The way businesses manage their data, apps and customer experience has changed due to AI and now generative AI will transform how IT teams operate in the future. As leaders begin to explore how AI will change network operations, the question has changed from “will AI” to “how quickly can we adjust infrastructure to support AI’s workload, security decisions, and constantly optimization?”.
With this change, the use of automation software for network operations has changed from being an additional tool to a must-have requirement. As per Slurp’it, IT leaders are aware that unless the network is fast, reliable and documentations are in place, there is no way that GenAI can produce any meaningful outcome.
This article discusses the value of automation for GenAI-powered IT operations and examines what IT leaders need to remember when they build their automation roadmaps.
How GenAI is accelerating the speed at which IT operations are conducted
Many enterprises have rapidly adopted AI within their business units over the last year. The findings of the McKinsey 2024 State of AI survey show that over 65 percent of businesses are currently using GenAI in their business units. Also, Gartner predicts that by 2027, generative AI will be utilized in more than 60 percent of infrastructure and operations workloads.
When It comes To Gen AI, without the use of automation, IT teams do not have enough time to keep pace with the investment of capacity being introduced by GenAI. Manual processes simply cannot scale In environments that change on a regular basis. Due to this, there are many reasons why manual network management approaches no longer work for workloads supporting AI.
1. AI pipelines are based on predictable sources of configuration
AI models require predictable performance and secure access (through low latency) to the sources of their data. If a single configuration error occurs in a switch or ACL, the result could be a breakage in an entire workflow. Additionally, due to Gen AI creating increased loads on the network, the possible margin of error Is reduced. However, because GenAI generates more traffic, network automation software provides an automated process to assess all configurations pre-deployment and continuously verify compliance across all devices.
According to the Uptime Institute, human error contributes to more than 70% of the network outages. As generation AI tools generate thousands of requests within a short period, even one mistake can disrupt a critical application. Automated workflows help to eliminate risk by enforcing standard proven configuration patterns as per Slurp’it.
2. Real-Time Changes to AI Systems
A driving factor of AI based tools is that they are able to react immediately to changes in the environment, as must the networks as well. Because of the rapid changes of AI’s analysis and decision making cycles, manual intervention is too slow to keep up. Automated monitoring and policy enforcement allow the network to be updated when performance thresholds, usage patterns and security risks change in real-time.
3. Documenting Network Changes Must Always Be Accurate and Updated
Generation AI tools rely on accurate network data to create recommendations and provide diagnostic triggers. Manual inventory updates cannot keep up with the need for accuracy. Network automation software maintains real-time documentation of devices, configurations, dependencies, and topology changes, allowing AI systems to operate with confidence.
How Network Automation Supports GenAI
While Network Automation software is a direct technology that supports AI, it is also quickly becoming a foundation for the safe and scalable deployment of AI systems. By combining both technologies, enterprises will be building an ecosystem of optimized speed, resilience and efficiency.
Automation strengthens the Operations of AI through the following four primary points:
1. Automated Validation of Configurations
GenAI driven platforms provide recommendations regarding routing, firewall rules, bandwidth allocations and micro-segmentation. Before deploying these changes to the network, the network needs to ensure that they are safe.
Automation provides:
- Continuous configuration audits
- Validation of configurations against compliance rules
- Detection of configuration drift
- Simulation of proposed configuration changes on the network’s current state
2. Automated Pipeline
AI tools utilize telemetry data to gauge the health of the network by collecting device data, normalizing it, correlating dependencies, and passing it on to AI models for clean, accurate data. Automated pipelines are needed to cope with the increasing volume of telemetry and the resultant use of AI.
Real-time visibility allows AI-powered decisions to be made based on up-to-date, trustworthy data.
3. Self-Healing and Automated Resolution of Issues
Generation AI will identify patterns quicker than a human will. When paired with automation, generation AI will also be able to resolve the issues identified without waiting for manual intervention.
For example:
- Traffic spikes trigger a scale-up to accommodate the increased traffic;
- A misconfigured interface can revert to the validated backup;
- A detected security risk can immediately update access control settings.
These automation capabilities significantly reduce mean-time-to-resolution (mttr) and improve reliability in distributed environments.
4. Enhanced Security for AI-Powered Workloads
The use of AI increases the attack surface area of a network due to the size of the systems communicating with each other and the number of systems communicating with external data centers and applications. Network automation software fortifies network security by:
- Enforcing fundamental zero trust policies
- Updating firewall rules based on current data (real-time inventory)
- Identifying and disallowing unauthorized configurations
- Validating segmentation between the various networks
The end result is an adaptive network environment that can react to security threats quickly without requiring intervention from the security team as per Slurp’it.
What senior leaders should consider before implementing network automation software?
While an organization can benefit greatly from the use of network automation software, in order for organizations to successfully adopt network automation software, they must take a thoughtful approach to implementing network automation software processes as per Slurp’it.
- Understanding the current inventory.
- Low-impact introductory workflow processes.
- Verify that the organization has the ability to integrate existing tools.
- Focus on automating processes through policy-based automation.
- Ongoing engineer training and support.
Lastly, how the future—AI and automation—will become one operating model.
According to IDC, by 2028, more than 75 percent of large enterprises will have fully autonomous or semi-autonomous network operations. This evolution to autonomous network operations is indicative of the broader movement within the industry to combine AI and automation into a single operational layer/platform.
Because of this, network automation software is no longer optional. It is the foundation that enables genAI to operate safely, efficiently, and at enterprise scale. For more information, contact us at Slurp’it!