time to detection and resolution for both operational and security incidents.
Erez Tadmor
Field CTO, Tufin

EXPERIENCE INTELLIGENCE

Observability Evolves into Experience Intelligence
Trend: 87% of IT teams say that Internet and cloud dependencies create network blind spots. In 2026, observability platforms will evolve beyond traditional monitoring. Expect a new category: Experience Intelligence — platforms that merge user experience analytics, AI inference visibility, and network telemetry into one real-time pane of glass. This will enable leaders to understand how every AI-driven decision impacts human experience. It's not just about seeing packets move — it's about measuring satisfaction, latency, and productivity as business outcomes.
Jeremy Rossbach
Chief Technical Evangelist, NetOps by Broadcom

NETOPS ADOPTS DEVOPS PRACTICES

NetOps will move towards increasing adoption of GitOps and Infrastructure as Code practices in 2026 and beyond, shifting to declarative and automated network management. Networks will increasingly be version-controlled, with automated drift detection and deployment with sound configuration backing through observability platforms. AIOps will constantly track network data to autonomously optimize the configuration. This approach blends AI with DevOps principles to enhance network reliability and minimize manual interventions.
Gowrisankar Chinnayan
Director of Product Management, ManageEngine

NETDEVOPS

Operations have progressed from NetOps to DevOps to NetDevOps. Today's AIOps era is beginning to shift toward VibeOps, where autonomous digital coworkers become active participants in daily workflows. These non-biological teammates will reason, act, and operate with real agency through toolchains unified by a common protocol. With the Model Context Protocol emerging as the USB-C of software, these agents will soon plug into a vast ecosystem of robust tools they can use autonomously.
John Capobianco
Head of DevRel, Selector

NEW NETWORK PERFORMANCE BENCHMARK

Enterprises are investing in the wireless infrastructure needed to support AI, automation and data-intensive operations. Modernization is no longer an abstract roadmap item; it is a near-term requirement. At the same time, advanced use cases are setting a new performance benchmark for networks. Rising uplink demand and constant mobility mean designers must think about how to maximize success across indoor and outdoor environments. Enterprises that anticipate these requirements and strengthen their foundational wireless infrastructure today will be able to adopt today's existing automation and AI capabilities and be ready to scale for next-generation capabilities when they arrive.
Kelly Burroughs
Director of Strategy and Market Development, iBwave Solutions

AI READINESS = NETWORK VISIBILITY

Network Visibility Becomes the New KPI for AI Readiness
Trend: Nearly every organization (99%) now runs a cloud strategy, yet fewer than half say their network can handle the demands of AI workloads. In 2026, "AI readiness" will no longer refer to compute or data — it will mean visibility. Network teams will measure success not just in uptime or throughput, but in their ability to see, predict, and explain what's happening across public cloud, Internet, and edge environments. 95% of enterprises report blind spots in their network visibility, led by public cloud environments. Takeaway: Visibility is the new performance metric — and the foundation of trust in every AI initiative.
Jeremy Rossbach
Chief Technical Evangelist, NetOps by Broadcom

NETWORK DEFINES AI PERFORMANCE

The AI Infrastructure Stack Flips; By 2026, the network will define AI performance. AI training, inference, and data movement will stretch across regions and regulatory boundaries, and the real limiter won't be GPUs but interconnects across the entire AI ecosystem. As distributed AI fabrics emerge, success will depend on how intelligently data moves between compute nodes, not just how fast it's processed inside them. As such, the network will become the control plane of AI.

By 2026, the competitive edge in AI won't come from compute density alone, but from network design.
As AI workloads scale across distributed data centers, the ability to move, synchronize, and manage data efficiently will matter as much as raw compute. Metro-scale and long-haul fiber will define the winners of distributed AI — those who can interconnect and orchestrate data across regions, clouds, and edges. The next wave of AI leadership won't be won in the data center alone, but across the networks that connect them.
James Tomko
SVP of Digital Infrastructure, Zayo

Go to: 2026 NetOps Predictions - Part 2

Related Links

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The Latest

Enterprise Edge AI Reaches Inflection Point

Edge AI is strategically embedded in core IT and infrastructure spending across industries, according to the 2026 Edge AI Survey from ZEDEDA. The research shows that 83% of C-suite and IT executive respondents say edge AI is important to their core business strategy ...

