Challenges of AI-driven Customer Support Automation — Abto Software

Deciding whether or not to automate customer support

How to approach the decision to implement AI-driven CSA

  • Evaluate the cost versus benefit. Developing new software, especially with AI features, can be quite costly. If your support team is more or less small and handles the job well, introducing an AI assistant might not be beneficial. On the other hand, if it’s quite large and is still growing, or cannot handle the volume of requests adequately, automation can be exactly what you need.
  • Analyze your customer support workflow. In order to determine the cost and the benefit of automation, one needs to understand how your customer support team works, how many support cases it is handling on a daily basis, how complex they are. Most businesses rely on centralized CRM systems where each support case is logged. If it’s not the case for you, and the supporting data is split between various systems that different divisions of your company use, this is where you could start building your automation solution.

Determining which customer support cases to automate

How to determine the scope of CSA

  • Understand what can be automated and what cannot. AI automation, e. g. with customer service chatbot, works best in cases where there is a high volume of simple and repeated customer requests. Even highly sophisticated conversational chatbots cannot completely replace the human (in the current state of the art), however, it can offload some routine tasks off them. Handling complex scenarios would be difficult for AI, and even if the automation is in place, it would have to be backed by a human support team. Chatbots also work well as the first line of support, determining the category of the customer request. The complete fulfilment of the requests, on the other hand, has many more variables that determine whether the request may or may not be automated.
  • Categorize your customer requests. Even if you have all the support case data collected in one place, it could still be hard to analyze if they are not assigned a category and just lay there in one big pile. Categorizing each request can help you determine the most repeated ones, the most likely candidates for automation.
  • Understand what it takes to fulfil specific customer requests. To automate the case, the machine needs to be able to do exactly the same things that a human support agent does. Even if some cases are often repeated, but require something that a machine cannot do, such cases cannot be fully automated. Also, if a customer issue requires specific knowledge and individual analysis of each case, it’s not a good candidate for AI-driven automation. An example of a good automation case is a request for the merchandise return. An example of a not-so-good automation candidate is an issue with a Wi-Fi router. Although, there are always options…

Handling complex CSA cases

How to handle complex CSA scenarios

  • Automate the first line. Although the AI will most likely not be able to solve your customer’s issue with the router (unless you invest a great deal of money and bring in top AI specialists), Artificial Intelligence could still save you money by acting in place of the person that directs the customer to the tech specialist that will solve it. Understanding natural language is something that AI-driven chatbots can handle really well.
  • Human-in-the-loop (HITL). If the case requires some actions that cannot be performed by a machine, you can still automate everything else, and have your human agents do only what is absolutely necessary. Another example could be the handling of sensitive requests that can be fully automated but require a review and approval by a human in order to confirm the validity of automated decisions.

Ensuring CSA solution quality

How to guarantee the highest quality of AI-driven CSA

  • Progressive rollout and monitoring. Instead of making your AI handle 100% of customer requests from the beginning, start with a small percentage and slowly increase it while monitoring how it performs and making necessary adjustments.
  • Selective rollout. If you have the ability to segregate your less valuable customers (i.e. trial vs paying customers), consider testing your automation on less valuable customers first, before rolling it out for more valuable ones.
  • Human as a backup. Any automated decision making should have humans backing it up. The AI cannot be 100% accurate, and if it isn’t sure how to behave with a specific customer, it’s good to have an opportunity to quickly switch that customer to a human agent instead.
  • Continual learning. As a natural consequence of the above point, it’s good to have the AI learn from such cases so that it can handle them next time.




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Abto Software

We empower customers business with innovative software by applying science, R&D, and own IP at