How AI Can Transform Your Network Operations


We hear constantly about the capabilities and functionality of AI. How can we apply them to network operations? What are the benefits, and – maybe more importantly – what are the pitfalls?

4 Ways Generative AI Can Optimize Network Operations

Network administrators are finding generative AI (GenAI) is having an impact on their work in four significant ways:

  1. Automating configuration and optimization: Organizations are leveraging the enhanced capabilities of AI and machine learning (ML) to automate configuration changes and optimization across the network. AI can analyze network data and identify patterns to automatically generate configurations that optimize performance – work that frees network engineers and reduces human error. Companies such as Cisco and Juniper (acquired by Hewlett Packard Enterprise) are developing intent-based networking solutions that use AI to understand an administrator’s intent and automatically configure the network accordingly. 
  2. Mitigating outages with predictive analytics: AI excels in analyzing historical data to identify patterns in network behavior and events leading up to outages. Through this analysis, AI predicts potential failures before they occur, allowing network administrator to take proactive actions. By training models with historical events and alerts, network administrators can train AI to recognize patterns in real-time. As organizations mature these models and refine the behavior of the AI system, they can allow automated configuration changes to mitigate or bypass network segments before they fail. 
  3. Improve network security: Maintaining the security of the network to protect customer and employee data is at the top of every organization’s priorities. Staying ahead of threats and vulnerabilities is a challenge many organizations face due to the rapidly changing threat landscape and limited resources. However, AI is helping organizations stay ahead of the attackers. AI models can analyze network traffic in real-time, identify anomalies that may indicate a security threat and automatically generate and apply configurations to mitigate the threat. Companies like Palo Alto Networks use AI in their next-generation firewalls to identify unusual patterns and automatically adjust security configurations. 
  4. Streamline and validate network designs: GenAI also can play a role helping network engineers design the network. With the appropriate design requirements and criteria, AI models can simulate and generate designs. This approach can allow organizations to model different designs to meet new or changing business and technology requirements. Companies such as Juniper (acquired by Hewlett Packard Enterprise) offer tools that can aid in the design of the network by automatically generating network designs that meet specific performance, reliability and scalability criteria. 

The Limitations and Pitfalls of AI

While AI is maturing at a staggering pace and seemingly getting better every day, it is not all sunshine. Organizations needs to be aware of the potential pitfalls and limitations of AI.

  • Integration challenges: To integrate and train AI to manage a network, an organization needs a deep understanding of its current environment. That environment needs to be clean and standardized as much as possible. AI tools need to be harmonized with the legacy infrastructure, which may not be designed to support AI functionality. Organizations need to be prepared to dedicate the time to ensure AI models are trained adequately and heavily test the behavior of AI before introducing it into the production environment to avoid disruptions and outages.
  • Garbage in, garbage out: Data quality is crucial to successfully train an AI model. Inaccurate, incomplete or biased data can lead to flawed AI decisions and predictions, potentially causing network inefficiencies or outages. The more complex and less standardized your network is, the more difficult it will be to ensure you are training AI with quality data. 
  • Security and privacy concerns: GenAI relies heavily on data analytics, and requires the collection, processing and storing of large amounts of network data. Organizations need to understand what data is being collected and stored to ensure the security of the data being used by the AI system and comply with data protection regulations.
  • Scalability and resource requirements: AI systems can require a significant amount of compute, network and storage capacity. Large volumes of data are needed to properly train the model and analyze data in real-time for predictive analytics and automated changes. As networks grow in size and complexity, the AI system must also scale to meet demand. If the AI system is unable to process the data quickly enough, decision-making and network management actions may be delayed, which can result in outages.

Creating an AI Strategy for Your Network

Integrating AI into your network operations brings numerous benefits, including automation, improved efficiency and enhanced security. It reduces the mundane and repetitive tasks of network engineers, freeing them for more valuable and strategic work. Organizations must take a measured and thoughtful approach to the implementation of AI into their network operations.  Rushing models into production and failing to take the necessary steps to ensure your environment is ready for AI can lead to wasted resources and outages.

Organizations don't have to do it alone. Partnering with vendors and managed service providers that have experience deploying and managing AI solutions for network management can mitigate the pitfalls and decrease the time to business value. If you are interested in learning more about how AI can help your network operations or developing a strategy for AI integration that is aligned to your business objectives and goals, contact ISG to schedule time to discuss.

About the author

Marc Herren

Marc Herren

Marc Herren is a Director at ISG.