AI-driven predictive analytics are helping telcos provide better services by utilizing data, sophisticated algorithms and machine learning techniques to predict future results based on historical data. This means operators can use data-driven insights to can monitor the state of equipment, anticipate failure based on patterns, and proactively fix problems with communications hardware, such as cell towers, power lines, data center servers, and even set-top boxes in customers’ homes.

In the short term, network automation and intelligence will enable better root cause analysis and prediction of issues. Long term, these technologies will underpin more strategic goals, such as creating new customer experiences and dealing efficiently with emerging business needs. An innovative solution by AT&T is using AI to support its maintenance procedures: the company is testing a drone to expand its LTE network coverage and to utilize the analysis of video data captured by drones for tech support and maintenance of its cell towers.

This is the most advanced stage of the Visual Transformation, where service organizations can increase call deflection and reduce customer effort with visual, interactive guidance powered by Computer Vision AI. The visual customer assistant can deliver full self-service resolutions without interactions with human agents – think of this as a chatbot that can see. The visual assistant can recognize the product and its parts, identify the issue, and provide the resolution for the customer.

For example, a customer needs help troubleshooting their security system, which is making chirping noises. With autonomous assistance, the customer interacts with a visual tech assistant that can see using the power of Computer Vision AI. The customer is instructed to capture images of the alarm’s control unit. The system recognizes the device and then provides the customer with step-by-step guidance to resolve the issue.

This technology can also be utilized as a visual employee assistant that can offer decision support for agents and technicians by recognizing the problem and supplying the employees with next-best actions to fix the issue. For example, an IT technician completes a server installation job and captures an image of the completed work. With autonomous assistance, a visual tech assistant — powered by Computer Vision AI — recognizes that one of the cables has been placed incorrectly, and supplies the technicians with the steps needed to fix it.

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