2026 NetOps Predictions - Part 2

Industry experts offer predictions on how NetOps and Network Performance Management (NPM) will evolve and impact business in 2026. Part 2 covers NetOps challenges and the edge.

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

NETOPS CHALLENGE: TRAINING AI

Training AI is about to give corporate networks a workout. With more companies adopting agents creating AI apps, the onus will be on IT and NetOps to condition their networks for the big lift in training AI. When AI apps are in learning mode they can access terabytes or petabytes of data very quickly, and they need high speeds to do it. Companies may need to alter their architecture to leverage the GPU on user machines, and create a time-sharing GPU infrastructure that distributes the AI processing towards users of AI rather than centralized data centers. With AI-capable devices and laptops taking some of the load, all users will get a better experience.
Prakash Mana
CEO, Cloudbrink

WEBINAR: Beyond the VPN - Why ZTNA Alone Isn't Enough — and What's Next 

NETOPS CHALLENGE: PHYSICAL SPACE

Addressing The Real Network Bottleneck - Physical Space: AI networks use dramatically more fiber than traditional cloud systems — in fact, ten times more in the GPU back-end alone — and they require predictable power distribution across expanding footprints. In 2026, operators will concentrate on extracting more capacity from the assets they already have, whether that's upgrading existing long-haul routes with low-loss fiber or maximizing conduit and rack space with high-density cabling. This is where glass becomes the hidden performance engine: how far, how densely, and how efficiently data can travel will hinge on advancements in fiber design. Instead of asking, "How fast is the link?" operators will increasingly ask, "How can we increase network capacity can we fit into the space we already have?" 
Brian Rhoney
Data Center Market Development Director, Corning

NETOPS CHALLENGE: WORKFORCE SHORTAGE

Because There Aren't Enough Technicians, Networks Must Become Easier to Build: One of the biggest challenges next year will be the shortage of trained installers. AI networks are growing faster than the engineering workforce can support. In 2026, more operators will adopt plug-and-play, modular, and error-resistant fiber systems that reduce the need for highly specialized labor. This shift isn't just about efficiency — it's about survival. Without simpler, faster ways to connect high-density systems, AI buildouts will hit deployment bottlenecks long before they hit hardware limits. These solutions will speed up installation, reduce mistakes, and help teams build larger networks with fewer technicians.
Brian Rhoney
Data Center Market Development Director, Corning

NETOPS CHALLENGE: CONNECTED DEVICES

More connected devices on more people will put security to the test. Personal connected devices like smart glasses, translation capable airpods, and personal robots will put more load on already straining networks and require new security processes and protocols. IT will need to compensate for the increase in PII in video, audio, and other formats, while maintaining an excellent user experience for employees on their networks. 
Prakash Mana
CEO, Cloudbrink

NETOPS CHALLENGE: DEEPFAKE ATTACKS

Deepfake-driven attacks will become the norm in the corporate world as cybercriminals embrace AI. Imagine attacks that use real-time voice and video cloning to impersonate executives, or fake "live" Zoom/Teams scams, or AI-written business email compromise (BEC) attacks that adapt mid-conversation. If you can imagine it, cybercriminals can do it. Not only are these attacks more difficult to detect, they are cheaper and easier for criminals who can now focus on compromising people to get at a company. Add these individual AI attacks to employees that work from anywhere and it becomes critical for corporate security controls to move away from protecting just the office or the organization with perimeter or network security. Every user, and every device, should be verified every time, regardless of location.
Prakash Mana
CEO, Cloudbrink

NETOPS CONVERGES WITH SECOPS

NetOps will (hopefully) fully converge with SecOps under a single goal: maintaining secure network intent across hybrid infrastructure. As automation deepens, network teams will adopt observability models that continuously validate connectivity, performance, and compliance. I believe that by 2026, successful NetOps organizations will rely on real-time topology awareness and policy-driven automation to reduce both downtime and exposure windows, ensuring agility doesn't come at the cost of control.
Erez Tadmor
Field CTO, Tufin

CLOUD-LIKE EXPERIENCE FOR THE EDGE

Edge computing will become a bigger part of the narrative in 2026. It's been reported, but my conversations with clients are starting to lean heavily on how to bring cloud-like experiences into a hybrid edge environment. AI, whether generative, agentic, or traditional, is becoming a bigger part of the conversation at the edge. Innovating, managing, and scaling solutions for a large fleet of devices/locations will be a big ask and clients want a similar experience for those environments as they have with their cloud operations.
Juan Orlandini
Chief Technology Officer, North America, Insight Enterprises

