Are Chatbots AI? How to Differentiate Chatbots From Conversational AI

What is a rule-based chatbot? 

Rule-based chatbots are the simplest form of chatbots. They’re programmed to respond to user inputs based upon a set of predefined conversation flows — in other words, rules that govern how they reply.

The computer programs that power these basic chatbots rely on “if-then” queries to mimic human interactions. Rule-based chatbots don’t understand human language — instead, they rely on keywords that trigger a predetermined reaction.

Rule-based chatbots on websites

The most common use of rule-based chatbots is on companies’ websites. After the page has loaded, a pop-up appears with space for the visitor to ask a question.

A visitor might ask a question like “Do you have wireless headphones in stock?” The chatbot picks out the phrases “wireless headphones” and “in stock” and follows an instruction to provide a link to the appropriate page.

The visitor could also say, “What are your store opening hours?” Upon seeing “opening hours” or “store opening hours,” the chatbot would give the store’s opening hours and perhaps a link to the company information page.

When a visitor asks something more complex for which a rule hasn’t yet been written, a rule-based chatbot might ask for the visitor’s contact details for follow-up. Sometimes, they might pass them through to a live agent to continue the conversation.

Other uses for rule-based chatbots

Rule-based chatbots can also be used to resolve customer requests efficiently. For example, they can help with basic troubleshooting questions to relieve the workload on customer service teams.

When integrated into a customer relationship management (CRM), such chatbots can do even more. Once a customer has logged in, chatbots can be trained to fetch basic information, like whether payment on an order has been taken and when it was dispatched.

Some companies use rule-based bots to help their internal teams. An employee could ask the bot for information on human resources (HR) policies, such as employment benefits or how to apply for leave. They could also ask the bot technical questions on an information technology (IT) issue instead of having to wait for a reply from their IT team.

Reaching the limits of rule-based chatbot usage

In recent years, the level of sophistication in the programming of rule-based bots has increased greatly. When programmed well enough, chatbots can closely mirror typical human conversations in the types of answers they give and the tone of language used.

In truth, however, even the smartest rule-based chatbots are nothing more than text-based automated phone menus (IVRs). If an IVR answers your call and you press a button that doesn’t have an assigned option, it doesn’t know what to do except to read the menu options again to you.

These virtual assistants remember nothing, either. The appearance of natural language understanding is a mirage. 

The origins of rule-based chatbots go back to the 1960s with the invention of the computer program ELIZA at the Massachusetts Institute of Technology’s Artificial Intelligence Laboratory. So, the technology that powers these chatbots is now more than 60 years old.

What is a conversational AI chatbot? 

When OpenAI launched GPT-1 (the world’s first pretrained generative large language model) in June 2018, it was a real breakthrough. Sophisticated conversational AI technology had finally arrived and they were about to revolutionize what chatbots could do.

These new conversational interfaces went way beyond simple rule-based question-and-answer sessions. These smart virtual agents could now identify user intent. They could also solve more complex customer issues without having to resort to human agents.

They’re now so advanced that they can detect linguistic and tone subtleties to determine the mood of the user. They remember previous interactions and can carry on with an old conversation.

Even the most talented rule-based chatbot programmer could not achieve the functionality and interaction possibilities of conversational AI. This is a technology capable of providing the ultimate customer service experience.

Let’s look at how conversational AI systems work.

How a conversational AI bot seems to understand human language

To work, conversational AI relies on three things:

1. Natural language processing (NLP): NLP is a key component, as this is what the technology uses first to understand human language and then to generate it. NLP is the tool that allows conversational AI to grasp nuance, context and linguistic structure. It’s what makes the AI output almost indistinguishable from a human being.
2. ML: Conversational AI runs on large language models (LLMs). These are huge databases that have billions of snippets of human language. Developers then “train” the AI using advanced ML to sound like humans.
3. Ongoing analysis of data: Conversational AI remembers. It constantly reviews how it does in every interaction. Its goal is to find ways to interact with humans in a more personalized, contextual and accurate way. This never stops and that means it gets better with each new conversation.

Although it gets some direction from developers and programmers, conversational AI grows and learns through its own experience. This is a stark contrast to rule-based chatbots.

AI-based chatbots can help you sell more

Conversational AI solutions can be trained to serve a purpose. One of those could be helping your website customers to find what they want.

Upload your product catalog and detailed product descriptions into your chatbot. Tell it that its mission is to provide customers with the best possible advice on which products they should buy.

For example, let’s say that you run an online laptop store. Your visitor might ask something like “Which laptop is best for editing YouTube videos that cost less than $1,500?”

