Is Conversational AI Better Than Chatbots? and What's the Difference Between Them?

August 6, 2020

Conversational AI platforms are gaining popularity, and companies embrace these technologies to automate their customer service operations, providing their customers with the answers they need, whenever they need it.

There is a difference between conversational AI and chatbots, some might even say one is better than the other. But to know if that is indeed true, we first need to understand what each other has to offer, and whether it can fit both our business and customer needs. And that’s exactly what we’re going to do.

Conversational AI vs. Chatbots

Conversational AI and chatbots serve the same goal - automating customer communications. But each one operates very differently:

The traditional chatbots are rule based and navigate the conversation by relying on complex maps and decision trees. That means chatbots are strictly limited to predefined scenarios, that has to be thought out and created by team members, executives or dev teams.

As you can imagine, the complexity of setting a chatbot up can (and often does) lead to a poor experience for the end user. In other words, companies are realizing that chatbots just can’t get the job done.

Conversational AI has a different approach. Instead of relying on “if-then-else” rules to navigate the conversation, it leverages NLU (Natural Language Understanding) to analyze what the user is saying, and provide a personalized answer. This allows conversations to evolve in different ways, enabling a more human-like experience.

For example, a customer can ask “Add eggs and blueberries to my cart”, and then have a change of heart, saying “Actually, I don’t want the blueberries”. The AI can decipher the request, put it in context and fulfill the request.

Chatbots, on the other hand, might struggle understanding there’s a connection between the two sentences, and will have to ask clarification from the customer - which can quickly lead to frustration with the process and the brand.

Simply put, conversational AI is trained to think like a human, not a robot, which makes it the ultimate successor to chatbots.

The Downside of Conversational AI

We can establish that conversational AI offers a seamless and ultimately better customer experience. And indeed, ever since it was first introduced in 2015, it came with a big promise to provide a 10x better experience and make customer support more efficient.

However, most companies still struggle to adopt conversational AI due to their dev-centric and generic approach, which includes an implementation process that often involves heavy setup and maintenance to perform well. The setup and maintenance process often includes:

  • Identifying User & Business Needs
  • Conversational Designing
  • Collecting Data & Training the AI Model
  • Integrating with Data Points and CRMs
  • Optimization

In most cases, every single step in this process is critical for building a good conversational experience, and can involve various domain experts across the company to design and successfully implement these platforms.

Enterprises and large companies such as Walmart or Bank of American can afford to spend time, money and resources on integrating these platforms, and build automation teams dedicated to the mission.

Smaller companies usually don’t have the budget or can’t allocate tens (and sometimes hundreds) of thousands of dollars for these kinds of solutions. More than the costs, most companies don't have the time required to invest in such projects - they need a solution that can be up and running in no time to address current issues.

It’s important to note that there are a few friendlier AI platforms that require less engineering work. However, the main part of training, designing, and maintaining the AI is still required.

What About Adaptive Conversational AI?

While both conversational AI and adaptive platforms result in a human-like conversational experience, they differ in how they are implemented and maintained.

Unlike traditional platforms that require long training cycles and detailed design to perform well, adaptive conversational AI platforms leverage business data to automatically design, train, and optimize the conversational experience.

The process of implementing an adaptive conversational AI involves the following phases:

  • Data

The first step is connecting with sales and support tools in your organization to get the relevant data.

  • Discovery

The adaptive platform will analyze business data such as phone calls, chats, emails, etc. to find repetitive cases that could be automated.

For example, for a standard e-commerce company, the adaptive platform would probably discover popular questions that include “Where’s my delivery” as a popular topic that can be automated.

Next - and this is important - we need to understand how these conversations are structured: How do they look like? What are the follow-up questions customers are asking in a specific conversation? Answering these queries will help us design effective conversations, which take into account the customer’s needs, business policy and results for a better experience.

  • Automation

Every insight that was discovered during the analysis phase, will then be used by the Adaptive AI to design and train a traditional Conversational AI and NLP model.

  • Monitor and Optimize

Conversational AI requires maintenance. Customer needs are evolving and changing over time as well as the business needs. People will react differently to automated conversations, and it is important to monitor these changes, and adjust accordingly. This is probably the most important part to successfully embrace AI. The nice thing about Adaptive platforms is that they will do this for you. This is the “Adaptive” part. It will automatically supervise the Conversational AI model, learn what works and what doesn’t - and then optimize the experience over time.

Is It Worth It?

Though they are built on top of conversational AI, adaptive platforms have two major advantages over traditional conversational AI:

Better Performance

The trickiest part is to design and get the right data to train the AI model. Usually, AI models are trained with irrelevant data, which is either synthetic or doesn’t reflect real-life situations.

Adaptive platforms are trained on the business-specific data, usually taken from the company’s CRM. As they “adapt” to the business and user needs, they reflect real-life conversations and use-cases, and can provide an overall better experience.

Minimal Setup & Maintenance

Adaptive platforms are known for their ease-of-use which, unlike dev-centric platforms, can be used and implemented by customer service directors, marketing managers, etc.

In most cases, adaptive conversational AI can be implemented in a few hours, without engineers or code involved. Once installed, the adaptive platform takes over as an autopilot that supervises and monitors the performance of the conversational experience - making it 10x easier to accomplish your automation goals.

Remember to Put Your Customers First

It’s clear that chatbots on their own just won’t cut it, and conversational AI is the better choice between the two. However, when it comes down to giving your customers the best experience possible, you’ll need to add an adaptive platform to that mix.

At the end of the day, and as the demand and issues increase, you need something that will help take the load off your CS and CX teams, without harming the relationship between you and your customers.

Whether you choose to keep your existing chatbots as is, or upgrade it with conversational AI - don’t forget to take the time to adjust those to fit your company and your customers’ specific needs, to make sure they’re getting the best experience possible.

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