Ideas lab
When machines join the team
Article
Sep. 26, 2025
Explore the network solutions that ready businesses for AI’s demands
By Paul Savill, Global Practice Leader for Network and Edge at Kyndryl
AI isn’t the future disruptor of networks — it’s already rewriting the rules. And as this emerging technology continues to reshape the world, leaders across every sector are seeking to ready their IT estates to support its successful and secure large-scale deployment.
They have every reason to act quickly.
Many industry observers expect AI-generated network traffic to surpass traditional data traffic by 2031, marking a major shift in enterprise network use and infrastructure demands. If networks are not redesigned with AI in mind, AI strategies will hit a wall, or even worse, create bottlenecks, security gaps, or operational failures.
The network is the unsung hero behind every innovative breakthrough powered by AI. And as AI models grow smarter and faster, they will demand more from the infrastructure that connects them. Where do AI and networking collide — and what does that mean for enterprise IT? Four key intersections reveal the answer.
When the network starts thinking
AI doesn’t just transform what networks do, it also changes how they think. By embedding intelligence into the network itself, AI paves the way for systems designed to self-monitor, self-heal, and continuously optimize performance with minimal human intervention.
This shift toward predictive, autonomous operations portends meaningful benefits. Fewer outages, faster issue resolution, and more resilient infrastructure are among, and all while freeing up busy IT teams to focus on higher-value tasks. AI-powered networks can preemptively identify threats and take preventative measures to mitigate them, automate routine support tickets, and adjust traffic flows in real time. And that’s especially important for AI-driven applications that demand reliability.
AI can also play a key role in boosting network security. With its ability to analyze vast amounts of data and catch even the smallest of system anomalies, the technology can respond to threats before they escalate.
Protecting what AI connects
AI may help optimize networks — but it also puts them to the test. Generative AI and large language models (LLMs) aren’t just compute-hungry; they’re network-intensive. They demand vast data movement at high speeds and with near-zero latency.
That means today’s networks need a major upgrade. To support modern AI workloads, infrastructure must be designed for real-time responsiveness, high-throughput connectivity, and resilience under pressure — especially as AI tools are embedded deeper into business-critical operations.
And because AI often handles sensitive and proprietary data, security must evolve in lockstep. Enterprises are turning to secure access service edge (SASE) architectures and zero-trust models to protect data as it moves — not just at rest. In a world where AI is always on and always learning, network security must be continuous, adaptive, and baked in at every layer.
AI traffic growth is accelerating, and it is projected to surpass conventional traffic by 2031.
Where speed matters, the edge leads
Edge computing plays a critical role in AI deployments, especially in mission-critical environments where data needs to be processed quickly and efficiently. AI inference at the edge involves processing data closer to the source, significantly reducing latency compared to cloud-based inference. This is crucial for applications requiring real-time insights, such as gaming, healthcare diagnostics and fraud detection. Edge AI also reduces cloud data transfers, cutting costs, boosting performance and improving energy efficiency to support sustainability.
One emerging approach accelerating edge AI is federated learning, which allows AI models to train directly on edge devices — no need to transfer raw data to the cloud. This benefits industries such as healthcare, finance and manufacturing, where data residency, confidentiality and real-time insights are critical.
Connected systems, smarter outcomes
AI readiness is not just about having the right technology in place — it’s about helping all components work together seamlessly to support the complex demands of AI. Integrated models help businesses ready their infrastructure for AI, providing security-rich and scalable solutions across different IT environments.
New AI techniques like vibe coding are democratizing software development, allowing nontechnical users to build functional applications or tools with AI assistants using natural language prompts. Trends such as vibe coding are lowering the barrier to entry for software creation and accelerating innovation, with significant implications for traditional IT roles where engineers often rely on repetitive, script-based tasks.
The more intelligent your infrastructure becomes, the more creative your workforce can afford to be.
As organizations scale their AI solutions, they must ensure that security protocols are embedded within all layers of the infrastructure to protect sensitive data and maintain compliance with evolving cyber regulations. Organizations should consider conducting a network security assessment to identify, track, and address risks posed by AI tools and systems.
Scaling AI for the future
As AI adoption accelerates, scaling AI solutions and supporting them with the right infrastructure is essential. By leveraging deep IT infrastructure expertise and collaborating with key technology partners, organizations can modernize their networks to handle the data demands of large-scale AI deployments – enabling them to focus on achieving meaningful business outcomes.
