The "AI for AI's sake" phase is over. After years of pilots and proof-of-concepts, enterprises are heading into 2026 with a reality check: only about one-third of companies have scaled AI beyond experiments. For most, the bottleneck isn’t ambition, it’s other internal systems that aren’t built to scale alongside AI. Networks built for yesterday’s workloads with siloed data simply can’t keep up with the speed, scale, and adaptability that AI-driven environments now demand.
AI has moved from experiment to operational core. Enterprise networks supporting AI deployments can no longer be reactive; they need to think ahead, adapt in real time, and operate at a scale far beyond what humans are capable. As a result, the entire enterprise network is being fundamentally redefined – opening the door to faster decisions, seamless collaboration, and accelerated innovation across the business.
Networks that will not wait
In the future, autonomous networking won't wait for engineers to identify bottlenecks or diagnose failures. Instead, AI will proactively predict disruptions before they cascade across the enterprise, reconfigure traffic patterns instantly based on changing demand and make thousands of optimisation decisions over its lifetime. It does all this while maintaining strong controls – ensuring human oversight where it is required or desired, and embedding governance, explainability, and auditability so that automated decisions can be understood, trusted, traced and audited.
To achieve this goal, multi-agent systems will be required to manage the complex tasks involved in the entire network lifecycle, from planning to optimisation. Different agents handle very specific tasks and are orchestrated by planning agents that understand user intent and translate this into an execution plan, invoking those specialised agents much like a team manager assigns tasks to experts on their team.
As trust grows through continuous evaluation of system accuracy, controls can be gradually relaxed, allowing processes to move faster and execute changes while human involvement shifts from being “in the loop” to being “on the loop”, focusing on supervision and freeing up time for proactive work and strategic tasks. The network learns and adapts on its own, while users oversee it using their expertise.
The real question isn't whether this is coming; it's how prepared your network infrastructure, processes and employees are to take full advantage of it. Thinking about how autonomous driving is evolving offers a useful parallel, not just in terms of technology, but also human acceptance and the willingness to embrace a new way of working and living.
The security equation changes
So, here's where it gets complex. As networks grow and support new AI workloads, they also become more populated with non-human identities. Automated workflows driven by agents across accounting, marketing, engineering, security, and network operations create identities that need access, permissions, and monitoring.
These systems make decisions, interact with other agents, and adapt their behaviour based on outcomes, but traditional security frameworks weren’t built for entities that act and learn on their own. Identity and access security must evolve to keep pace.
AI agents need identity- and access-based controls that grant permissions based on purpose, data sensitivity, and context, treating them like any other user with the same or stricter controls. Continuous verification ensures that every interaction is validated, keeping agents accountable and auditable.
To enforce these controls effectively, organisations need full network visibility, which is where network fabric comes in. By mapping traffic and system connections in real time, organisations can use microsegmentation, isolating sensitive systems and keeping a compromised agent from moving laterally or accessing unrelated applications. This means separating operational systems from payment environments, isolating IoT devices from core applications, and containing breaches before they can spread.
Integrated platforms that combine Zero Trust Network Access, cloud NAC, and AI-powered threat detection tie everything together with robust identity and access management systems. By linking identity controls, continuous verification, network visibility, and segmentation, organisations can safely manage AI agents, turning autonomous systems from a potential risk into a secure, high-performing part of the network.
Where it gets real
Look at retail operations in 2026, where AI promises hyper-personalised shopping experiences. Behind every tailored recommendation lies a complex web of IoT sensors, cameras, mobile apps, edge devices and AI platforms. Through each one flows real-time inventory data, customer preferences and behavioural analytics. For example, electronic shelf labels, RFID systems and automated checkouts all need network access, making every connection point a potential vulnerability.
Healthcare is moving into a far more connected future as well. AI will support diagnostics, predictive alerts, and even robotic-assisted procedures, with patient data flowing continuously across EMR systems, monitoring devices, and AI analysis platforms. From wearables to surgical robots, the network becomes a critical pathway, and a point of exposure if networks and access controls aren’t designed for it.
In manufacturing, this evolution plays out on an industrial scale, with AI predicting equipment failures, optimising production lines, and coordinating autonomous robots as sensors, industrial controllers, and AI-driven machinery communicate nonstop, multiplying connected endpoints and raising the stakes if networks aren’t designed to support distributed AI safely.
Across industries, more automation and autonomy also means more risk. Without a unified enterprise network, strong access controls, and clearly defined human checkpoints, a single misconfigured AI agent could expose sensitive data, disrupt operations, or even put lives at risk. Security and AI-ready networks must be built in from the start to make innovation safe in 2026.
The foundation for autonomy
This brings us to the real challenge enterprises face in 2026: you can't bolt autonomous AI onto outdated, fragmented enterprise networks and expect it to deliver.
Enterprise networks need strong, scalable foundations that can handle these demands with minimal latency. They require edge computing close to where decisions are made, as well as high-bandwidth connectivity that won't collapse under IoT sensor loads or the strain of real-time analytics.
Most importantly, they need network infrastructure built for continuous learning – systems that can take in operational data, identify patterns and refine their models without slowing down. Organisations moving into this need to ask hard questions. Can your current network handle distributed AI agents running at the same time? Do you have enough compute power at the edge to process decisions locally, or are you still routing everything through centralised clouds? Is your security built for thousands of non-human identities or are you still thinking in terms of user credentials?
But the relationship goes both ways. Just as strong networks are critical for AI, AI is becoming essential for networks. Intelligent automation helps networks maintain performance, resilience, and security at scale. Full visibility, centralised data, and AI-driven optimisation ensure that networks not only support enterprise operations but continuously improve themselves, with humans guiding where and how autonomy expands.
In 2026, the winners will be enterprises that understand this symbiosis: AI needs the right network to deliver value, and networks need AI to operate at the speed, scale, and intelligence that modern business demands.
Applied AI, not aspirational AI
The focus this year is on applied AI – systems that deliver real results and actual business value. Impressive demos and speculative use cases don't cut it anymore.
What matters now is production-grade AI that operates safely at enterprise scale.
For businesses willing to prioritise overhauling outdated enterprise networks, the opportunity is massive. AI unlocks entirely new business model horizons, closing competitive gaps faster than manual operations ever could, that is, but only if the foundation is there to support it.
2026 is the year that AI will scale in production. The question is: is your organisation ready?
