With one in five Americans admitting to using a chatbot within the past month, generative AI is becoming increasingly commonplace — and intelligent.
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. But while chatbots are simple applications that merely simulate basic conversation via text, conversational AI bots can actually engage in human-like dialogue and emotions.
So if you’re looking for some artificial support but you’re not sure which tool to choose, don’t worry! We’ve compiled all the must-know info about chatbots versus conversational AI below.
Chatbots vs conversational AI: What are they?
The terms “chatbot” and “conversational AI” are often used interchangeably — however, in reality, while all conversational AI can be considered chatbots, not all chatbots qualify as conversational AI:
- 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 for structured, repetitive tasks — for example, tracking a customer’s order.
- Conversational AI is a broader, more advanced technology that uses natural language processing (NLP), machine learning, and large language models (LLMs) to engage in dynamic, human-like conversations. It is capable of understanding, processing, and responding to human input in a dynamic, contextual manner — think OpenAI’s ChatGPT or Anthropic’s Claude.
Chatbot vs conversational AI: Side by side
At their core, chatbots and conversational AI both facilitate communication between humans and machines — but the level of sophistication sets them apart.
A rule-based bot can efficiently guide you to the right information but struggles if you type something unexpected, like “Can you recommend a gift?” Even simple changes in the way you phrase common queries can limit its ability to interact — for example, instead of saying “What is the return policy?” you say, “How can I return the clothes I bought yesterday?”
Conversational AI, on the other hand, develops with data, learning from previous interactions to provide a more personalized customer experience — like offering a particular discount or upselling based on client preferences.
| 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 customer experience and span complex processes. |
| Ideal use cases | Simple FAQs, greeting customers, tracking orders, collecting basic data, troubleshooting | Customer service automation, sales lead generation, real-time language translation, virtual assistants, personalized product recommendations |
Chatbots vs conversational AI: Customer service use cases
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.
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 outlook for 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.
What might this look like? We can expect to see more hybrid models combining rules-based thinking with natural language processing, ultimately creating a more powerful customer service tool that’s truly available 24/7.
Though there’s still a long way to go before companies can trust AI bots to operate autonomously. For example, 2024 alone saw New York’s MyCity chatbot encourage users to break the law, while AirCanada had to pay damages to a passenger after a virtual assistant gave them incorrect flight information.
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Photo by Anna Shvets
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