Why Maturity in Conversational AI Matters

The term artificial intelligence (AI) is a lot broader than most people realize. AI is used in our homes to help us make our personal lives easier to manage, as well as in our jobs to help us get things done. An AI solution can be as complex as a virtual coworker helping you scale repetitive, time-consuming tasks essential to keeping revenue teams successful.

To better understand the different maturity levels of AI, you first need to understand what Conversational AI is and how it can be tailored to different uses.

A Quick Introduction to Conversational AI

As the name suggests, Conversational AI is any technology that processes human speech to provide services. Probably the two best-known examples of Conversational AI are Siri and Alexa, which are so ubiquitous that they don’t even need to be explained. Alexa has entered so many homes she can be triggered when anyone on TV or radio says her name – a trick that a couple of late-night hosts have used to send their viewers scrambling to stop purchases they don’t want made.

And that is exactly the power of Conversational AI. At its most sophisticated it can literally respond like a person and start to do whatever is asked of it.

While all types of Conversational AI are more complex than the average AI, there exists a range of complexities within its subcategories:

  • Chatbots are the most basic version, and they are often used by customer service sites to handle questions from people who visit websites. They do interact with people through written communication (online chat) instead of through vocal communications. Since there are only a few questions that most people want to ask, chatbots free up Support staff to handle other, less repetitive questions. New avenues are being explored for chatbots because of their efficiency in this particular area, including capturing contact info for Marketing teams.
  • Voice assistants are commonly used for more mundane tasks, such as setting alarms, checking the weather, or playing a music list. Alexa is perhaps the best-known example of this. You can do a lot with one of these units, but we tend to use them to do basic tasks on the fly. It’s much easier to yell out to a voice assistant to set a reminder when we have our hands full in the kitchen, are toting laundry, or while setting a timeout for a child.
  • Mobile assistants Mobile assistants are older than voice assistants, and Siri is easily the most well-known. It has the same capabilities, but typically requires pushing a button to activate. They can also do things like send texts and conduct other mobile app tasks, giving you a more streamlined approach to regular chores and tasks – after all, if you set a reminder on your smartphone—and the situation shifts a day or two later—you’ll be able to make the changes from your computer.
  • Intelligent Virtual Assistants Intelligent Virtual Assistants (or AI Assistants) are among the most complex types of Conversational AI designed specifically to help businesses, they are scalable to help attract, acquire, and grow a customer base. These AI Assistants combine the best of the other types of systems, giving you a way to automate repetitive, time-consuming tasks. This frees up staff so they can handle the more complicated human interactions such as closing deals with sales-ready leads or helping drive customer health. The next phase for Intelligent Virtual Assistants is to provide counseling to customers, relaying the kind of information that doesn’t change to help customers narrow their choices. From there, they can start talking to a member of customer service to make a final decision.

The differences seem fairly nuanced, but the real difference between them is the level of maturity used by each type of Conversational AI.

Different Degrees of AI Maturity

There are four levels of Conversational AI maturity, so you can tailor your service based on the complexity of the tasks to be completed.

  • The first level is largely a triage mechanism, like chatbots. They handle basic, repetitive questions with predetermined answers. Essentially, these very basic AI solutions help filter out easy questions so that Support staff can dedicate their time to more complicated issues. They are like a chatty version of an FAQ.
  • The second level can answer basic questions but also has the ability to interact with other systems to provide answers to less predictable questions. The most common uses for this level are for pre- and post-sale tasks, with online chat and email being some of the most frequent types of interactions.
  • The third level is driven more by accumulated data than by a purely reactionary AI. This level is able to make decisions about more complicated questions based on the data it accesses. This is the first level that offers a greater return on investment because it has a much more robust learning component. It is also the first real level of communication that acts more like human interactions as it can provide more information, even if the information is not solicited by potential customers. For example, if it sees someone that is looking into information, it can act more like a Sales Development Representative and step in to offer more information.
  • The fourth level is integrated across most or all systems, learning from the vast amounts of data from multiple front- and back-end systems. They are able to assess data from previous client interactions by the other levels, assess historical data, and across your organization to provide more accurate, effective decision making. It can augment the abilities of different departments.

The first two levels largely help to deflect costs. Conversational AI with third and fourth levels of maturity are connected to a lot more data, allowing them to learn and improve over time.

How Machine Learning Is Improving Conversational AI

The most mature Conversational AI relies on machine learning to become more effective and accurate. Machine learning is a subsection of AI technology that mimics human learning. In the same way that humans learn by experience or instruction, machine learning improves AI over time via interactions and data. Just like you are able to make better decisions as you age because of your experience, AI is able to learn more when it has a larger amount of data.

The fourth level of maturity for Conversational AI is achieved by collecting vast amounts of data to feed into the solution. As machine learning establishes patterns within the data, the Conversational AI solution improves and acts in an advisor capacity to help make decisions.

Interested in exploring even more about how Conversational AI is maturing to empower revenue teams? Watch our webinar with 451 Research: The Augmented Workforce – A Maturity Model for Intelligent Virtual Assistant Adoption.