How do machine learning and AI fit into UC monitoring and analytics?

The use of machine learning and AI is still in the early stages of development for most industries. This is especially true in the unified communications market. As a result, AI and machine learning in UC monitoring and analytics are still in their infancy, although that hasn't stopped vendors from adding it as a feature to enterprise communications offerings.

Monitoring and analytics with machine learning in the UC space are much the same as machine learning in general-use monitoring and analytics tools. Most of the focus for AI in the communications space is in anomaly detection algorithms. The value in machine learning is to find outliers in trends for collected data sets over time, such as available bit rate, packet loss, call setup time and session length. AI can then make suggestions based on this data analysis.

For UC platforms, using machine learning and AI to monitor trends helps give a clearer picture of how your team members are using their tools and when your team is most efficient and effective.

Another area where AI and machine learning are applied to UC monitoring is in fraud detection. Suspicious activities can be tracked and isolated by AI in an effort to shut down fraudulent accounts or assist customers in finding fraud within their organizations.

An example would be international phone calling fraud, where an extension in the business is forwarded to numbers abroad and dialed in locally, increasing cost. Once the patterns are recognized and labeled as fraudulent, a machine learning model can be used to track similar activities across the businesses.

Due to market interest, communications vendors are adopting machine learning.  This includes UC vendors who focus on monitoring and analytics. Most of the focus for machine learning and AI in the UC market is on speech to text and voice bot use cases. But the potential applications for AI technology in communications are seemingly limitless.

Vendors see machine learning as a way to differentiate themselves from competitors and lure investors to win new business. It is unclear what new use cases and capabilities AI will bring to enterprises. Most AI initiatives are driving the market toward data collection and cloud deployments -- two aspects many AI initiatives have in common.