Chatbots are everywhere. With one in five Americans using a chatbot within the past month, this technology is becoming increasingly commonplace — and intelligent.
Rule-based chatbots were once the only type available, merely simulating basic conversation via text. But conversational AI bots have now entered the scene, and they can actually engage in human-like dialogue and emotions. It’s not hard to see why both are making a splash in the customer service sector — already more than a third of businesses are reportedly using AI for customer support interactions.
If you’re thinking about implementing AI in your business — particularly in customer service — it’s a good idea to understand these options and how they’re being used so you can apply the solution that benefits you most. In this article, we’ll explain the differences between a chatbot vs conversational AI and cover some examples of how they’re currently being used in the customer service sector./p>
Chatbots vs conversational AI: What are they?
According to Dominic Sobe, founder of HelpKit, the terms “chatbot” and “conversational AI” are often used interchangeably, which creates confusion. In reality, while all conversational AI can be considered chatbots, he says, not all chatbots qualify as conversational AI. Here are the definitions of each to clarify:
- Chatbots are simple, rule-based systems designed to carry out specific tasks using predefined scripts. They’re typically programmed to respond to specific triggers or keywords. They follow a decision-tree logic and are best suited to structured, repetitive tasks.
- Example: Think of a chatbot on a retail website that asks, “What can I help you with today?” and offers options like “Track my order,” “Change my address,” or “View return policy.”
- Conversational AI is a broader, more advanced technology that uses natural language processing, machine learning, and large language models to engage in dynamic, human-like conversations. It’s capable of understanding, processing, and responding to human input in a dynamic, contextual manner.
- Example: Think of OpenAI’s ChatGPT or Anthropic’s Claude, where you can ask open-ended questions like, “What’s the best gift for someone who loves tech gadgets?” and get a personalized, detailed response. These systems can understand context, adapt to follow-up questions, and provide nuanced, conversational answers. This is also the technology that powers virtual assistants such as Google Assistant and Siri.
Overall, while a rule-based bot can efficiently guide you to the right information, it struggles if you type something unexpected, like “Can you recommend a gift?”, or use a different phrase for a common question — for example, saying “How can I return the clothes I bought yesterday?” instead of “What is the return policy?”
The key differences between a chatbot vs conversational AI
At their core, chatbots and conversational AI both facilitate communication between humans and machines — but the level of sophistication sets them apart.
According to Arvind Rongala, founder of Edstellar, “In theory, conversational AI develops with data, whereas chatbots depend on preprogrammed processes. Chatbot applications include organizing appointments and tracking orders. Personalized interactions, including settling conflicts or upselling based on client preferences, are where conversational AI shines. For example, after reviewing past purchases, AI may provide customized discounts, resulting in a unique and interesting experience.”
Maurizio Isendoorn, co-founder of Ringly.io, puts it this way: “A chatbot can be compared to an FAQ page, while conversational AI is an expert support agent that knows what you want. It can learn and improve over time, and check the history of the conversation as well.”
Here’s an overview of how rule-based chatbots and conversational AI bots differ:
| Characteristic | Chatbots | Conversational AI |
|---|---|---|
| Technology | Rule-based systems | Natural language processing, machine learning, and large language models |
| Interaction style | Basic, static responses that lack contextual understanding | Conversational, human-like dialogue |
| Mode of operation | Linear, following a set path | Can handle context switching, multi-turn conversations |
| Performance | Improves through manual updates and training only | Learns and improves through data-driven interactions |
| Types of tasks best suited to | Specific, preprogrammed tasks | Intricate tasks |
| Optimal environment | Businesses that require basic automation and simple workflows | Large organizations that aim to provide intelligent, human-like interactions to enhance the customer experience and span complex processes |
| Ideal use cases | Simple FAQsGreeting and guiding customersTracking ordersCollecting basic customer informationTroubleshooting | Customer service automationSales lead generationReal-time language translationVirtual assistantsPersonalized product recommendations |
Use cases for chatbots and conversational AI in customer service
Both rule-based chatbots and conversational AI are a good fit for customer service. Ultimately, they provide organizations with more resources to improve this all-important business function, with 90 percent of businesses saying they have witnessed faster complaint resolution thanks to chatbots — and that these bots have helped them increase their customer support satisfaction scores by 24 percent.
Case study: Tanganyika Wildlife Park
One such business making use of AI in customer service is Tanganyika Wildlife Park in Kansas. Chief operating officer, LynnLee Schmidt, says they’ve used both chatbots and conversational AI to interact with customers — and they’ve benefited in part because they’ve struggled to find human operators with the right customer service skills.
“If someone calls Tanganyika and it goes to voicemail, our chatbot automatically sends them a text message saying, ‘Sorry we missed your call, how can we help?’ The customer responds and then our human team takes over communication from there. Other times we use it to send automated messages to guests, like, ‘Have you bought your season pass yet?’ If a guest’s reply is positive the chatbot replies with ‘Awesome! Can’t wait to see you this season.’ Or, if the guest says no, then it might reply with, ‘Oh no! Don’t miss out, here’s the link.’”
The park also uses a conversational AI bot, named “Tanya.” Tanya is trained with information about the park’s products and services so that it can make recommendations, share information, and answer customer questions. After each conversation, Tanya sends a message to the human team with a summary of the call and any action items or follow-up on their part.
Overall, using bots of both types has leveled up Tanganyika’s customer service by allowing the park team to spend more one-on-one time with guests.
With this in mind, here are some of the key scenarios where businesses might benefit from each type of AI.
| Rule-based chatbots | Conversational AI |
|---|---|
| – Handling tier-1 inquiries (most basic and routine customer service inquiries) – Answering FAQs – Finding help articles – Escalating unresolved issues to human agents – Retrieving order status information – Handling refunds Booking appointments | – Navigating complaints – Guiding people to fill out forms – Asking people to complete orders – Creating support tickets for customers – Directing customers to products that fit their preferences – Answering questions about financing, coupons, and other transactional information – Providing customized discounts – Proactive customer outreach – Altering shipping requests |
The future of chatbots vs conversational AI
For now, chatbots and conversational AI remain distinct — but with conversational AI bots offering much greater power, flexibility, and adaptability, many experts predict that these two types of AI will merge in the near future.
According to Isendoorn, “Conversational AI has become so much better in just the last two years alone, and I don’t expect it to slow down anytime soon.”
Sobe agrees, saying rule-based chatbots will evolve to adopt more AI features, gradually closing the gap with conversational AI: “Expect more hybrid models combining rules-based systems with AI enhancements. In my opinion, the future is rooted in large language models and their continued evolution. AI systems will become even more efficient, contextually aware, and capable of supporting real-time, multi-turn conversations. These AIs basically are there for customers to respond to questions 24/7, they never take any vacation, and will continue to help companies save a lot of money.”
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