The role of network automation in AI-driven businesses

AI adoption is accelerating across nearly every industry. According to McKinsey’s 2025 State of AI report, 78% of organizations now use AI in at least one business function, up from just 55% the year prior. From real-time analytics to generative tools and process automation, AI is becoming a fundamental part of how modern businesses operate and compete.

But behind every AI application is a need for fast, uninterrupted access to data. That’s where network automation comes in. As businesses scale their use of AI, they’re relying on intelligent network automation technology to maintain performance, ensure uptime, and reduce the risk of bottlenecks or outages that can derail AI workflows. The network has become the invisible backbone of AI operations, and automating it now can help prepare your business for what’s to come.

In this article, we’ll dive into why this shift matters and how network automation technology can support your organization’s AI-driven operations. We’ll also share some practical tips to help you prepare as the future unfolds. 

Why AI adoption demands a smarter network

​​Modern AI applications thrive on fast, uninterrupted access to data. Whether it’s a chatbot delivering real-time answers or a machine learning model analyzing hours of video footage, these systems depend on networks that can handle huge volumes of information with minimal delay. To meet that demand, a wave of innovation in data center connectivity and rack architectures is underway, with companies like NVIDIA, Dell, and Synopsys rolling out new tools to reduce latency and improve throughput.

But here’s the catch: most traditional networks weren’t built for this. Manual configuration and static systems can’t respond fast enough to handle traffic spikes or the constant back-and-forth that AI workloads require. The result? Slowdowns, performance issues, and overburdened IT teams. That’s why more organizations are turning to network automation — a smarter, more scalable way to support the next generation of intelligent applications.

How network automation supports AI-driven operations 

In the world of business IT, network automation refers to the use of software tools to monitor, manage, and adjust a network’s performance without constant manual input. Instead of relying on IT teams to configure settings or chase down issues, automated systems handle tasks like routing optimization, anomaly detection, and traffic balancing in real time. This makes it easier to maintain a network that’s secure, resilient, and always performing at its best, especially as complexity scales.

That real-time responsiveness is critical for organizations running AI-powered tools. AI models are only as good as the data they can access and the speed at which they can access it. If network slowdowns or security events interrupt that flow, the performance of AI systems can drop dramatically, leading to missed insights, laggy user experiences, or even incorrect results.

It’s no surprise, then, that enterprises are accelerating their investments in automation. According to Gartner, the percentage of enterprises that automate more than half of their network activities is expected to triple, from less than 10% in mid-2023 to 30% by 2026. As more AI applications enter the enterprise stack, automation isn’t just a nice-to-have. It’s becoming foundational. Network automation helps identify and resolve issues before they interfere with operations, ensuring that both the tools and the people who rely on them stay productive.

Benefits of network automation for AI-driven businesses

As AI adoption grows, so does the pressure on network infrastructure. Manual processes simply can’t keep up with the speed, scale, and sensitivity of AI workloads. That’s where network automation delivers real value — not just by improving performance, but by making the entire system more responsive, secure, and future-ready. Here are five key benefits for AI-driven organizations:

1. Increased network reliability

AI tools rely on continuous access to data. Even a short network disruption can stall model performance or lead to failed inferences. Network automation reduces the risk of downtime by constantly monitoring performance, rerouting traffic when needed, and identifying issues before they escalate. This means fewer disruptions and more consistent results from your AI systems.

2. Improved performance for AI workloads

AI workloads are bandwidth-hungry and often latency-sensitive. Automation helps dynamically balance traffic, prioritize time-critical processes, and optimize paths through the network. That translates into smoother real-time experiences — whether it’s a customer on the receiving end of a chatbot or an employee processing large datasets.

3. Scalability for growing AI needs

As businesses layer in more AI applications, from edge devices to cloud platforms, the network must scale in lockstep. Automated systems are built for this kind of growth. They can handle rising data volumes, new device connections, and shifting workload demands without constant hands-on configuration, making them ideal for future AI investments.

4. Enhanced security and compliance

Your AI systems may handle sensitive data — from proprietary algorithms to personal customer information. Network automation strengthens security posture by maintaining up-to-date device configurations, monitoring for unusual behavior, and supporting consistent access control practices. It also reduces the risk of human error during routine administrative tasks, helping IT teams meet compliance requirements more efficiently.

5. Reduced IT workload and human error

Managing an AI-ready network manually is time-consuming and error-prone. Automation can take repetitive, low-value tasks off your IT team’s plate, freeing them to focus on more strategic initiatives. This reduces burnout, speeds up response times, and helps build a more proactive, efficient IT environment.

The network strain of scaling AI

For many organizations, early AI adoption meant using tools like ChatGPT or Microsoft Copilot — convenient SaaS offerings that delivered value without straining internal infrastructure. But as businesses look to embed AI deeper into their operations, the technical demands grow fast. To avoid private data going into public AI models, organizations like Spirent are experimenting with custom LLMs, which require large-scale data movement, modern integration pipelines, and real-time responsiveness. 