AI Is Hitting Operational Limits

As AI adoption accelerates, operational complexity — not model intelligence — is becoming the primary barrier to reliable AI at scale, according to the State of AI Engineering 2026 from Datadog ... The report highlights a compounding complexity challenge as AI systems scale ... Around 5% of AI model requests fail in production, with nearly 60% of those failures caused by capacity limits ...

Alert Fatigue Is No Longer a Morale Problem, It's a Reliability Risk and a System Failure

For years, production operations teams have treated alert fatigue as a quality-of-life problem: something that makes on-call rotations miserable but isn't considered a direct contributor to outages. That framing doesn't capture how these systems fail, and we now have data to show why. More importantly, it's now clear alert fatigue is a symptom of a deeper issue: production systems have outgrown the current operational approaches ...

Most Enterprises Think They Can Switch AI Vendors in a Month ... Most Who've Tried Couldn't

I was on a customer call last fall when an enterprise architect said something I haven't been able to shake. Her team had just spent four months trying to swap one AI vendor for another. The original plan said three weeks. "We didn't switch vendors," she told me. "We rebuilt half our integrations and discovered what we'd actually been depending on." Most enterprise leaders don't expect that to be the experience ...

Your Observability Stack Has a Telemetry Pipeline Problem

Ask any senior SRE or platform engineer what keeps them up at night, and the answer probably isn't the monitoring tool — it's the data feeding it. The proliferation of APM, observability, and AIOps platforms has created a telemetry sprawl problem that most teams manage reactively rather than architect proactively. Metrics are going to one platform. Traces routed somewhere else. Logs duplicated across multiple backends because nobody wants to be caught without them when something breaks. Every redundant stream costs money ...

Operator to Orchestrator: 80% of IT Pros See Shift in Role as AI Permeates Workflows

80% of respondents agree that the IT role is shifting from operators to orchestrators, according to the 2026 IT Trends Report: The Human Side of Autonomous IT from SolarWinds ...

Gartner: 40% of Organizations Deploying AI Will Use AI Observability to Monitor Model Performance by 2028

40% of organizations deploying AI will implement dedicated AI observability tools by 2028 to monitor model performance, bias and outputs, according to Gartner ...

Almost Half of AI-Generated Code Fails in Production

Until AI-powered engineering tools have live visibility of how code behaves at runtime, they cannot be trusted to autonomously ensure reliable systems, according to the State of AI-Powered Engineering Report 2026 report from Lightrun. The report reveals that a major volume of manual work is required when AI-generated code is deployed: 43% of AI-generated code requires manual debugging in production, even after passing QA or staging tests. Furthermore, an average of three manual redeploy cycles are required to verify a single AI-suggested code fix in production ...

AI Requires Design, Not Edicts

Many organizations describe AI as strategic, but they do not manage it strategically. When AI plans are disconnected from strategy, detached from organizational learning, and protected from serious assumptions testing, the problem is no longer technical immaturity; it is a failure of management discipline ... Executives too often tell organizations to "use AI" before they define what AI is supposed to change. The problem deepens in organizations where strategy isn't well articulated in the first place ...

How Intelligent Orchestration Enables Enterprises to Move from AI POC to AI Production

Across the enterprise technology landscape, a quiet crisis is playing out. Organizations have run hundreds, sometimes thousands, of generative AI pilots. Leadership has celebrated the proof of concept (POCs) ... Industry experience points to a sobering reality: only 5-10% of AI POCs that progress to the pilot stage successfully reach scaled production. The remaining 90% fail because the enterprise environment around them was never ready to absorb them, not the AI models ...

