As of 2024, 82% of consumers would rather chat with a bot than wait for a customer service agent. This indicates that chatbots are transforming customer service, making them a must-have tool for businesses everywhere.
Conversational AI platforms are taking customer service further, offering smarter, more human-like interactions that improve marketing and customer engagement.
While “chatbots” and “conversational AI” may seem interchangeable, they differ significantly in scope, functionality, and use cases.
Chatbot vs. conversational AI: How do these two solutions compare, and which suits your business needs? Read on.
What is a chatbot?
A chatbot is a computer program designed to respond to customer queries. When a customer tries to initiate contact through your website or another communication channel, a chatbot will identify and resolve the customer's query.
It can also initiate a conversation with an opening sentence such as ‘How can I help you today?’. The user then has a set of pre-defined answers to choose from and carry the conversation forward.
Conventionally, a chatbot works on if-this-then-that logic. So it gives straightforward answers to queries.
There are three primary types of chatbots:
Rule-based
These chatbots operate based on predefined rules and scripts. This means they can only respond to specific inputs or keywords. Think of a basic FAQs support chatbot that answers commonly asked questions about the store’s business hours, product return/cancellation policies, etc.
AI-based
AI chatbots for business, on the other hand, use natural language processing (NLP) and machine learning (ML). They understand and respond to complex queries in a human-like, context-aware manner.
For example, Google Assistant is an AI-based chatbot that handles varying tasks from appointment reminders to answering general knowledge questions.
Hybrid
These chatbots combine the strengths of both rule-based and AI-based systems. They follow pre-defined rules to answer basic queries but can also use AI for more complex, contextual conversations.
Businesses can configure a hybrid chatbot across communication channels like live chat, messaging apps (WhatsApp, Messenger, etc.), and in-app messaging. Given the hybrid model, these chatbots are more versatile than the other two. Several industries can integrate them into their communication systems.
Businesses can also create AI chatbots for automation by integrating conversational AI, given it’s developed on a scalable platform.
What is conversational AI technology?
Conversational AI technology provides more advanced and dynamic solutions to customer queries.
While conventional chatbots follow scripted responses, conversational AI technology consists of NLP, ML, and automatic speech recognition (ASR) to carry out human-like conversations. Here’s their function explained:
- NLP: Helps the system understand and interpret human language and nuances such as tone, intent, and context.
- ML: Supports AI self-learning with past customer interactions and behavior.
- ASR: Converts spoken words into text so that you can integrate voice-based interactions in conversational AI systems.
Normal chatbots handle FAQs, while conversational AI adapts to user preferences and makes conversations based on their input.
For instance, Plivo-powered AI voice agents use conversational AI to provide real-time customer service. They can act as a personal shopping assistant for customers, automate routine financial transactions, break language barriers in customer support or learning, and even provide personalized health assessments to patients.
Chatbot vs. conversational AI chatbots at a glance
Here’s a table stating the differences between a chatbot and conversational AI.
Use cases for chatbot vs. conversation AI in customer service
Chatbots aren’t just a passing trend. In fact, Gartner states that chatbots will become the primary customer support channel by 2027. However, how can you include them in your existing communication systems? Let’s explore.
Use cases for chatbot
Here’s a quick rundown of the specific use cases for chatbots.
Appointment scheduling
Chatbots streamline appointment scheduling by automating the process. They let customers book, reschedule, or cancel appointments 24/7, eliminating the need for staff intervention.
They can also send reminders, reducing no-shows and ensuring a positive experience for businesses and customers.
Order status updates
Updating customers about order statutes from confirming orders to tracking shipments, can chip away a big chunk of your agents’ time. In contrast, chatbots instantly respond to customer inquiries like “Where is my order?” or “What’s the estimated delivery time for x product?”.
Customers don’t need to wait for an agent to answer these queries, which significantly improves satisfaction rates while saving time and resources.
Answer FAQs
An IBM report states that chatbots can resolve up to 80% of routine customer inquiries. Imagine the time this saves up on your resources. With chatbots, customers get the answers they need without waiting 48 hours for an email response because of a holiday or an agent's unavailability.
It can handle frequently asked questions like inquiries about product features, order-related updates, shipping updates, refund/return requests, etc.
Loan applications, payments, and billing assistance
While e-commerce and healthcare have picked up on integrating chatbots into their customer service, the banking sector is no exception.
Chatbots can help users with payment due dates, loan application updates, and billing breakdowns. Simply create a bank interactive voice response (IVR) menu, and integrate it into voice-enabled bots to make customer support easier. It’ll automate routine queries and reduce operational costs.
Customer feedback collection
Businesses go out of their way to solicit customer feedback. While you can use the rule-based bots to ask customer feedback questions, take it a notch up with conversational AI. The bot then sends a feedback form to the customer after an item is sold or a task is performed.
For example, you may use it to ask users about:
- Their experience with the product.
- The delivery process.
- If the product/item met their expectations, etc.
- How they would like the product/shopping experience to be better.
Use cases for chatbots with conversational AI
You can integrate conversational AI and build on previous use cases of basic chatbots to move beyond scripted responses. However, let’s explore specific conversational AI use cases that can benefit business operations.
Personalised product recommendations
Conversational AI platforms have access to customer data, including their browsing history, purchase behavior, etc. These bots use the data and recommendation algorithms to suggest specific or complementary products.
So a generic customer query about a mobile phone narrows down to a specific model that fits their budget.
