How to Deploy Conversational AI for Voice?

Conversational AI is becoming popular nowadays, with the growing need and use of technology. It is also becoming an integral part of the customer service that works through voice calls. 

The voice assistants that usually work with the help of conversational AI can easily interact with the customers just like an agent while offering support to your customers 24X7.

After you have decided to work by using conversational AI for voice, you will become aware that there are various types of methods for designing, creating, and also deploying a voice assistant successfully. 

Though some of those ways are cost-effective at first, after some time, those may become expensive. So, here are some different approaches with which you can successfully deploy voice assistants to work with ease.

Method 1: Using the Port chatbot technology

Several companies are starting their journey with a chatbot. It is a good choice because chatbots can be easily deployed on the established channels and also can help in deflecting the call volume. 

Thus, many different companies have already become successful by using chatbots in the field of customer service. However, the procedure of converting a chatbot into a voice bot may have mixed results.

The process of converting chatbots into the Voicebots may fail

The basic process of converting a chatbot into a voice bot is to add text-to-speech for output and speech recognition to the input. So, the results may frustrate the customers who do not speak precisely. 

Chatbots will look for keywords. As speech recognition symbolizes transcribing the spoken words accurately, the out-of-the-box phrases cannot be determined by them. Thus, to work properly, they need to fine-tune the neutralized accents. Such as, if the caller says the number “4”, the bot needs to disregard the non-numeric words like “for, “ “fur”, etc. 

However, the machines cannot hear the words with 100% accuracy, and thus they need to use knowledge to understand completely what they are “hearing. “

Method 2: Using third party conversational platforms

Some organizations are working with the conversational platforms that are provided by IBM Watson, Google Dialogflow, Rasta, Amazon Lex, etc. These usually follow the approach of a GUI or graphical user interface to create conversational assistants. 

Thus, these are preferred by those who plan to keep their development or project in-house. However, only a few organizations have already put their voice assistants that are platform-built to live with their customers. 

The proof of concepts of those organizations remains locked even after several months of the initial development and only interacts with the testers. 

The starting proof of the idea that can handle 3 to 5 intents needs to face the customers after two weeks and need expansion within six weeks of the deployment process. 

Limitations of this process:

  • Higher cost

This process is difficult to find and also very costly. Many of these projects usually run longer than planned and thus can be expensive. Though the third-party platforms offer scope for creating logic-based and simple chatbots, those are not sufficient for complex use cases.

  • Limited flexibility

Scalability and flexibility are also two challenges for the virtual assistants that are built in this way. 

Therefore, though there are different ways of deploying conversational AI for voice, all of those need careful planning and implementation to enjoy real success.

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