FRONTIER EDGE

The industry's definition of "the Edge" is now obsolete; 2026 is here, and so is the "Frontier Edge." This shift is driven by several compounding pressures: the explosion of AI-generated content, the massive amount of data for inference (from immersive 8K media to critical AI model updates), and the necessity of connecting the never thought before locations, such as the deep sea, outer space, and the quantum world. However, consumer immersive traffic always takes center stage and competes with critical data, leading to a choke point within cellular 5G, Wi-Fi, and satellite networks. Avoiding this contention requires an immediate architectural shift away from congested and designed for one-to-one communication systems (e.g., cellular 5G, Wi-Fi, satellite), to a scalable, one-to-many distribution like the broadcast networks to ensure seamless and reliable connectivity in the Frontier Edge era.
Apoorva Jain
CPO, EdgeBeam Wireless

DIGITAL TWINS

The digital twin is evolving from a visualization tool into a practical workspace for network planning. It's becoming the operational backbone that unifies teams, accelerates design cycles and drives smarter decisions throughout the entire lifecycle of a network. Although still in the early stages, digital twins are rapidly evolving into a key enabler for AI-driven network lifecycle management, powering faster and more precise strategic planning.
Kelly Burroughs
Director of Strategy and Market Development, iBwave Solutions

WORK ANYTIME

Work from anywhere will become work anytime. Back-to-office mandates have pulled many workers back to the office, but WFH habits die hard. Many tech workers are used to logging in at times convenient for their schedule or work habits. Our usage data early this year showed heavy transfer of data on Fridays, an indication that "work from anywhere" employees actually put in longer hours than their "9 to 5" counterparts — with heavy usage starting at 7:00 am and continuing to 7:00 pm. In 2026 we expect to see more workers logging in both at the office and at home in their off-hours, which may temporarily increase productivity, but burn workers out more quickly. Companies will need to focus on worker experience as well as productivity.
Prakash Mana
CEO, Cloudbrink

Go to: 2026 Cloud Predictions

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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 ...

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 ...

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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 ... 

Capacity Isn't a Guess: Observability-Driven Sizing for On-Prem Databases

In cloud-native systems, scaling is often as simple as moving a slider. For on-premise databases, the stakes are different. Over-provisioning hardware is expensive. Under-provisioning leads to performance bottlenecks that are difficult to fix once the equipment is in the rack ...

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When most people think about cybersecurity, they picture firewalls, encryption, and access controls — technical tools designed to protect systems and data. But beneath the technology lies a deeper set of principles about trust, decision-making, and resilience ... The best leaders don't eliminate risk. They manage it intelligently. And in many ways, cybersecurity offers a surprisingly useful playbook for doing exactly that ...

2026 NetOps Predictions - Part 2

Industry experts offer predictions on how NetOps and Network Performance Management (NPM) will evolve and impact business in 2026. Part 2 covers NetOps challenges and the edge.

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

NETOPS CHALLENGE: TRAINING AI

Training AI is about to give corporate networks a workout. With more companies adopting agents creating AI apps, the onus will be on IT and NetOps to condition their networks for the big lift in training AI. When AI apps are in learning mode they can access terabytes or petabytes of data very quickly, and they need high speeds to do it. Companies may need to alter their architecture to leverage the GPU on user machines, and create a time-sharing GPU infrastructure that distributes the AI processing towards users of AI rather than centralized data centers. With AI-capable devices and laptops taking some of the load, all users will get a better experience.
Prakash Mana
CEO, Cloudbrink

WEBINAR: Beyond the VPN - Why ZTNA Alone Isn't Enough — and What's Next 

NETOPS CHALLENGE: PHYSICAL SPACE

Addressing The Real Network Bottleneck - Physical Space: AI networks use dramatically more fiber than traditional cloud systems — in fact, ten times more in the GPU back-end alone — and they require predictable power distribution across expanding footprints. In 2026, operators will concentrate on extracting more capacity from the assets they already have, whether that's upgrading existing long-haul routes with low-loss fiber or maximizing conduit and rack space with high-density cabling. This is where glass becomes the hidden performance engine: how far, how densely, and how efficiently data can travel will hinge on advancements in fiber design. Instead of asking, "How fast is the link?" operators will increasingly ask, "How can we increase network capacity can we fit into the space we already have?" 
Brian Rhoney
Data Center Market Development Director, Corning

NETOPS CHALLENGE: WORKFORCE SHORTAGE

Because There Aren't Enough Technicians, Networks Must Become Easier to Build: One of the biggest challenges next year will be the shortage of trained installers. AI networks are growing faster than the engineering workforce can support. In 2026, more operators will adopt plug-and-play, modular, and error-resistant fiber systems that reduce the need for highly specialized labor. This shift isn't just about efficiency — it's about survival. Without simpler, faster ways to connect high-density systems, AI buildouts will hit deployment bottlenecks long before they hit hardware limits. These solutions will speed up installation, reduce mistakes, and help teams build larger networks with fewer technicians.
Brian Rhoney
Data Center Market Development Director, Corning

