Why Conversational AI and Machine Learning?


Traditional customer service technologies like interactive voice response (IVR) rely on menus and phone trees with preprogrammed responses. Conversational AI is a type of machine learning technology that understands natural language. No menu needed. The technology responds to almost anything a customer says. There are plenty of reasons conversational AI is preferred by both businesses and customers.



Low Costs

Decrease agent handle time


Productive Agents

Agents focus on complex requests


Loyal Customers

Improve satisfaction and promoter scores


Minimal Wait

Answer all calls right away


Quick Resolution

Resolve more types of tasks on first contact


Enjoyable Experience

Offer surprisingly easy and engaging service




How Does Conversational AI Work?


Conversational AI requires three elements: a gateway to accept the customer’s input, a speech engine to communicate back and forth, and an app framework to resolve customer requests.


The gateway converts voice data into digital data and sends it to the speech engine. If the conversation is taking place via text, like SMS or web chat, this conversion is not needed.


The speech engine compares each word against its word database and identifies the most important keywords. With these, the speech engine deciphers the intent and compiles a logical, natural sounding response. It sends this response back to the gateway, which delivers it to the customer.


This process improves all the time with machine learning. As conversational AI interacts with customers, machine learning allows the speech engine to collect and retain feedback. It learns what word combinations are the most common and learns what requests usually go together. This allows virtual assistants to sound more realistic and predict how to help the customer next.


The app framework is what handles the service automation. After the speech engine deciphers the intent, it tells the app framework to transfer funds, answer a question, pull account info and handle any other customer requests. The speech engine then tells the gateway that the app framework completed the task, and the gateway delivers this message to the customer.


Mosaicx combines the gateway, speech engine and app framework together, creating comprehensive conversational AI capabilities within a single solution.




Conversational AI vs. IVR vs. Chatbot


Early IVR systems simply routed calls to different agents. Modern IVRs are faster, more realistic and can resolve many common tasks without an agent, but despite these improvements, they still require a set menu and phone tree to figure out what to say and do next. They create an if-this-then-that interaction.


Conversational AI solutions offer the same voice-based service as an IVR, but without menus or if-then formats. Instead of recognizing a handful of inputs, they pull keywords from the entire lexicon of human language. The technology and experience is so different that products like Mosaicx aren’t considered IVRs at all. Instead, they’re referred to as intelligent virtual assistants (IVA), and they’re expected to become the standard in voice-based customer service automation, just as IVRs are today.


On the text and chat side, it seems everybody is looking for a chatbot. A chatbot interacts with customers without agent assistance, but most use the same menu setup as an IVR. If you want to discuss something outside of the chatbot’s handful of options, you’re out of luck.


In contrast, IVAs are actually interactive. Some IVAs, like Mosaicx, can integrate with a chat provider to create a text-based customer service tool that's much better than a chatbot. Mosaicx also lets you send and accept text messages from customers, making it a comprehensive, multichannel customer-service solution.








Experience an Example of Conversational AI


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