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

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I've spent a lot of time in the channel, and one thing I keep coming back to is this: a partner program is only as good as what it looks like in the field. Many programs look great on paper, but when a partner is in front of a customer navigating a complex hybrid environment or trying to make the case for AI-powered observability, the gap between what a vendor promises and what it actually delivers becomes very clear, very fast ...

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Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...

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For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...

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Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...

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Let me start with something I've seen play out more times than I can count. A team hits a wall with the cloud. Costs creep up, then spike. Performance starts to feel inconsistent. Someone in finance asks a simple question like "why did this double?" and nobody has a clean answer ... Maybe this isn't the right place for everything. That realization feels like a breakthrough, like you've identified the problem. In reality, you've just identified the starting line ...

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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

Building AI Agents That Build Trust

As AI moves from generating responses to performing actions, the need for trust increases exponentially. And as organizations enlist AI agents for increasingly sophisticated business processes, trust is going to be the single most important theme for spurring adoption. What can organizations do to build trustworthy AI agents? ...

Keys to Building a Partner Ecosystem That Scales

I've spent a lot of time in the channel, and one thing I keep coming back to is this: a partner program is only as good as what it looks like in the field. Many programs look great on paper, but when a partner is in front of a customer navigating a complex hybrid environment or trying to make the case for AI-powered observability, the gap between what a vendor promises and what it actually delivers becomes very clear, very fast ...

How AI Agents Are Reshaping DataOps for the Always-On Enterprise

Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...

AI Deepfakes: Rethinking Trust in the Workplace

For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...

Nine in Ten Enterprises Plan Cloud Data Repatriation amid Rising Cloud Costs and Data Sovereignty Mandates

Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...

Why ITOps Need Right-Sized AI, Not Bigger Models

Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...

The End of Reactive DevOps: AI-Driven Observability for Zero-Defect Digital Systems

For years, DevOps teams operated under a simple assumption: collect enough telemetry, and you can find and fix any problem. That assumption is breaking down. Modern enterprises now operate across microservices, hybrid cloud environments, APIs, Kubernetes, and highly automated delivery pipelines. Releases happen continuously, dependencies shift constantly, and failures spread faster than teams can diagnose them ...

5 Takeaways from the Observability Forecast for Media and Entertainment

New Relic surveyed IT and engineering leaders from the media and entertainment (M&E) sector to understand what's working — and where challenges persist with their observability practices. The findings reveal how M&E organizations are navigating rising platform complexity, audience expectations, and AI-driven change. Below are five takeaways that stand out ...

Why Moving Off the Cloud Is the Easy Part and What Comes Next Is Where Things Get Hard

Let me start with something I've seen play out more times than I can count. A team hits a wall with the cloud. Costs creep up, then spike. Performance starts to feel inconsistent. Someone in finance asks a simple question like "why did this double?" and nobody has a clean answer ... Maybe this isn't the right place for everything. That realization feels like a breakthrough, like you've identified the problem. In reality, you've just identified the starting line ...

MEAN TIME TO INSIGHT Podcast - Episode 24: Network Observability Tool Sprawl

In MEAN TIME TO INSIGHT Episode 24, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network observability tool sprawl ...