NETOPS CHALLENGE: CONNECTED DEVICES

More connected devices on more people will put security to the test. Personal connected devices like smart glasses, translation capable airpods, and personal robots will put more load on already straining networks and require new security processes and protocols. IT will need to compensate for the increase in PII in video, audio, and other formats, while maintaining an excellent user experience for employees on their networks. 
Prakash Mana
CEO, Cloudbrink

NETOPS CHALLENGE: DEEPFAKE ATTACKS

Deepfake-driven attacks will become the norm in the corporate world as cybercriminals embrace AI. Imagine attacks that use real-time voice and video cloning to impersonate executives, or fake "live" Zoom/Teams scams, or AI-written business email compromise (BEC) attacks that adapt mid-conversation. If you can imagine it, cybercriminals can do it. Not only are these attacks more difficult to detect, they are cheaper and easier for criminals who can now focus on compromising people to get at a company. Add these individual AI attacks to employees that work from anywhere and it becomes critical for corporate security controls to move away from protecting just the office or the organization with perimeter or network security. Every user, and every device, should be verified every time, regardless of location.
Prakash Mana
CEO, Cloudbrink

NETOPS CONVERGES WITH SECOPS

NetOps will (hopefully) fully converge with SecOps under a single goal: maintaining secure network intent across hybrid infrastructure. As automation deepens, network teams will adopt observability models that continuously validate connectivity, performance, and compliance. I believe that by 2026, successful NetOps organizations will rely on real-time topology awareness and policy-driven automation to reduce both downtime and exposure windows, ensuring agility doesn't come at the cost of control.
Erez Tadmor
Field CTO, Tufin

CLOUD-LIKE EXPERIENCE FOR THE EDGE

Edge computing will become a bigger part of the narrative in 2026. It's been reported, but my conversations with clients are starting to lean heavily on how to bring cloud-like experiences into a hybrid edge environment. AI, whether generative, agentic, or traditional, is becoming a bigger part of the conversation at the edge. Innovating, managing, and scaling solutions for a large fleet of devices/locations will be a big ask and clients want a similar experience for those environments as they have with their cloud operations.
Juan Orlandini
Chief Technology Officer, North America, Insight Enterprises

FRONTIER EDGE

The industry's definition of "the Edge" is now obsolete; 2026 is here, and so is the "Frontier Edge." This shift is driven by several compounding pressures: the explosion of AI-generated content, the massive amount of data for inference (from immersive 8K media to critical AI model updates), and the necessity of connecting the never thought before locations, such as the deep sea, outer space, and the quantum world. However, consumer immersive traffic always takes center stage and competes with critical data, leading to a choke point within cellular 5G, Wi-Fi, and satellite networks. Avoiding this contention requires an immediate architectural shift away from congested and designed for one-to-one communication systems (e.g., cellular 5G, Wi-Fi, satellite), to a scalable, one-to-many distribution like the broadcast networks to ensure seamless and reliable connectivity in the Frontier Edge era.
Apoorva Jain
CPO, EdgeBeam Wireless

DIGITAL TWINS

The digital twin is evolving from a visualization tool into a practical workspace for network planning. It's becoming the operational backbone that unifies teams, accelerates design cycles and drives smarter decisions throughout the entire lifecycle of a network. Although still in the early stages, digital twins are rapidly evolving into a key enabler for AI-driven network lifecycle management, powering faster and more precise strategic planning.
Kelly Burroughs
Director of Strategy and Market Development, iBwave Solutions

WORK ANYTIME

Work from anywhere will become work anytime. Back-to-office mandates have pulled many workers back to the office, but WFH habits die hard. Many tech workers are used to logging in at times convenient for their schedule or work habits. Our usage data early this year showed heavy transfer of data on Fridays, an indication that "work from anywhere" employees actually put in longer hours than their "9 to 5" counterparts — with heavy usage starting at 7:00 am and continuing to 7:00 pm. In 2026 we expect to see more workers logging in both at the office and at home in their off-hours, which may temporarily increase productivity, but burn workers out more quickly. Companies will need to focus on worker experience as well as productivity.
Prakash Mana
CEO, Cloudbrink

Go to: 2026 Cloud Predictions

Related Links

Hot Topics

The Latest

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 ... 

Capacity Isn't a Guess: Observability-Driven Sizing for On-Prem Databases

In cloud-native systems, scaling is often as simple as moving a slider. For on-premise databases, the stakes are different. Over-provisioning hardware is expensive. Under-provisioning leads to performance bottlenecks that are difficult to fix once the equipment is in the rack ...

5 Security Principles Every Entrepreneur Should Apply to Leadership

When most people think about cybersecurity, they picture firewalls, encryption, and access controls — technical tools designed to protect systems and data. But beneath the technology lies a deeper set of principles about trust, decision-making, and resilience ... The best leaders don't eliminate risk. They manage it intelligently. And in many ways, cybersecurity offers a surprisingly useful playbook for doing exactly that ...