With 5G, AI technologies for enterprises have the low latency to thrive. And with AIOps, 5G can be optimized like never before.
With explosive growth under its belt, AI has transcended from nerdy water cooler talk to a focal point for company leaders across all industries. From a 2023 McKinsey Global Survey, nearly a quarter of C-suite executives indicate personal usage of generative AI (GenAI) tools for work, and one-third of respondents say these technologies are already being used in at least one function of their organization. GenAI continues to reach its fingers toward AIOps and more, so it requires a hearty network platform to perform at peak levels. That’s how AI and 5G work together to shape the future.
Defining AIOps
Artificial intelligence operations, or AIOps, leverage AI technology and machine learning techniques to enhance, automate, and simplify IT operations. AIOps platforms collect data, metrics, and more from their existing IT environment to identify anomalies, patterns, and trends. The platform can then use this information to help automate manual tasks, predict performance, troubleshoot and remediate, and take proactive measures to avoid service or production disruptions.
The expansion of AIOps is accelerated in large part due to GenAI. GenAI is different from traditional AI in that it uses intricate algorithms to create new content such as images, text, audio, or video based on information or patterns it has observed.
How do 5G and AI benefit from each other?
At the highest level, 5G and AI are cornerstone technologies for digital transformation and innovation across industries. 5G in enterprise provides higher data speeds and lower latency than 4G and LTE. This enables the rapid data processing essential to AI applications.
AIOps algorithms can also be used for cellular optimization, helping 5G networks become more effective and efficient by predicting congestion or downtime, improving traffic flow, and allocating network resources to improve performance. This symbiotic relationship lends itself to applications including multi-access edge computing (MEC) and IoT.
Multi-access edge computing (MEC)
MEC moves data analysis, processing, and storage from the cloud to the network edge — closer to where data is generated. This reduces latency and allows high-bandwidth applications to respond in real time by negating the need to transmit data to a cloud for processing.
Supporting IoT
Enterprise IoT often requires 5G connectivity to accommodate latency and bandwidth requirements in dispersed locations that aren’t effectively served by wired connectivity. Using 5G to keep IoT devices online opens the door to incorporating AI models into their functionality. For example, AI-powered video surveillance devices can scan license plates or badges to look for certain individuals, or pinpoint incident footage without having to manually sort through hours of video.
Key 5G and AI use cases for enterprise networking
As businesses increasingly rely on advanced technologies to streamline operations and enhance productivity, 5G and AI emerge as pivotal ingredients in reshaping enterprise networking landscapes. By harnessing the power of 5G’s high-speed, low-latency connectivity alongside AI’s intelligent analytics and automation capabilities, organizations can unlock unprecedented opportunities in their networking infrastructure.
Autonomous vehicles
When AI-powered autonomous vehicles communicate with servers far away, there can be too much latency to allow for quick adjustments. This creates dangerous conditions for autonomous driving. Autonomous vehicles requiring AI operate best at the 5G network edge, enabling rapid response when calculating obstacles or hazards.
Industrial automation
AI and 5G play key roles in Industry 4.0. For example, in a manufacturing environment, the two can be used in tandem to better plan the production line, replicate processes using digital twins, or control robots doing work that is too dangerous for humans.
Retail and customer service
In a retail environment, it’s clear how AI and 5G work together to shape the future. AI technology backed by 5G can reveal new ways to display products or analyze shopping habits to provide personalized, augmented, or virtual purchase recommendations to shoppers.
Considerations for WAN optimization using AIOps
Integrating AI into network management platforms and other tools can help 5G customers optimize performance and efficiency. 5G connectivity makes AI-driven applications work efficiently, but what happens when enterprise businesses begin incorporating AI into network management systems?
AIOps have been around for many years in the wired space. Now that cellular WAN is hitting its stride and adoption rates have skyrocketed, there are opportunities to leverage AIOps and virtual expert capabilities to help network administrators be more efficient when running their 5G networks.
For example, IT teams can use AI to detect areas of performance degradation or faults in the network and determine the severity of the fault based on the number of sites and users impacted. Then, the AI model can provide the root cause analysis to drill down into the fault hypothesis. This saves time for administrators who no longer have to toggle between platforms and applications, giving them a head start on resolutions, which is crucial for all teams, especially lean IT.
Virtual expert capabilities are another modern “easy” button powered by AI. Administrators can simply input a request such as “show me the performance of my sites,” “help me configure…,” or “give me all the technical documentation on…”
Use cases that rely on traditional AI and AIOps will be enhanced by GenAI, but no matter how an enterprise chooses to integrate AI into its operations, it’s important to select AI tools optimized for business. What’s more, enterprises must be diligent in keeping proprietary data out of public tools as AIOps, GenAI, and more continue to undergo considerable transformations quickly.