Author

Markus Nispel

Published

13 July 2026

Estimated Read time

5Mins

Markus Nispel, Head of AI Engineering & EMEA CTO at Extreme Networks explains why your strategy and budget aren’t holding up your AI network initiatives – your execution is.

The pilot phase is over. A third of companies have now scaled AI into production. For everybody else, the holdup isn’t strategy or budget – it’s execution.

Many AI initiatives struggle to deliver meaningful ROI for familiar reasons: missed business objectives and clearly defined KPIs, limited AI expertise, processes that haven’t been redesigned for AI, gaps in AI and data governance, and persistent challenges around data availability and siloes, quality, and preparation.

At the same time, supporting AI at scale requires infrastructure that can keep pace with continuous data flows, shifting workloads, and real-time decision-making. Traditional networks were built for stable, predictable workloads – not the speed, volume, and variability that AI-driven systems demand.

As AI moves from experimentation into production, networks must handle continuous data flows, adapt instantly to shifting demand, and operate at a scale that manual intervention simply cannot match. Meeting these demands requires a shift in network design and operation. This is where AI-powered, autonomous networking becomes essential.

Autonomous networking in practice

Autonomous networking allows systems to detect disruptions early, adjust traffic flows and configuration dynamically, and continuously optimise performance in real time, rather than reacting after issues occur.

This is made possible through multi-agent systems. Individual agents manage narrow tasks such as bandwidth monitoring, anomaly detection, and routing optimisation. Others sit above them, orchestrating them, interpreting user and business needs, and pulling the right capabilities together.

The system also works within defined governance frameworks. Humans stay involved in critical decisions, with full visibility, transparency and explainability into system activity, planning, reasoning and decision-making. Every automated action is logged and can be traced, verified and audited if necessary.

Trust, as in real life, is built through repeated positive outcomes. When networks keep making the right decisions, teams gain confidence, reduce oversight and can focus on higher-value work rather than constant troubleshooting.

This transition is already underway. Recent data reveals that 88% of organisations already rely on multiple AI-powered tools for networking and security operations. For stretched-thin teams, autonomous networking has grown into a necessity. Meanwhile, the strain is evident, with 92% of leaders reporting that AI places heavier demands on bandwidth and computing resources.

In this environment, network performance becomes a defining factor in whether AI systems can operate reliably and deliver measurable value.

Security through intelligent boundaries

Scaling AI also changes the security landscape. Networks are no longer connecting only users and devices; they are supporting a growing number of non-human identities. Agents across finance, marketing, engineering and operations interact with systems and data, often without human involvement.

Traditional security models were not designed for this level of autonomy. Access can no longer be broad or static. It must be tightly scoped, context-aware, and continuously verified. Every action must be visible and traceable.

But confidence is climbing. In fact, 93% of executives now say AI-powered networking reduces security risk rather than increasing it. That shift matters. It shows organisations are moving past scepticism and starting to take control.

That doesn’t mean we should be giving agents free rein. It means tightening the boundaries around what they’re allowed to do and enforcing strict agent governance. Access can’t be broad or permanent. It has to be specific, context-aware, and continuously verified. Every action should be traceable through a clear audit trail as AI for networking is implemented.

Where fabric architecture makes the difference

Networking for AI will not work without visibility. Real-time traffic monitoring and system connection mapping make enforcement possible. From there, segmentation becomes essential to contain risk and prevent unintended access or lateral movement – especially in the agentic era we have entered.

Fabric architecture enables this level of control at scale. By creating a unified network environment, it ensures that policies are applied consistently across all users, devices, and workloads.

This allows organisations to automatically isolate sensitive systems and tightly control how entities interact. If an AI agent behaves unexpectedly or is compromised, its access can be contained immediately, preventing wider impact.

Organisations that get this right bring identity, access control, visibility and threat detection together into a single, cohesive system. Autonomy can then operate within clearly defined boundaries, giving IT teams the confidence to guide, verify and trust every action the network takes.

Real-world application

The impact of this shift is visible across industries. Take retail in 2026. AI allows hyper-personalised shopping through cameras, IoT sensors, mobile apps and edge devices. Inventory data, customer preferences and behavioural patterns flow nonstop. Shelf labels, RFID systems and automated checkouts all require connectivity.

Healthcare follows similar patterns. AI supports diagnostics and robotic-assisted procedures, with patient data moving continuously across EMR systems, monitoring devices and analysis platforms. Without well-designed enterprise networks and access controls, those same systems can become exposure points rather than protection.

Manufacturing uses AI to predict equipment failures, optimise production lines and coordinate autonomous robots as sensors and machinery continuously communicate.

