While the differences between a chatbot and a conversational AI solution may not be evident, they are pretty extreme. Have you ever had to repeat yourself multiple times because the robot on a service line didn’t understand what you were saying? That was a chatbot. Alternatively, if you could have a natural conversation with a robot that helped you solve your problem, chances are that was a conversational AI solution.
That is not to say that all chatbots lead to frustrating CX. The difference between a chatbot and a conversational AI solution is between having a prerecorded fixed set of instructions and being able to talk to an intelligent robot that can solve your problems.
In this entry, we will discuss in depth what a conversational AI solution entails and its primary components.
Conversational AI is a technology or combination of technologies that can effectively understand and answer multiple human conversations. It is effectively the solution that allows humans to talk to machines naturally to solve their problems.
Unlike its predecessors, conversational AI can articulate a conversation with users using natural language processing and machine learning technologies. However, plenty of other technologies play a role in the entire journey of a successful conversation.
NLP is a branch of artificial intelligence that allows computers to process, understand, and generate successful responses to everyday conversations. While the underlying processes for NLP may change depending on the conversational AI provider, there are usually layers of computational linguistics, statistical modeling, and deep learning. In other words, the computer needs to process the language (NLP), understand its context and intent (NLU - natural language understanding), and provide an answer (NLG - natural language generation.)
It’s important to note that these processes must happen in instants, pushing the boundaries of previously used closed decision trees for a much more robust option.
ML is the ability of computers to use algorithms and statistical models to understand and draw connections between multiple data patterns to provide answers. Unlike previous technologies, which were narrower and had to follow specific instructions, machine learning refers to the technology's "learning" capacity. As it learns more, it can provide more nuanced answers that are adaptable without having to be previously programmed for them.
When it comes to conversational AI, ML is continuously updated to improve response quality, working in tandem with NLP processes.
The next important process during a conversational AI flow is the context and state management stage. This is where AI tracks multiple states and data points, including preferences, previous interactions, ongoing tasks or problems, etc. By doing this, the conversational software identifies the context of the conversation and can proactively adapt to better serve the customer and solve their problems.
After the conversational AI machine has identified the context and the problem this person is facing, searching through an information repository allows it to provide quick answers. These repositories are usually a combination of FAQs, product details, and customer data combined with everything the machine has learned from previous similar interactions.
Finally, personalization based on interactions occurs once intent, potential ways to help the user, and other contextual data points are clear. While this may seem like an afterthought, it is an essential part of the process, given that the person calling or interacting via text with the conversational AI solution experiences a human-like interaction instead of a robotic one.
Unlike simplified solutions like IVRs or chatbots, conversational AI provides responses similar to what an actual human agent might provide. On top of that, it also can quickly identify if the person calling is experiencing a complex scenario that requires human agents to redirect them diligently.
Conversational AI is a technological system that works in synchrony to understand human intent, search for the right information to solve a problem, and communicate with users in the most appropriate manner.
There are multiple reasons why conversational AI is a superior option to regular chatbots or IVRs. However, one characteristic stands out: natural language processing.
Chatbots and IVRs are helpful to users but are also limited, given how the work is, by following a previously built decision tree. If the problem or the intent behind the interaction with an IVR or chatbot falls within those predetermined parameters, it’s possible to get a helpful answer without human intervention. However, users will not get a satisfactory answer if the user talks about the problem in a way that is not logged in the decision tree or if the issue falls outside the most commonly asked journeys.
Conversational AI, on the other hand, has a natural language processing component that allows it to make sense of the information despite how users communicate it. It identifies intent and can quickly determine the best course of action, providing solutions in an instant or redirecting the call to a human agent if need be.
Conversational AI technologies are everywhere in our lives. For example, when it comes to personal relationships, products like Replika, an emotional support chatbot that simulates emotional connection, and Gatebox, similar to the former but with a holographic avatar, were created to help people build emotional connections with AI avatars.
There are also plenty of examples of conversational AI in the business realm. Products such as Optimizely, a marketing A/B testing tool that created an IVA system to set up, monitor, and analyze multiple tests in parallel to find the most successful ones.
All industries can benefit from using conversational AI to support their customer service and customer experience teams. Among the top benefits are providing 24/7 support with higher resolution rates, automating routine tasks, and improving personalization. As a result, it is typical for companies incorporating conversational AI to experience enhanced customer engagement and retention and reduced operation costs.
Conversational AI allows organizations to improve operations, provide better service, and free agents to do their best work by focusing on complex matters. Mosaicx is a conversational AI solution that integrates next-generation virtual agents and white-glove services to help enterprises meet their most ambitious goals.
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