Now it has in-depth knowledge of each of your products, your conversational AI agents can come into their own. Because your chatbot knows the visitor wants to edit videos, it anticipates the visitor will need a minimum level of screen quality, processing power and graphics capabilities.

It finds all qualifying laptops and presents the options to the customer. It can then follow up with another question, like “I found several laptops that are great for video editing under $1,500. Do you have a preference for screen size or battery life?” 

The AI can narrow down the choices further based on the visitor’s answer. You could even prompt your chatbot to ask the visitor about preferred warranties and after-care packages. Ultimately, the AI takes them through to the shopping cart to complete the purchase.

Rule-based chatbots, the previous dominant automated messaging technology, could never handle something this complex.

Integration with and inclusion within CRM systems

Independent chatbot providers like Amelia provide direct integrations of its technology into the important business apps companies use, such as order management systems. Many of the best CRM systems now integrate AI chatbots directly or via third-party plug-ins into their platforms.

A chatbot integrated within a CRM can help your customers do the following once they’ve logged in:

• Track orders: Your AI chatbot can let customers check their order status so that they know when their order will arrive. They could even get tracking numbers so they can follow their parcel’s process on the courier’s website.
• Manage invoices: Particularly useful if you sell to business customers, it allows clients to circumvent contacting your accounts team and request purchase invoices from your chatbot. 
• Arrange pick-ups: Customers can request, change and cancel pickup and delivery times with their chatbot. Going one step further, your chatbot could manage much of your returns process.
• Update account info: Rather than wait in line to speak to accounts or customer services, your customers could change their account information via the chatbot as well as on the portal on your site.
• Handle warranty queries: Your customers can check how long is left on their warranty for particular items. They could even arrange warranty claims through the chatbot and come back later to ask where their warranty claim is in the process.

Machines are not the answer to everything but AI’s ability to detect emotion in language also means you can program it to hand over a case to a human if a more personal approach is needed.

How AI conversational bots can empower your customers

An AI bot can do much more than provide quick customer support as well. A growing number of companies are uploading “knowledge bases” to their website. They are centralized sources of information that customers can use to solve common problems as well as find tips and techniques on how to get more from their product or service.

AI chatbots are equipped to handle complex customer interactions so they’ll be able to take customers step by step through a troubleshooting process or show them how to perform a particular task faster than they are now.

Deploying intelligent chatbots for your staff

We saw earlier how traditional chatbots have helped employees within companies get quick answers to simple questions.

CRM-based chatbots now offer the ability to:

• Handle employee queries: Simply upload your employee handbooks, software and IT manuals and more and encourage your staff to use the chatbot first before reaching out to a human team member.
• Deal with HR: Feed your AI with information employees want to know more about, like how long they can take off for vacation or details of your workers’ compensation scheme.
• Handle customers better: Some CRM systems now will analyze previous interactions with clients and present on-screen prompts to help human customer service reps achieve case resolutions faster.

How are rule-based chatbots and conversational AI chatbots similar and different? 

As we’ve seen, the technology that powers rule-based chatbots and AI chatbots is very different but they still share much in common.

Both chatbots’ primary purpose is to provide assistance through automated communication in response to user input based on language. They can answer customer queries and provide general information to website visitors and clients.

But because these two types of chatbots operate so differently, they diverge in many ways, too. Conversational AI adapts and learns, building on its experience and its ability to understand natural language, context and intent. Rule-based chatbots cannot break out of their original programming and follow only scripted responses.

Every conversation to a rule-based chatbot is new whereas an AI bot can continue on an old conversation. This gives it the ability to provide personalized answers, something rule-based chatbots struggle with. AI bots are more capable of connecting and interacting with your other business apps than rule-based chatbots.

How can businesses use rule-based chatbots and conversational AI chatbots? 

Chatbots are not just online — they can support both vocal and text inputs, too. You can add an AI chatbot to your telephone system via its IVR function if your supplier supports it. Using voice recognition, it can listen to the customer and, through access to its training and CRM data, respond using voice replication technology.

Despite the technical superiority of conversational AI chatbots, rule-based chatbots still have their uses. If yours is an uncomplicated business with relatively simple products, services and internal processes, a rule-based chatbot will be able to handle nearly all website, phone-based and employee queries.

However, conversational AI chatbots are better for companies that want to offer customers and employees a detailed and responsive service that’s capable of handling more challenging external and internal queries. If your business requires multiple teams and departments to operate because of its complexity or the demands placed on it by customers and staff, the new AI-powered chatbots offer much greater value.

Mikhail Naumov contributed to this article.