2026 NetOps Predictions - Part 1

APMdigest's Predictions Series continues with 2026 NetOps Predictions — industry experts offer predictions on how NetOps and Network Performance Management (NPM) will evolve and impact business in 2026.

Listen to Episode 20 of the MTTI Podcast: 2026 NetOps Predictions

AI-POWERED NPM

In 2026, network performance management (NPM) will be driven by more automation and software-defined approaches, aided by AI-driven predictive analytics and observability. Gartner® forecasts that 30% of enterprises will automate more than 50% of network tasks by 2026. NPM tools will couple with observability platforms to correlate network and application performance, such as in multi-cloud or SD-WAN environments that see a lot of flux. AI will also help proactively track and fix anomalies as well as optimize dynamic configurations to boost security and capacity planning.
Gowrisankar Chinnayan
Director of Product Management, ManageEngine

ANALYST REPORT: 2025 Gartner® Magic Quadrant™ for Digital Experience Monitoring

The next phase of AI in network operations won't be about replacing humans but about operationalizing AI so it provides continuous, trustworthy assistance — instruments that automate routine tasks while surfacing context and uncertainty for humans to act on. IT teams should be thinking about instrumenting telemetry, establishing fast feedback loops, and embedding AI-aware observability so AI becomes an operational advantage rather than an experiment.
Dan Zaniewski
Chief Technology Officer, Auvik

AGENTIC AI IN NETOPS

Agentic AI will play a significant role in automated network change management, including risk assessment, planning, execution, and post-change validation. It will lead to a significant reduction in human errors and network outages. Agentic AI is just now mature enough to perform this task reliably, and the benefits are significant. Roughly 40% of significant network outages are caused by human error (via the Uptime Institute).
Song Pang
CTO, NetBrain Technologies

GAO'S LAW

AI and automation will halve Mean Time to Repair (MTTR) every 12-18 months. I call it Gao's Law. This improvement will come from maturing AI and network automation technology, and improvements in network observability (including applications and security policies). More proactive monitoring and testing of redundancy systems, disaster recovery, and failover capabilities will contribute as well.
Lingping Gao
Founder and CEO, NetBrain Technologies

PREDICTIVE PERFORMANCE MANAGEMENT

From Reactive to Predictive — Networks Start Thinking Ahead: Visibility and automation are merging into proactive intelligence. Top capabilities desired by NetOps teams include intelligent traffic shaping (47%) and predictive analytics (46%). By 2026, predictive performance management will move from elite capability to operational baseline. AI will forecast congestion, latency, and degradation before they affect users, marking the rise of self-healing networks that anticipate, adapt, and act autonomously. Takeaway: The era of firefighting is ending. The next phase of networking is anticipatory — where resilience is built, not recovered.
Jeremy Rossbach
Chief Technical Evangelist, NetOps by Broadcom

DATA FOR AI TOOLS

AI tools for IT infrastructure monitoring will require three types of high-quality data to customize to each environment. First, raw data — the pure digital facts from network devices, topology, and historical records. Second, expert knowledge — the IT team's know-how and the intent for how the network should behave (the "why" behind the "what"). Finally, workflows — how the company operates, including manual processes, runbooks, and incident collaboration. Without all three types of data, AI tools have limited insight and cannot contribute towards a self-healing, autonomous network.
Lingping Gao
Founder and CEO, NetBrain Technologies

DISTRIBUTED END-TO-END OBSERVABILITY

Distributed, end-to-end observability — across cloud, edge, and on-prem — will move from "nice to have" to essential. As networks get more complex and distributed, unified discovery, contextual correlation, and automated remediation will be the capabilities that drive reliable, efficient, and resilient operations.
Douglas Murray
CEO, Auvik

INTEGRATION OF OBSERVABILITY WITH PROACTIVE GOVERNANCE

The next generation of observability platforms will correlate performance anomalies with network configuration drift, access changes, and policy violations, giving teams unified visibility across performance, reliability, and security posture. The organizations that integrate observability with proactive governance will shorten mean time to detection and resolution for both operational and security incidents.
Erez Tadmor
Field CTO, Tufin

