AI has officially shed its buzzword status. It’s now the backbone of many customer interactions, from customer service to product research. More than half of customer management leaders have already invested in CX-focused AI, and 30% plan to deploy a new generative AI (GenAI) tool within six months, according to a recent survey.
But while adoption of the technology is picking up fast, many leaders are still figuring out how to use GenAI meaningfully—or even fully understand what distinguishes it from customer experience (CX) automation tools they’d been using already, like chatbots.
AI-powered chatbots and GenAI are both technologies that can improve CX. They’re both built on machine learning (ML), a branch of AI that enables systems to analyze vast amounts of data and make predictions. While they differ in function and application, they’re not mutually exclusive—many modern chatbots now use GenAI to deliver more natural, dynamic conversations.
To understand how these technologies work together, and where they diverge, let’s look at each one more closely.
What Is a Chatbot?
A chatbot is a type of automated interface that simulates conversation with humans. Early chatbots have been rule-based, but they have increasingly used conversational AI. Conversational AI combines ML and natural language processing (NLP) to understand text or voice and then respond in natural language. Conversational AI can detect a person’s intent and emotional state through sentiment analysis.
Chatbots are often used in customer service settings to answer questions and offer support. In fact, 68% of organizations now cite chatbots as a top use case for AI in customer experience.
Well-designed chatbots can:
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- Use existing conversation data to understand the types of questions people ask
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- Analyze accurate responses to those questions
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- Employ machine learning and natural language processing to grasp context, improving their ability to answer those questions correctly
Some of today’s most advanced chatbots are powered by GenAI, which allows them to generate more dynamic, personalized responses. It also blurs the line between traditional chatbots and GenAI applications.
What Is Generative AI (GenAI)?
Generative AI is a type of AI that can create new content using large language models (LLMs). LLMs analyze existing content to find patterns and make predictions, allowing them to create new text, images, computer code and even music in response to inputs. GenAI can be used in chatbots to generate more dynamic, personalized responses based on user prompts.
According to IDC, “the primary application for early versions of GenAI is in AI-driven chatbots and agents for contact centers and customer self-service. [IDC believes] GenAI will enable more personalized product recommendations through insight analytics…and faster resolution of customer complaints.”
What Are the Differences Between a Chatbot and Generative AI?
While chatbots and GenAI are often compared with each other, it’s more accurate to think of them as overlapping technologies. Many modern chatbots use conversational AI or even GenAI to enhance their capabilities. Here’s how the three approaches compare.
| Traditional Chatbot | Conversational AI Chatbot | GenAI | |
|---|---|---|---|
| Focus | Rule-based conversation: – Answer FAQs – Follow scripted flows | Context-aware interaction: – Understand intent – Respond naturally | Creating new content: – Text – Images – Audio – Code |
| Underlying Technology | – User-selected options – Simple text | – Machine learning – Natural language processing (NLP) | – Large language models (LLMs) – NLP – Deep learning |
| Input | – User-selected options – Simple text | Human language (text or voice) | – Data (structured or unstructured) – User prompts |
| Output | Predefined responses | Human-like responses based on context | New content |
| Capabilities | – Handle basic tasks – Provide static info | – Hold dynamic conversations – Adapt to user intent | – Create original content – Summarize or transform data |
| Limitations | Limited flexibility | May struggle with complex or emotional nuance | Can hallucinate or misinterpret prompts |
How Can Chatbots and GenAI Be Used to Enhance CX?
| CX Applications | Benefits | |
|---|---|---|
| Traditional Chatbot | – Customer service – Appointment scheduling – Information lookup via menus or keywords | – 24/7 availability – Reduces basic support load – Enables self-service for routine tasks |
| Conversational AI Chatbot | – Virtual assistants – Context-aware customer support – Intelligent routing to agents | – More natural, human-like interactions – Reduces customer effort by understanding intent – Collects actionable data from conversations |
| GenAI | – AI-powered chatbots and virtual assistants – Personalized content and recommendations – Feedback analysis (e.g., survey insights) – Social media response automation – Voice assistants (e.g., Alexa, Siri) | (Includes benefits of chatbots) – 1:1 personalization at scale – Real-time translation and summarization – Surfaces insights from unstructured data – Scales content creation and saves staff time |
How to Implement Chatbots and GenAI for CX Improvement
What steps should you take to implement these AI use cases? They’ll vary depending on whether you’re deploying a traditional chatbot, a conversational AI chatbot or a GenAI-powered solution. Here’s how to approach each in five steps.
