Key Points
• AI networking combines AI and machine learning to automate network operations and improve availability and performance.
• Gartner predicts that 90% of enterprises will use AI to automate day 2 operations by 2027.
• AI networking can lower operational management costs by 25% and improve troubleshooting and networking availability.
As networks become more complex and distributed, they face numerous challenges, including performance, speed, availability, and latency issues. Legacy problems can cause financial and reputational damage, as seen in the CrowdStrike outage. However, AI holds the potential to improve IT networks by automating network operations and managing networks to improve availability and performance.
What is AI Networking?
AI networking deeply integrates AI into networking infrastructure to automate numerous processes and improve efficiency, adaptability, performance, speed, latency, and other critical factors. It primarily addresses day 2 operations, although it will likely be applied to day 0 and day 1 functions in the future. AI can be used to allocate resources, identify and quickly address problems in the network, centralize problem identification, automate recommendation and response, and reduce trouble ticket false positives.
Core Components of AI Networking
AI networking leverages advanced techniques to automate processes and monitor networks, including real-time traffic analysis, capacity planning, resource allocation, and longer-term prediction modeling. It also supports IT service management, improves threat response, and optimizes network experiences for different user groups.
Current Challenges in AI Networking
While AI networking has the potential to transform IT networks, it also faces challenges, including inflated expectations, overstated capabilities, and concerns about cost. Additionally, AI can make inaccurate recommendations, and enterprises must commit time and resources to upskill or reskill employees. Cultural buy-in is also crucial, as workers may be risk-averse or distrust AI.
How to Get Started with an AI Networking Strategy
To get started with AI networking, enterprises should assess their network needs, understand challenges and requirements, and identify areas where AI might be most beneficial. They should also select the right architecture, integrate tools with existing systems, and support Day 0 to Day N use cases. Proof of concept tests and careful iteration are essential to ensure the best outcomes.
In conclusion, AI networking has the potential to transform and modernize IT networks. However, it requires a strategic approach, careful iteration, and education, training, and upskilling to gain the greatest benefits.
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