EXPERIENCE INTELLIGENCE

Observability Evolves into Experience Intelligence
Trend: 87% of IT teams say that Internet and cloud dependencies create network blind spots. In 2026, observability platforms will evolve beyond traditional monitoring. Expect a new category: Experience Intelligence — platforms that merge user experience analytics, AI inference visibility, and network telemetry into one real-time pane of glass. This will enable leaders to understand how every AI-driven decision impacts human experience. It's not just about seeing packets move — it's about measuring satisfaction, latency, and productivity as business outcomes.
Jeremy Rossbach
Chief Technical Evangelist, NetOps by Broadcom

NETOPS ADOPTS DEVOPS PRACTICES

NetOps will move towards increasing adoption of GitOps and Infrastructure as Code practices in 2026 and beyond, shifting to declarative and automated network management. Networks will increasingly be version-controlled, with automated drift detection and deployment with sound configuration backing through observability platforms. AIOps will constantly track network data to autonomously optimize the configuration. This approach blends AI with DevOps principles to enhance network reliability and minimize manual interventions.
Gowrisankar Chinnayan
Director of Product Management, ManageEngine

NETDEVOPS

Operations have progressed from NetOps to DevOps to NetDevOps. Today's AIOps era is beginning to shift toward VibeOps, where autonomous digital coworkers become active participants in daily workflows. These non-biological teammates will reason, act, and operate with real agency through toolchains unified by a common protocol. With the Model Context Protocol emerging as the USB-C of software, these agents will soon plug into a vast ecosystem of robust tools they can use autonomously.
John Capobianco
Head of DevRel, Selector

NEW NETWORK PERFORMANCE BENCHMARK

Enterprises are investing in the wireless infrastructure needed to support AI, automation and data-intensive operations. Modernization is no longer an abstract roadmap item; it is a near-term requirement. At the same time, advanced use cases are setting a new performance benchmark for networks. Rising uplink demand and constant mobility mean designers must think about how to maximize success across indoor and outdoor environments. Enterprises that anticipate these requirements and strengthen their foundational wireless infrastructure today will be able to adopt today's existing automation and AI capabilities and be ready to scale for next-generation capabilities when they arrive.
Kelly Burroughs
Director of Strategy and Market Development, iBwave Solutions

AI READINESS = NETWORK VISIBILITY

Network Visibility Becomes the New KPI for AI Readiness
Trend: Nearly every organization (99%) now runs a cloud strategy, yet fewer than half say their network can handle the demands of AI workloads. In 2026, "AI readiness" will no longer refer to compute or data — it will mean visibility. Network teams will measure success not just in uptime or throughput, but in their ability to see, predict, and explain what's happening across public cloud, Internet, and edge environments. 95% of enterprises report blind spots in their network visibility, led by public cloud environments. Takeaway: Visibility is the new performance metric — and the foundation of trust in every AI initiative.
Jeremy Rossbach
Chief Technical Evangelist, NetOps by Broadcom

NETWORK DEFINES AI PERFORMANCE

The AI Infrastructure Stack Flips; By 2026, the network will define AI performance. AI training, inference, and data movement will stretch across regions and regulatory boundaries, and the real limiter won't be GPUs but interconnects across the entire AI ecosystem. As distributed AI fabrics emerge, success will depend on how intelligently data moves between compute nodes, not just how fast it's processed inside them. As such, the network will become the control plane of AI.

By 2026, the competitive edge in AI won't come from compute density alone, but from network design.
As AI workloads scale across distributed data centers, the ability to move, synchronize, and manage data efficiently will matter as much as raw compute. Metro-scale and long-haul fiber will define the winners of distributed AI — those who can interconnect and orchestrate data across regions, clouds, and edges. The next wave of AI leadership won't be won in the data center alone, but across the networks that connect them.
James Tomko
SVP of Digital Infrastructure, Zayo

Go to: 2026 NetOps Predictions - Part 2

Related Links

Hot Topics

The Latest

Enterprise Edge AI Reaches Inflection Point

Edge AI is strategically embedded in core IT and infrastructure spending across industries, according to the 2026 Edge AI Survey from ZEDEDA. The research shows that 83% of C-suite and IT executive respondents say edge AI is important to their core business strategy ...