According to Flexential, 43% of companies now report bandwidth shortages, while 34% struggle to scale data center space and power to meet growing AI workload requirements. And that’s just the beginning. AI doesn’t just consume more data — it generates more too, requiring modern pipelines and real-time visibility to keep everything flowing smoothly.

As organizations transition from proof-of-concept to production, these infrastructure challenges become make-or-break. Delays in AI inference, unreliable connections, and siloed data can derail adoption. That’s why more teams are turning to intelligent network automation to ensure the infrastructure is as dynamic, resilient, and responsive as the AI tools it supports.

Why IT teams need network visibility before they scale AI

AI use cases are expanding across departments, locations, and platforms. At the same time, IT teams are expected to keep everything connected, compliant, and high-performing, often with limited insight into what’s actually happening on the network.

Many teams are still operating with outdated diagrams, static documentation, or piecemeal monitoring tools. That becomes a serious liability in an AI environment, where data needs to move freely between systems, and a minor slowdown in one area can cause a chain reaction across the business. Without visibility, you can’t see where bottlenecks are forming, where performance is degrading, or where sensitive data is exposed.

That’s why real-time network visibility is the foundation of successful AI infrastructure. Automated network discovery, live topology mapping, and contextual alerting empower IT teams to stay ahead of issues, troubleshoot faster, and confidently support complex AI workloads. And as enterprise AI evolves beyond internal tools into customer-facing applications, the stakes get even higher. 

The future of network automation for AI-enabled enterprises

While AI continues to reshape how businesses operate, network automation is evolving in two powerful directions. As Scott Raynovich, Founder at Futuriom.com explains, the impact of AI on networking runs both ways: there’s “Networking for AI” — the infrastructure needed to support the heavy bandwidth and low latency demands of AI workloads — and “AI Networking” — the use of AI to automate, optimize, and secure networks themselves. For enterprises investing in AI, both sides of this equation are evolving fast. 

We’re entering an era where networks can’t afford to be reactive. Automation is making them predictive, policy-driven, and increasingly autonomous. Concepts like intent-based networking (IBN) are gaining momentum, with the global IBN software market projected to grow at a CAGR of 19.6% through 2030. IBN lets IT teams define desired business outcomes while the network configures and maintains itself to meet those objectives, essential for keeping pace with the agility AI demands.

Meanwhile, AIOps (Artificial Intelligence for IT Operations) is becoming a key enabler of automated, intelligent incident management. By gathering performance data from across the environment and applying machine learning to identify patterns and root causes, AIOps helps prevent disruptions before they affect users or AI workloads. This goes hand in hand with predictive analytics, which uses historical network behavior to forecast future issues and dynamically allocate resources.

Looking ahead, the infrastructure layer itself is evolving to support this shift. Companies like NVIDIA, Arista, and Broadcom are leading the charge in building AI-optimized networking solutions, including SmartNICs, DPUs, and fabric switches that reduce latency and accelerate AI traffic flow. 

In short, network automation is becoming the connective tissue between scalable AI performance and operational resilience. As AI continues to push the boundaries of what’s possible, the network will need to do more than just keep up — it will need to think ahead.

How to get started with network automation for AI

If you’re exploring or scaling AI tools within your organization, now’s the time to assess whether your network is ready. Start by asking a few key questions:

• Do you have real-time visibility into your network across all locations?
• Can your infrastructure detect and resolve issues without constant manual intervention?
• How quickly can you adapt your network to support new AI tools or data sources?

For many IT teams, the answer to at least one of these is “not yet” — and that’s okay. The good news is, modern network automation platforms make it easier than ever to fill those gaps without re-architecting everything from scratch.

You don’t have to go all-in overnight. Start small:

• Automate network discovery and documentation.
• Set up alerting thresholds for key performance metrics.
• Roll out performance monitoring across critical sites or systems first.

As AI becomes more embedded in your business, network automation ensures you’re building on solid ground and frees your team to focus on what’s next.

Auvik: Simplifying network automation for AI workloads

AI workloads depend on fast, reliable, and uninterrupted access to data, and Auvik helps you make that happen. Our network automation software makes it simple to stay ahead of issues, optimize performance, and keep your AI infrastructure running smoothly.

With automated network discovery and mapping, you can get full visibility into your infrastructure in under an hour. Auvik automatically detects connected devices and builds a real-time network topology map, so you can quickly understand what’s connected where and troubleshoot faster when issues arise. In AI environments where latency matters, having a clear view of your network is key.

Once you’ve gained visibility, Auvik helps you stay ahead of issues with real-time performance monitoring and intelligent alerting. You’ll receive instant notifications when something deviates from normal, whether it’s packet loss, bandwidth congestion, or a misbehaving device. That means you can take action before it impacts your AI tools or your team, giving you confidence that your network can support the scale and speed AI demands, without adding to your IT workload.

Start your free trial and see what Auvik can do in under an hour. Or, book a guided demo to explore how Auvik fits into your AI strategy.