From discovery to checkout, these bots can handle the entire funnel. Use it to help customers compare products, suggest similar products, and apply discounts or upselling based on previous purchases.
Text-based bots are sufficient for a target audience that prefers texting over speaking. However, voice-enabled bots provide more convenience and feel more personalized to customers. Plus, launching context-aware voice bots is simpler than you think.
For instance, Plivo-powered voice agents integrate easily with any speech-to-text (STT), large language model (LLM) or text-to-speech provider of your choice to get started.
You can learn all about AI voice bots and their use cases in this guide.
Intelligent IVR systems
Say a customer chats with the bot and the bot transfers the call to the right agent for a nuanced response. What happens during the time between the transfer and when the agent picks it up? Typically, customers hear music or are provided with a waiting number.
However, you can maximize IVR menu efficiency with pre-answers. Create multilevel IVR menus with audio or text for prompts. Specify exactly what happens after a call is transferred before it's picked up to add value to the customer journey.
For example, if a call is transferred to a department frequently asked the same questions, you can record answers to those common queries and play them for the caller while they wait. This reduces call drops and provides information to customers upfront.
Multilingual support
Language barriers can significantly hurt your customer support strategy. The best chatbots for customer service are designed to be customer-friendly, meaning they understand and respond in the same language as the caller.
Conversational AI bots solve this by instantly switching between languages and providing personalized, native-like assistance, removing the need for multiple agents or long wait times.
Dynamic query resolution
Complex questions often lead to frustration with standard chatbots. AI-powered bots analyze the intent behind queries, offering precise solutions instead of generic responses, saving time and reducing escalation rates.
Since the bots understand the sentiment behind the words, they offer solutions after analyzing the tone of the conversation. While text-based input works well for sentiment analysis, voice-enabled bots can achieve similar insights by transcribing voice inputs into text for deeper customer analytics.
Advanced after-sales support
After-sales support issues including troubleshooting or warranty claims often take too long. AI bots streamline this process by guiding customers step-by-step, scheduling follow-ups, or handing unresolved cases to human agents without delays.
Fraud detection and prevention
Suspicious activity can compromise customer accounts.
AI bots monitor patterns, flag risks, and trigger security protocols like verification requests or account freezes, keeping customer data and transactions safe in real time.
Along with these, winning back abandoned carts, offering discounts and offers, streamlining the help desk or ticketing system, etc. are some of the top AI sales bot use cases.
Chatbot vs. conversational AI: What the future holds
Artificial intelligence in customer service is just the beginning.
Looking ahead, conversational AI is set to expand beyond traditional customer support. It will take on roles as virtual healthcare assistants for patients, personal tutors, and investment advisors, and even offer even more personalized recommendations in entertainment and retail.
As AI continues to leverage more user data, companies will face increased pressure to ensure compliance with data privacy regulations like the General Data Protection Regulation (EU) and California Consumer Privacy Act (CCPA).
The future of conversational AI will require businesses to prioritize safeguarding user data — such as clearly disclosing when a bot is interacting rather than a human agent. To keep up with these demands, conversational AI systems will be designed with built-in compliance features to meet evolving privacy standards.
Ultimately, natural language processing in AI promises more sophisticated, smart, and intuitive communication systems.
Chatbot vs. conversational AI chatbot: Which to choose when?
The decision between a chatbot and a conversational AI chatbot hinges on your business needs and the complexity of customer interactions.
For small and medium-sized enterprises (SMEs) that want to handle routine queries efficiently, chatbots are a great choice. They excel as triage systems, routing simple inquiries to the right channels and saving valuable time for your team.
Before implementing it, ask: How will your business benefit from automating basic customer concerns?
Whether it’s saving time, freeing up agents for more complex tasks, or helping with customer acquisition, understand the potential impact on your business goals.
Conversational AI chatbots, however, are ideal for more nuanced customer service needs. They’re particularly useful when you require multilingual support, the ability to handle multiple intents in a single conversation, or context-aware solutions.
Use chatbots for automation at the top of the funnel for initial contact, lead capture, or general inquiries. As customers move down the funnel, integrate conversational AI for detailed product inquiries, after-sales support, and nurturing client relationships.
Take the first step to automation with Plivo’s Voice API
For many SMEs, diving straight into conversational AI can feel like a big leap. Hence, if you’re looking for a solution that doesn’t require hefty investments, Plivo’s Voice API is the perfect starting point.
Plivo's Voice API easily integrates with your choice of speech-to-text, text-to-speech, and language model providers to build smart IVR systems. With smart IVR capabilities, you can automate routine tasks, direct calls to the right agents, and save valuable time for you and your customers.
In addition, Plivo’s Voice API supports straightforward integration with leading AI providers, including OpenAI’s speech models. Using our simple integration endpoints, you can test the language model’s ability to transcribe voice to text before deploying your IVR system. The setup is easy, requires minimal technical expertise, and adapts effortlessly to your evolving needs.
For example, you can build an IVR to automate common queries like checking account balances or updating personal information, allowing your agents to focus on more complex issues. Plus, its voice API supports key features like call forwarding, global conference calling, and call recording for in-depth analytics, providing high-quality services that meet customers’ needs.
Plivo’s reliable coverage in over 220 countries and territories, 99.99% uptime guarantee, and less than 2-second call rerouting in case of failovers amplify all these benefits.
Start simple, and scale smart. Contact us to build an integrated and intuitive communication system.
