AI in Networking: How Businesses Are Adapting in 2026

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. If you’re ready to start building expertise in AI, enroll in IBM AI Foundations for Everyone: A Practical Intro for Beginners Specialization. You’ll have the opportunity to learn foundational AI concepts like generative AI, machine learning, and natural language processing in as little as four weeks. Upon completion, you’ll have earned a career certificate for your resume.

AI in networking

AI in networking is also known as automated networking because it 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. Today, managing IT infrastructure is more complex than ever, thanks to rapidly evolving technology and copious amounts of data. AI in networking is just one way IT managers and business leaders ensure organizations remain competitive, secure, and agile. 

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].

How is AI used in networking?

• 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

Benefits and challenges of an AI networking strategy

How to keep your company current and competitive

According to Stanford University’s AI Index Report 2025, 78 percent of businesses reported using AI in 2024, an increase from 55 percent in 2023 [3]. In regard to the return on investment (ROI) of AI in networking, 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. 

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: Before implementing an AI solution, ensure your organization has systems in place to collect and process large amounts of diverse, high-quality, structured data. Gauge your data readiness by identifying any weak points in your system, such as where the location processing takes place or how long it takes edge devices to collect data. Since artificial intelligence trains itself over time through the data it’s provided, its 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. 

• 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. 

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. 

Which AI is best for networking? How to choose the right AI tools

Many modern businesses rely on a combination of applications, software, hardware, and cloud technology for daily operations. When selecting an AI networking solution, it’s important to keep compatibility at the top of mind. For example, cloud infrastructure dealing with high volumes of user traffic may have different requirements than on-premises or hybrid systems designed for internal use. Additionally, certain AI models may be more suited to specific industries based on training methods, data labeling techniques, and built-in metrics.

Explore resources to take your skills further 

Interested in the latest trends shaping your industry? Stay current by subscribing to our LinkedIn newsletter, Career Chat! Or if you want to keep learning more about AI and IT concepts, check out the following free resources:

• Take the quiz: Which IT Course Should You Take? Find Out in 1 Minute 

• Watch on YouTube: 3 Ways IT Professionals Can Start Using Generative AI Right Now 

• Hear from an expert in the industry: The AI Advantage: 9 Questions with UC Davis AI Instructor Sadie St. Lawrence

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