Across industries, without unified enterprise networks, stringent access control and defined human checkpoints, a single misconfigured AI agent could compromise sensitive data, disrupt operations or even endanger lives.

From experimentation to production

The era of experimentation is over. Organisations that operationalise AI effectively move faster, operate more securely, and respond to change with greater agility. Achieving this requires networks that can anticipate demand, enforce boundaries, and adapt continuously without compromising performance.

When that foundation is in place, AI can deliver on its promise – reducing operational friction, minimising disruption, and enabling teams to focus on innovation rather than maintenance.

That’s what 2026 demands. Are you ready?

By Markus Nispel, Head of AI Engineering & EMEA CTO, Extreme Networks

  • Data & AI

Author

Jean-Philippe Avelange

Published

24 June 2026

Estimated Read time

4Mins

Jean-Philippe Avelange, CIO at Expereo, on why network resilience must be front od mind to drive digital transformation

Digital transformation has become fluent in the language of applications. Boards are asking for AI, cloud migration, automation, better customer experiences and more real-time data. Teams are being asked to modernise faster, operate more efficiently, and make digital investment show measurable returns. But what many enterprises are still learning is that the bottleneck is not always the application, the data, or the platform team. It is often the network underneath them.

For years, connectivity has been treated as little more than plumbing. Something to procure, renew and occasionally upgrade when a site opens or a contract ends. That assumption held up reasonably well when enterprise technology was slower, more centralised and more predictable. But it has become harder to defend now that applications are distributed, users are everywhere, data moves constantly between environments, and businesses expect digital services to perform in real time.

Expereo’s Enterprise Horizons 2025 research found that only half of organisations believe their networks are ready to support new technology initiatives. This is an issue for any organisation expecting transformation to move from a programme plan to something employees and customers can depend on every day. Once new tools scale across sites, providers, clouds and regions, the network either becomes an enabler of change or the place where momentum starts to slow.

The same research found that more than half of companies had experienced financial impact from network downtime or poor performance in the previous 12 months. That is lost productivity, lost revenue and lost confidence in the digital services that a business is trying to build around.

Resilience is not the same everywhere

The instinct, when faced with a fragile network, is often to add more – more bandwidth, more redundancy, another circuit. This creates the appearance of resilience while driving up costs and doing little to improve the experience where it actually matters. At the same time, most organisations still treat every location as equally important. But a headquarters, a manufacturing site, a contact centre and a small regional office are fundamentally different in how they operate and the risk they carry. Yet they are often supported by similar connectivity models.

Resilience needs to reflect that reality. The network should be designed around the role each site plays in the business. Some locations need diverse fibre paths or wireless failover to maintain continuous operations. Others need strong performance guarantees and rapid remediation. Others require a simpler, reliable and cost-appropriate baseline.

The goal is not to make every site identical – it is to make every site fit for purpose.

Standardisation still plays a role, but not in forcing every location into the same architecture. The more effective approach is to define clear site categories, each with a pre-defined level of resilience. That turns expansion into a repeatable process, rather than a constant redesign exercise.

Seeing where failures actually happen

Another gap sits in visibility. Most IT teams monitor applications closely, but far fewer have a clear view of the network paths on which those applications depend. When issues arise, teams are often troubleshooting without knowing whether the problem sits with the user, the site, the provider or the cloud environment. By the time the cause is identified, the impact has already been felt.

Failures rarely occur in isolation. They happen across the path between users, sites, providers and cloud platforms. Without visibility into those paths, problems are misdiagnosed, escalated slowly or incorrectly attributed to the application itself. Network observability changes this dynamic. It provides early warning rather than post-incident analysis, showing where performance is degrading, where traffic is being routed inefficiently and where user experience is being shaped by infrastructure rather than software. This visibility is also critical for data sovereignty. As organisations adopt cloud, AI and third-party services, understanding how data moves becomes as important as knowing where it is stored. In many cases, the network is the only place where the view exists.

From network to advantage

As digital transformation becomes embedded in day-to-day operations, connectivity moves from a background concern to a business-critical capability. The question is no longer whether the network matters, but whether investment in it is aligned with business risk. Adding more bandwidth or blanket resilience does not solve the problem – being more precise does. Resilience needs to be applied where it matters most, observability needs to be built into the network layer and design decisions need to reflect how each part of the organisation actually operates.

Connectivity has long been treated as plumbing, but in reality, it underpins digital transformation. It determines whether applications perform consistently, whether users trust the tools they are given, and whether organisations can adapt without repeatedly rebuilding their foundations. The organisations pulling ahead are not treating the network as an afterthought. They are designing resilience into the transformation from the start.

Learn more at expereo.com

  • Data & AI
  • Digital Strategy
  • Infrastructure & Cloud