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

The Real Enterprise AI Risk Isn't Hallucinations

Ask most IT leaders about their biggest concern with AI and you'll hear the same answer: hallucinations ... Today, however, the conversation has shifted ... As organizations move beyond chatbots and experiments, they are increasingly deploying AI agents that perform multi-step tasks. These systems retrieve documents, query databases, call APIs, generate reports, write code, and make recommendations. The issue is not whether the model can reason. The issue is whether the organization can see, verify, and govern the decisions being made along the way ...

Beyond Buy vs. Build: The New Playbook for Data Pipelines

While organizations want to take control of their telemetry, building telemetry pipelines from scratch can be a very daunting, complicated task, even when leveraging open-source standards like OpenTelemetry. It requires specialized knowledge across distributed systems, data engineering, and security. This fragmented approach across systems causes higher operational costs; it puts a strain on resources and reduces efficiency as teams have to work with different interfaces and processes ...

The Corporate Network Is Fading - Here's What Replaces It

For decades, enterprise networks were designed around a simple assumption: work happened inside the office. Applications lived in centralized data centers, employees connected through internal infrastructure, and security focused on protecting the perimeter that surrounded everything ... But the way organizations operate today bears little resemblance to that environment. Cloud platforms host critical applications, employees connect from homes and airports as often as they do from offices, and partners collaborate through shared systems that exist far beyond corporate walls. In short, the corporate network no longer resembles the environment it was designed to protect ...

IT Leaders Must Establish a Network Data Architecture Practice

As an analyst who researches how IT organizations design, build, and operate their networks, I find that network data is a constant source of pain. Network teams struggle with data quality, fragmentation, authority, access, and trust. And these issues undermine everything they try to do. Here are the numbers: Only 45% of network teams are completely confident in the accuracy of their network source of truth, which documents the intent of their network ...

Data Center Outages Are Decreasing but More Costly

The 2026 Global Data Center Survey from Uptime Institute reveals an industry navigating workforce constraints, escalating outage expenses, even as rising costs remain the top concern for management teams ...

Observability Has a Physical Layer Now: What AI Infrastructure Teaches IT Operations

The next observability gap may not be in the code. It may be under the rack. That sounds strange until you think about how AI incidents actually feel in the middle of an investigation ... The application dashboard may be accurate. It may also be stopping at the wrong boundary. AI systems depend on software, but they also depend on a dense physical stack: racks, power paths, thermal margin, maintenance activity and, in many environments, liquid cooling. Those physical dependencies can change slowly before they look like a software incident ...

TLS Certificate Expiration Is Becoming an Observability Problem

Certificate expiration is the rare outage you can see coming. Every TLS certificate carries the date it stops working, so the moment it will begin breaking connections is knowable in advance. That's what makes an expired certificate such a frustrating way to lose a service. What's changing now is how often that date comes around ...

Enterprise Cloud Observability: Choosing the Right Starting Point

Enterprises operate different combinations of workloads across cloud, hybrid and multicloud environments. For business-critical workloads, teams need to consider monitoring and observability early so they can detect health issues, investigate failures, and understand operational impact. Organizations place workloads on cloud platforms based on a combination of technical requirements, economics, existing dependencies, organizational standards, and business priorities. Their monitoring priorities therefore depend on what they operate and where those systems run. Those priorities will not look the same for every organization ...

AI Is Making Incident Management More Complex - Bad Data Makes It Harder

Top-performing businesses prioritize data-driven decision making, enabling leaders to move from intuition and gut feel towards evidence-based judgment. But that judgment is only sound when the data underpinning decisions is accurate. With incident management, data accuracy is particularly important. Long-term revenue, customer trust, and operational stability depend on high-quality data that enables teams to quickly identify and address the root cause of major incidents. Against this backdrop, governance becomes a critical endeavor to ensure the right data drives the right action ...

MEAN TIME TO INSIGHT Podcast - Episode 26: Network Compliance

In MEAN TIME TO INSIGHT Episode 26, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network compliance ... 