Traditional Chatbot Implementation
- Define goals and use cases: Focus on automating routine tasks like FAQs, appointment scheduling, or basic information retrieval. These bots are ideal for handling predictable, repetitive queries.
- Design rule-based flows: Use decision trees or keyword triggers to guide users through predefined paths. Keep the flows simple and intuitive.
- Choose a simple platform: Many no-code tools support rule-based chatbot creation, making it easy to get started without heavy development resources.
- Test and deploy: Ensure the bot handles expected queries and reliably escalates to human agents when needed.
- Monitor and refine: Use analytics to identify if customers hit a snag in their journeys as they use this channel. Apply the insights to improve the chatbot flows or adjust the rules. Even simple bots benefit from ongoing optimization.
Conversational AI Chatbot Implementation
- Understand your audience: What do you want to achieve with chatbots (e.g., faster resolution of common issues)? You must understand the needs, behavior and preferred communication style of the customers who will be using the chatbot.
- Design the chatbot experience:
- Functionality: What specific tasks will the chatbot handle? Examples: Answer FAQs, troubleshoot problems, collect customer information.
- Personality and tone: Align with your brand voice—friendly and informal, or professional and authoritative?
- Conversation flow: Make it clear, concise and user-friendly. Include options for users to easily switch to a human agent if needed.
- Train with real data: Choose a development platform that suits your needs and technical expertise. Train the chatbot with a massive data set of relevant information, including your internal FAQs, customer support transcripts and product knowledge. This is a form of retrieval augmented generation, or RAG.
- Integrate across channels: Deploy the chatbot across your website, mobile app or messaging platforms. Thoroughly test it to ensure it understands user queries and provides helpful responses.
- Continuously optimize: Gather feedback from customers. Monitor performance through analytics and continuously refine the chatbot’s training data and conversation flows.
Generative AI Implementation
- Identify pain points and choose a use case: Pinpoint a specific journey that could be improved using GenAI. For example, in the payment journey, bill confusion is a leading reason for billing-related calls. An AI-assisted digital bill explanation tool provides a personalized bill summary that helps customers understand charges and reasons for month-to-month changes. The tool reduces bill confusion and contact center calls.
- Start with clean, well-structured data: GenAI output is only as accurate as the input data it’s trained with. That requires effective data management and integration practices.
- Maintain compliant processes: To protect data privacy, you must follow existing rules (e.g., obtain customer consent) and quickly adopt new ones as AI and the regulatory landscape evolve.
- Establish continuous monitoring: Analyze business metrics and key performance indicators to measure success. For example, to test the GenAI-assisted bill explanation tool, you might monitor the number of billing calls your human agents receive from customers who can access the tool, and compare it with the billing contact rate from customers who don’t have it.
- Scale thoughtfully: Use GenAI with a focus on augmenting (rather than replacing) human agents and existing automation. Look to additional use cases where GenAI adds value through personalization, summarization or insight generation.
CSG Makes Chatbots and GenAI Work—For Your Business and Your Customers
Chatbots and GenAI are both powerful tools to improve customer experiences, but only when they’re applied with a purpose. That’s how we approach those technologies with our customer engagement platform, CSG Xponent. Xponent’s conversational AI doesn’t just recognize words—it understands what customers mean, how they speak and what they need. That means more natural conversations, faster answers and better experiences.
CSG Bill Explainer uses generative AI-driven, personalized statement summaries to guide customers through their bill, helping them understand charges and month to month variations. Bill Explainer reduces bill confusion and contact center calls and encourages prompt payment. It’s how you can turn a common frustration into a trust-building moment—and a better customer experience.
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