AI in Networking: How Businesses are Adapting in 2024

Written by Jessica Schulze • Updated on

Learn about the state of AI in networking and how you can prepare your organization to adapt.

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Artificial intelligence (AI) has found its way into nearly every industry, even those not traditionally associated with tech. According to Stanford University’s AI Index Report 2023, the number of companies adopting AI has more than doubled since 2017. AI in networking isn’t a new application but it is a rapidly advancing one. In the following article, we’ll examine why it’s more than just the latest trend–it’s an essential IT management strategy. 

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?

Thirty-seven percent of companies are developing an AI strategy and 28 percent already have one in place. IT professionals in the following industries are most likely to report company use of AI: media, energy, automotive, aerospace, financial services, and oil [1].


Use cases for AI 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. 

Benefits and challenges of an AI networking strategy

Cost reductionTool integration
Remediation guidanceAI ethics
Real-time analytics and incident responseData quality
IT process automationEmployee learning curve

AI for the environment

Many companies are using AI initiatives to strengthen their environmental, social, and governance (ESG) initiatives. Specifically, 66 percent of IT professionals say their companies either already are or planning to adopt AI for sustainability purposes [1].


How to keep your company current and competitive

In regard to the return on investment (ROI) of AI in networking, studies show 30 percent of IT professionals worldwide are saving time thanks to automation tools and software [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 the AI, the more intelligent it will become. 

  • Scalability plans or requirements. A notable benefit to 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 Chat-GPT have made it easier for companies to introduce AI to employee workflows. Research shows, however, that 49 percent of employees in the US say they require more training to be able to use these tools effectively [2]. Given that 14 percent of survey respondents said they don’t plan to use AI tools at all, 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

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.

Keep pace with an evolving workplace on Coursera 

Employing AI in networking is an excellent way to ensure your system stays adaptable, efficient, and secure against AI-powered cyber threats. However, protocols and transparency with your IT team are essential pillars of support for any digital transformation initiative. Set your team up for success with a two-part plan, including technical implementation supported by thorough employee training. 

Article sources


IBM. “Global AI Adoption Index 2022,,-Automate-Processes-and-Encourage-Sustainable-Operations.” Accessed December 5, 2023. 

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Jessica is a technical writer who specializes in computer science and information technology. Equipp...

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