Chatbot vs. conversational AI: Key differences for B2B teams

Conversational artificial intelligence (AI) is designed to simulate conversations through text or voice chat. It relies on natural language processing (NLP) to understand messages and generate responses. Machine learning (ML) technology helps AI learn from these conversations, so future responses are more helpful.

Sometimes, a chatbot is a subset of conversational AI, using NLP to answer customer questions. Other times, the technology is even simpler, relying on a straightforward conversation tree to chat with people. If a customer asks about X, then answer with Y.

The difference between chatbots and conversational AI

While the terms are often used interchangeably, they’re not exactly the same. Below, we’ll go over some key differences between chatbots versus conversational AI for B2B teams.

Tool	Underlying technology	Flexibility	Context-handling
Chatbots	Rely on predefined conversation flows, rules, and, in some cases, basic AI technologies	Generally less flexible because they follow predefined scripts and rules, making conversations less dynamic	Have limited ability to handle complex queries, maintain context, or learn from customer interactions unless combined with AI
Conversational AI	Powered by ML and NLP technologies	More flexible because it can adapt to user intent, understand nuances in language, and improve over time	Can manage complex requests, maintain context throughout conversations, and retain context across multiple interactions, enabling natural and personalized experiences

Chatbot and conversational AI use cases in customer service

In B2B customer service, chatbots are most compatible with predictable workflows where requests are simple. They pull information from knowledge bases to handle FAQs about account management, pricing, and payment methods.

Chatbots are also a great asset for providing 24/7 self-service, as they answer questions when human agents are outside working hours. If customers do need to talk to a person, chatbots can route them to the right team.

Conversational AI typically fits well in dynamic environments where a solid understanding of intent and past interaction matters. For example, it can qualify leads, offer tips during onboarding, and route requests to the most qualified human agent. Rather than just talking, advanced conversational AI can also perform tasks. Top tools can execute workflows, integrate with customer relationship management (CRM) systems, and coordinate discussions across teams.

How to choose between a chatbot and conversational AI

How common are the customer conversations in your team’s inbox? Are they context-dependent? The answers determine whether you need a chatbot or conversational AI.

If your customer support team handles a lot of repetitive, simple queries, chatbots are a better fit. These requests are less context-dependent, meaning bots typically don’t need to think through responses or write personalized messages. They can answer questions quickly and accurately by sharing information found in knowledge bases.

On the other hand, if your team mostly manages complex, multi-step questions, conversational AI is a better fit. It can adapt to the evolving context of each interaction and tailor responses accordingly.