AI Is Hitting Operational Limits

As AI adoption accelerates, operational complexity — not model intelligence — is becoming the primary barrier to reliable AI at scale, according to the State of AI Engineering 2026 from Datadog ... The report highlights a compounding complexity challenge as AI systems scale ... Around 5% of AI model requests fail in production, with nearly 60% of those failures caused by capacity limits ...

Alert Fatigue Is No Longer a Morale Problem, It's a Reliability Risk and a System Failure

For years, production operations teams have treated alert fatigue as a quality-of-life problem: something that makes on-call rotations miserable but isn't considered a direct contributor to outages. That framing doesn't capture how these systems fail, and we now have data to show why. More importantly, it's now clear alert fatigue is a symptom of a deeper issue: production systems have outgrown the current operational approaches ...

Most Enterprises Think They Can Switch AI Vendors in a Month ... Most Who've Tried Couldn't

I was on a customer call last fall when an enterprise architect said something I haven't been able to shake. Her team had just spent four months trying to swap one AI vendor for another. The original plan said three weeks. "We didn't switch vendors," she told me. "We rebuilt half our integrations and discovered what we'd actually been depending on." Most enterprise leaders don't expect that to be the experience ...

Your Observability Stack Has a Telemetry Pipeline Problem

Ask any senior SRE or platform engineer what keeps them up at night, and the answer probably isn't the monitoring tool — it's the data feeding it. The proliferation of APM, observability, and AIOps platforms has created a telemetry sprawl problem that most teams manage reactively rather than architect proactively. Metrics are going to one platform. Traces routed somewhere else. Logs duplicated across multiple backends because nobody wants to be caught without them when something breaks. Every redundant stream costs money ...

Operator to Orchestrator: 80% of IT Pros See Shift in Role as AI Permeates Workflows

80% of respondents agree that the IT role is shifting from operators to orchestrators, according to the 2026 IT Trends Report: The Human Side of Autonomous IT from SolarWinds ...

Gartner: 40% of Organizations Deploying AI Will Use AI Observability to Monitor Model Performance by 2028

40% of organizations deploying AI will implement dedicated AI observability tools by 2028 to monitor model performance, bias and outputs, according to Gartner ...

Almost Half of AI-Generated Code Fails in Production

Until AI-powered engineering tools have live visibility of how code behaves at runtime, they cannot be trusted to autonomously ensure reliable systems, according to the State of AI-Powered Engineering Report 2026 report from Lightrun. The report reveals that a major volume of manual work is required when AI-generated code is deployed: 43% of AI-generated code requires manual debugging in production, even after passing QA or staging tests. Furthermore, an average of three manual redeploy cycles are required to verify a single AI-suggested code fix in production ...

AI Requires Design, Not Edicts

Many organizations describe AI as strategic, but they do not manage it strategically. When AI plans are disconnected from strategy, detached from organizational learning, and protected from serious assumptions testing, the problem is no longer technical immaturity; it is a failure of management discipline ... Executives too often tell organizations to "use AI" before they define what AI is supposed to change. The problem deepens in organizations where strategy isn't well articulated in the first place ...

How Intelligent Orchestration Enables Enterprises to Move from AI POC to AI Production

Across the enterprise technology landscape, a quiet crisis is playing out. Organizations have run hundreds, sometimes thousands, of generative AI pilots. Leadership has celebrated the proof of concept (POCs) ... Industry experience points to a sobering reality: only 5-10% of AI POCs that progress to the pilot stage successfully reach scaled production. The remaining 90% fail because the enterprise environment around them was never ready to absorb them, not the AI models ...