What Is AI in Networking? Different Uses and Key Tools to Stay Competitive

Key takeaways

AI in networking can automate IT processes and make IT networks more efficient.

• According to a 2025 Atomicwork survey, 24 percent of IT professionals report receiving a positive return on investment from AI adoption [1].

• Applying AI in networking can offer several benefits, like cost reduction, remediation guidance, and real-time incident response.

• You can apply AI to various networking areas, including cybersecurity, data analytics, and performance monitoring.

Explore how you can use AI for various networking and IT functions, and learn more about formulating an AI strategy to keep your business current. Afterwards, advance your knowledge of using AI to improve systems with IBM's Generative AI for IT Systems Analysts and Architects Specialization, where you'll learn how to use generative AI to turn messy inputs into clear, accurate systems requirements and documentation.

What is AI in networking?

AI in networking, also known as automated networking, streamlines IT processes, such as configuration, testing, and deployment. The primary goal is to increase the efficiency of networks and the processes that support them.

As managing IT infrastructure grows more complex thanks to rapidly evolving technology and copious amounts of data, applying AI is just one way IT managers and business leaders ensure organizations remain competitive, secure, and agile. 

How is AI used in networking?

There are different ways AI can be used in networking. These include:

• Cybersecurity: AI in cybersecurity enhances threat detection and response time by broadening the parameters used to identify suspicious patterns and behavior. It can also be employed for autonomous scanning, patching, and system updates. 

• Data analytics: Businesses generate massive amounts of data daily, including security logs containing vital information about network health, user behavior, and anomaly detection. AI can parse through historical data to identify opportunities for predictive maintenance and visualize findings for easier review. 

• Performance monitoring: AI in networking can be used to continuously monitor user experiences. By constantly analyzing network data, AI can predict, prevent, and detect performance degradation.

• Intelligent routing and scaling: An AI-optimized network can balance loads and optimize resource allocation to reduce network congestion and latency caused by high traffic. 

Read more: Why Cybersecurity Professionals Need to Understand AI

How to choose the right AI tools for networking

Selecting the best AI networking solution requires first evaluating several dimensions to ensure a seamless integration and increased performance.

1. Infrastructure compatibility assessment

Start by thoroughly analyzing your existing network architecture. Cloud-native environments with elastic scaling needs will benefit from AI tools optimized for distributed processing and real-time traffic analysis, such as those offering auto-scaling anomaly detection.

Conversely, on-premises networks may prioritize AI solutions with robust edge computing capabilities and minimal latency for critical internal operations. Hybrid environments require AI tools that can seamlessly bridge both worlds while maintaining consistent security policies and performance monitoring.

2. Industry-specific AI model selection

Different sectors demand specialized AI approaches. Financial services networks need AI models trained on fraud detection patterns and regulatory compliance requirements, while healthcare networks prioritize patient data privacy and medical device integration capabilities.

Manufacturing environments benefit from AI tools designed for IoT device management and predictive maintenance algorithms, whereas educational institutions need solutions optimized for bandwidth management during peak usage periods.

3. Technical requirements evaluation

Consider your network's specific demands: traffic volume, security requirements, compliance needs, and integration complexity. Evaluate whether you need real-time processing capabilities, historical data analysis, or predictive modeling.

Additionally, assess the AI solution's learning capabilities—some tools excel with supervised learning from labeled network data, while others perform better with unsupervised anomaly detection in dynamic environments.

4. Scalability and future-proofing

Choose AI tools that can grow with your network infrastructure and adapt to emerging technologies like 5G, edge computing, or quantum networking protocols.

Benefits and challenges of an AI networking strategy

Implementing AI as part of your networking strategy comes with certain benefits, but there are also considerations to keep in mind.

How can companies stay current and competitive?

Studies show that 24 percent of IT professionals worldwide realized a positive ROI from using AI, with the most common benefits including increased productivity, enhanced user experience, streamlined operations, reduced costs, and better decision-making [1].

The implementation of AI in networking is gradual for a few reasons. Notably, organizations must strengthen their data management techniques in order to deploy AI in a meaningful way. The next couple of sections expand upon why this type of digital transformation takes more than tech. 

1. AI strategy

Network requirements are changing rapidly alongside advancements in AI and machine learning technology. Although employing AI is a crucial step toward modernizing your organization, you’ll need to examine your existing infrastructure and protocols to arrive at a comprehensive solution. Here are a few things to consider while planning your migration to AI in networking:

• Your organization’s current approach to data collection and management: AI's output can only be as precise as the input. The more quality data your organization can provide to the AI, the more intelligent it will become. Ensure your organization has systems in place to collect and process large amounts of diverse, high-quality, structured data.

• Scalability plans or requirements: A notable benefit of automated networks is scalability. AI can help adjust resource allocation to maintain optimal network performance as your business grows or more organization members are added. 

• Goals and key performance indicators (KPIs): Your plans for implementing AI in networking should align with your organization’s bigger-picture business goals. Identify how AI might increase value by highlighting company priorities such as cost reduction, risk management, enhancing user experience, or process automation. Setting quantifiable metrics surrounding these goals can help measure the success of your AI networking strategy and keep your initiative on track. 

Who’s using AI in IT?

In the 2025 McKinsey State of AI survey, 9 percent of respondents reported using AI agents for IT functions, which was also the business area with the most significant increase in AI agent adoption [2]. Areas that saw the most AI application in the IT domain include technology, insurance, health care, and media and telecom  [2].

2. Employee training

User-friendly AI tools such as ChatGPT have made it easier for companies to introduce AI to employee workflows. Research shows, however, that 41 percent of IT professionals worldwide report a lack of expertise as a top barrier to AI adoption [1]. Given that 5 percent of survey respondents said they don’t plan to use AI tools at all [1], employee training can be an effective way to encourage adaptation and strengthen engagement. Ensuring the members of your organization are willing and able to adapt is a core principle of change management. 

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