Customer service centers, whether a shipping group dealing with consumers or an IT organization working with corporate employees, has a lot of information to manage. Technology has been improving over the last decades, and the move of artificial intelligence (AI) into the real world hold promise to help.
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Network management was one of the first internal groups to begin to leverage AI to better manage the vast amount of data in a more timely and accurate fashion. One reason was the limited ecosystem of networking and the more technical nature of the users. Machine learning is still fairly technical, especially machine learning (ML), and the early audience had to be able to handle the detail. Now natural language processing (NLP) and generation (NLG), are starting to mesh with machine learning to provide an interface than can help the non-technical audience.
The ML side has its own complexity. While there is much overlap in customer service systems, each company has its own way of doing things. Understanding how an individual organization works, what terms it uses, and what systems are involved is a signification challenge. That means a company wanting to provide a system can pre-train systems with large data sets, but also that companies who have their own data can train systems on corporate data.
The combination of natural language and machine learning techniques are components needed in order to leverage the power of AI to enhance customer service. It’s not one or the other, both are useful tools.
Aisera is a young company working in customer service management (CSM), and it is addressing that challenge. IT helped desks are often understaffed and overworked. That means any system that can effectively handle the most basic questions means both IT and their customer are happier to get faster resolution.
For instance, a business customer is in a conference room, preparing for a meeting. She can call or text the support system and say “I need access to the wireless network.” The system can understand her location, understand that “wireless” is the same as “Wi-Fi”, see what is available, and quickly send back connection information. The sales executive is quickly ready to present to the sales prospects, while IT isn’t called off of more complex issues.
copyright by www.forbes.com
Network management was one of the first internal groups to begin to leverage AI to better manage the vast amount of data in a more timely and accurate fashion. One reason was the limited ecosystem of networking and the more technical nature of the users. Machine learning is still fairly technical, especially machine learning (ML), and the early audience had to be able to handle the detail. Now natural language processing (NLP) and generation (NLG), are starting to mesh with machine learning to provide an interface than can help the non-technical audience.
The ML side has its own complexity. While there is much overlap in customer service systems, each company has its own way of doing things. Understanding how an individual organization works, what terms it uses, and what systems are involved is a signification challenge. That means a company wanting to provide a system can pre-train systems with large data sets, but also that companies who have their own data can train systems on corporate data.
The combination of natural language and machine learning techniques are components needed in order to leverage the power of AI to enhance customer service. It’s not one or the other, both are useful tools.
Aisera is a young company working in customer service management (CSM), and it is addressing that challenge. IT helped desks are often understaffed and overworked. That means any system that can effectively handle the most basic questions means both IT and their customer are happier to get faster resolution.
For instance, a business customer is in a conference room, preparing for a meeting. She can call or text the support system and say “I need access to the wireless network.” The system can understand her location, understand that “wireless” is the same as “Wi-Fi”, see what is available, and quickly send back connection information. The sales executive is quickly ready to present to the sales prospects, while IT isn’t called off of more complex issues.
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