The real hallmark of success for an automated self-service system is its capability to understand cases where an automated response is not appropriate. It needs to adequately recognize the need for human intervention, and seamlessly put the customer through to an agent where necessary. When situations escalate or get emotional, this is when swift access to a live agent is required.

 

What AI Requires to Do Well

 

An AI-based system learns the best way to handle situations based on data and experience. This means chatbots are only as good as both the data they are given to learn from and the process steps that have been pre-programmed to automate customer service requests. So many background data is required for an AI chatbot to be able to answer any given customer issue. There may be many hundred different ways that a customer may phrase a query, and several response options available to resolve the issue. For instance, a leading credit card company analyzed how many different ways customers asked for their present account balance. The outcome was 2,100 distinct ways—a surprising amount of variation for what seems a pretty prescriptive request at first glance.

 

It is true that machine learning allows AI to bit by bit improve performance over time. But at the outset, initial training is needed to customize the algorithms — the rules — for certain use cases.  Live chat and email logs can provide real-world data to capture customer intents.  Once these are evaluated and understood, organizations can then move towards a machine-learning chatbot implementation.

 

AI-Based Triage

 

When deciding where to apply chatbot technology, concentrating on processing only a small amount of very high-volume customer requests can often create bigger business wins. For instance, 50% of all customer inquiries may in fact relate to just three very common, regularly seen issues or faults.  Pointing chatbot learning and training to recognize and triage these inquiries properly will instantly improve the customer experience, while reducing agent workloads.

 

Natural language processing (NLP) in an integrated platform that balances human and AI-assisted service can discover whether an interaction would be better resolved by a human agent or AI. If human, it will route it to the best-skilled advisor; if AI, it will use Robotic Process Automation (RPA) to process the requests and answer to the customer.  NLP technology can also extend beyond a chatbot — playing an integral role in email customer service support or online chat, automating and accelerating response times.

 

Self-service technology is not here to replace or diminish communication with agents — it is as an alternative a tool to manage interactions more successfully. By judiciously applying intelligent routing with AI, those customers who need or expect agent intervention receive it flawlessly with a better experience — while those who have a routine query can rapidly access the information they need.

 

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