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

The Real Enterprise AI Risk Isn't Hallucinations

Ask most IT leaders about their biggest concern with AI and you'll hear the same answer: hallucinations ... Today, however, the conversation has shifted ... As organizations move beyond chatbots and experiments, they are increasingly deploying AI agents that perform multi-step tasks. These systems retrieve documents, query databases, call APIs, generate reports, write code, and make recommendations. The issue is not whether the model can reason. The issue is whether the organization can see, verify, and govern the decisions being made along the way ...

Beyond Buy vs. Build: The New Playbook for Data Pipelines

While organizations want to take control of their telemetry, building telemetry pipelines from scratch can be a very daunting, complicated task, even when leveraging open-source standards like OpenTelemetry. It requires specialized knowledge across distributed systems, data engineering, and security. This fragmented approach across systems causes higher operational costs; it puts a strain on resources and reduces efficiency as teams have to work with different interfaces and processes ...

The Corporate Network Is Fading - Here's What Replaces It

For decades, enterprise networks were designed around a simple assumption: work happened inside the office. Applications lived in centralized data centers, employees connected through internal infrastructure, and security focused on protecting the perimeter that surrounded everything ... But the way organizations operate today bears little resemblance to that environment. Cloud platforms host critical applications, employees connect from homes and airports as often as they do from offices, and partners collaborate through shared systems that exist far beyond corporate walls. In short, the corporate network no longer resembles the environment it was designed to protect ...

IT Leaders Must Establish a Network Data Architecture Practice

As an analyst who researches how IT organizations design, build, and operate their networks, I find that network data is a constant source of pain. Network teams struggle with data quality, fragmentation, authority, access, and trust. And these issues undermine everything they try to do. Here are the numbers: Only 45% of network teams are completely confident in the accuracy of their network source of truth, which documents the intent of their network ...

Data Center Outages Are Decreasing but More Costly

The 2026 Global Data Center Survey from Uptime Institute reveals an industry navigating workforce constraints, escalating outage expenses, even as rising costs remain the top concern for management teams ...

Observability Has a Physical Layer Now: What AI Infrastructure Teaches IT Operations

The next observability gap may not be in the code. It may be under the rack. That sounds strange until you think about how AI incidents actually feel in the middle of an investigation ... The application dashboard may be accurate. It may also be stopping at the wrong boundary. AI systems depend on software, but they also depend on a dense physical stack: racks, power paths, thermal margin, maintenance activity and, in many environments, liquid cooling. Those physical dependencies can change slowly before they look like a software incident ...

TLS Certificate Expiration Is Becoming an Observability Problem

Certificate expiration is the rare outage you can see coming. Every TLS certificate carries the date it stops working, so the moment it will begin breaking connections is knowable in advance. That's what makes an expired certificate such a frustrating way to lose a service. What's changing now is how often that date comes around ...

Enterprise Cloud Observability: Choosing the Right Starting Point

Enterprises operate different combinations of workloads across cloud, hybrid and multicloud environments. For business-critical workloads, teams need to consider monitoring and observability early so they can detect health issues, investigate failures, and understand operational impact. Organizations place workloads on cloud platforms based on a combination of technical requirements, economics, existing dependencies, organizational standards, and business priorities. Their monitoring priorities therefore depend on what they operate and where those systems run. Those priorities will not look the same for every organization ...

AI Is Making Incident Management More Complex - Bad Data Makes It Harder

Top-performing businesses prioritize data-driven decision making, enabling leaders to move from intuition and gut feel towards evidence-based judgment. But that judgment is only sound when the data underpinning decisions is accurate. With incident management, data accuracy is particularly important. Long-term revenue, customer trust, and operational stability depend on high-quality data that enables teams to quickly identify and address the root cause of major incidents. Against this backdrop, governance becomes a critical endeavor to ensure the right data drives the right action ...

MEAN TIME TO INSIGHT Podcast - Episode 26: Network Compliance

In MEAN TIME TO INSIGHT Episode 26, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network compliance ...