If I asked you to raise your hand if you’d ever interacted with some kind of chatbot, I’m sure you’d raise it high. They can be helpful and exciting to use. However, not all conversational question and answer interfaces or tools are made equally. You can probably think back to a time or two where your chat experience was less than exciting or helpful for that matter. Let’s take this discussion through the lens of something we have all experienced, regardless of our role or the industry we work in, and that is needing to get the answer to a question.

Whether you’re a customer engaging with customer service, an HR professional helping an employee, an agent in a call center, a sales representative supporting a prospect, or even a marketing specialist answering product questions. Getting information quickly and efficiently makes life so much easier. Answering questions quickly and correctly is also a critical part of every business, and is often a place where you might find a chatbot or two. Question answerers, for example, customer service agents, gain real benefit when AI helps them work more efficiently. However, when a question answerer, a knowledge worker, or a human agent a knowledge worker, or a human agent gets assistance from a chatbot, it’s usually to help with simpler or repetitive questions.

However, if the experience for the question asker isn’t seamless and perfect, that’s where things start to go wrong. Believe it or not, there are key differences between the building blocks and technological components of chatbots and AI assistants that make this experience either successful, or one where you are typing or yelling “agent” into your device to get you to an agent or knowledge worker ASAP. Chatbots have become a bit of a catch-all term. They refer to a computer program being able to answer a human question whether using gen AI or not, which is what can often create confusion. Chatbots traditionally are built with things like decision trees, rules engines, and often have a limited list of questions that they can support a user around.

On the other hand, chatbots powered by generative AI technology are better identified as AI assistants. They’re powered by AI capabilities like natural language processing, natural language understanding, and machine learning so that they can understand and correctly assist a user question and match them to specific needs. AI assistants also have the ability to learn over time, which we call deep learning. They also have memory capabilities that allow them to remember the history of a user’s inquiries and better assist them. Robust AI assistants may also have automation capabilities that allow them to execute tasks on the back end, like send an email or update a user’s account information.

Let’s explore a scenario to compare chatbots and AI assistants. A customer, let’s call them Janice, is needing to get information about a certain offering or service. According to this company’s website, using a chatbot is the best way to get an answer to Janice’s question. Great. Upon visiting the tool, Janice will have very different experiences if the tool is a traditional chatbot or an AI assistant.

Traditional chatbot experience might go a little something like this. Janice types in her question using natural language, but the chatbot doesn’t quite understand and asks her to select from a preexisting list. Maybe it’s a FAQ, order, inquiry, or other. Unfortunately, the list doesn’t completely address what Janice needs help with, so she selects other and types in her question using slightly different terms. The list and the terms aren’t what Janice needs, and unfortunately she can’t get an answer, so she has to route to the agent.

This is fine because the agent is able to help Janice with what she needs, but it doesn’t lend to any productivity or efficiency gains. Now let’s talk about the AI assistant experience. Here, Janice can simply type in her question using natural language, and the AI assistant will understand, answer the question, and even provide other assistance, helpful information, or even relevant links depending on the context of Janice’s question. Personalization is also available with AI assistants, so the AI assistant can greet Janice by name. The call center agent doesn’t have to spend time with Janice because her question is easily answered, and the customer service department can work efficiently, save time, and address ever increasing customer expectation of response times.

The agent can continue working on more complex customer needs without interruption, and Janice has what she needs. When it comes to AI, humans bring capability and machines provide scalability. Remember chatbots and AI assistants have different building blocks that really create clear differentiation. Choosing your technology carefully is critical to ensure the right experience for your end user, like Janice. I would argue that chatbots in the way that I’ve described in this video are a thing of the past.

Tangible business value and productivity gains are key drivers for innovation. When it comes to using AI, AI assistants can bring major ROI, like empowering knowledge workers or employees, modernizing applications, or addressing skills gaps all by simply answering questions quickly, effectively and accurately. AI assistants are the future, and the future is already here.

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