Telcos are Buying AI Functionality, if Not AI Directly

Artificial intelligence is being adopted by communications networks in many subtle ways, even if AI is not a product but a capability.

Businesses and consumers do not buy “AI” any more than they buy calories in a direct sense. Instead, AI is a capability of some other product a service provider, enterprise or consumer purchases. 

That applies in telecom as much anywhere else. Some note that telecom networks can use AI for network optimization, preventive maintenance, virtual assistants and robotic process automation.  AI also plays a role in self optimizing networks (SONs), software defined networks and Network Function Virtualization as well, which are basic network principles in the 5G era. 

IDC has argued that 64 percent  of network operators are investing in AI systems to improve their infrastructure, for example. 

Some popular AI use cases in telecom include:

• ZeroStack’s ZBrain Cloud Management, which analyzes private cloud telemetry storage and use for improved capacity planning, upgrades and general management
• Aria Networks, an AI-based network optimization solution that counts a growing number of Tier 1 telecom companies as customers
• Sedona Systems’ NetFusion, which optimizes the routing of traffic and speed delivery of 5G-enabled services like AR/VR
• Nokia launched its own machine learning-based AVA platform, a cloud-based network management solution to better manage capacity planning, and to predict service degradations on cell sites up to seven days in advance.

AI functions almost always are used for pattern recognition, to understand typical trends or behaviors. In a consumer context that often is used to monitor customer financial transactions, to  spot anomalies in an account’s spending data that could represent potentially fraudulent behavior. Automated financial advisor services use AI to provide recommendations. 

In a manufacturing or energy industry use case, supply chain optimization, automated detection of defects during production and energy forecasting are use cases based on pattern recognition. 

Prediction, such as forecasting energy consumption, is another common use for AI. Classification or image recognition are other use cases, as when law enforcement agencies use facial recognition. Health and life science users might use AI to help process data from past case notes, biomedical imaging and health monitors to use for  predictive diagnostics.

Consumer speech to text is a frequent AI use case as well. E-commerce engines often use AI in a cognitive search mode to generate personalized recommendations to online shoppers. 

A related use case is natural language interaction, where a software application generates a report on sales revenue predictions without having to run the reports manually, or natural language generation, where a user might hear summaries of everything that has been analyzed from a large document collection. 

Communications networks might use AI to route traffic, optimize server loads or predict future capacity demand. Also, any industry relying on call centers for customer interaction and support use AI to support chatbots. 

Retailers use AI for personalized shopping experiences and customized recommendations.