What is an AI Chatbot?: How They Work, Benefits & Use Cases

In a world that is constantly buzzing, how do businesses provide consistent service around the clock?

Customers often need help outside of regular business hours, browsing products, and seeking support at all times. AI chatbots can assist with this, not just on websites, but in many places customers interact with you, like messaging apps and social media. These artificial intelligence (AI) helpers can answer questions and guide users 24/7. While they handle common requests, more complicated issues can still be passed on to your employees for personalised attention, ensuring customers always receive the support they need.

With 91% of small and medium businesses declaring that artificial intelligence boosts revenue, it's clear that AI chatbots are here to stay. But if you're new to AI, you may be wondering, "What is an AI chatbot — and what are its benefits?"

Let’s take a look.

• What is an AI chatbot?
• The difference between chatbots, AI chatbots, and AI agents
• How do AI chatbots work?
• Types of AI chatbots
• Benefits of AI chatbots
• How AI chatbots are used across businesses
• Selecting the Best AI Chatbot for Your Needs
• Unlock the power of AI for your business

An AI chatbot is an AI digital assistant that uses AI to understand and respond to inquiries in real time, simulating human conversation with an end user. Unlike traditional chatbots, which rely on preprogrammed rules and responses, AI chatbots use natural language processing (NLP) and machine learning (ML) to understand and respond to user questions on demand.

These capabilities let the bot engage in more natural and relevant conversations, learn from user input over time, and develop ways of handling issues more accurately and efficiently. They can answer questions, provide product recommendations, and facilitate transactions. AI chatbots can also collect information and quickly suggest solutions. They are even designed to react much like a human would. For instance, in service, AI chatbots can help streamline processes by offering responses 24/7. This reduces wait times, boosts overall efficiency, and improves the customer experience.

But these AI chatbots aren’t only about serving customers. They can support your internal team, too. One way a sales AI chatbot can do this is by acting as a coach, allowing your reps to practice pitching, negotiating, and handling objections based on specific information about the rep, the potential customer, and the company’s selling goals.

So many kinds of bots have popped up in recent years that it can be difficult to distinguish between AI agents and chatbots. The most basic bots are rules-based chatbots. They follow a predefined set of responses to common inputs. Contextual AI chatbots, which are a type of AI agent, are more advanced; they use machine learning and NLP to decipher the context of a conversation. AI agents encompass a larger group of autonomous agents that use agentic AI to take action on their own as they continuously learn, adapt, and collaborate with humans.

Here’s a more detailed breakdown of the differences:

A traditional chatbot uses a set of predefined rules and scripted responses to interact with users. They’re limited to basic interactions, like responding to common customer support questions. Although chatbots have conversational interfaces similar to AI chatbots and AI agents, they don’t understand language in the same way as AI that uses large language models (LLMs).

Chatbots can provide quick, consistent responses to common customer questions. This makes them a reliable, cost-effective solution for handling routine customer service inquiries, collecting basic information, and suggesting relevant resources. So while these bots are effective for straightforward, repetitive tasks, they’re not made for open-ended conversations.

As we’ve been discussing here, AI chatbots are built to interact directly with humans. With the help of machine learning and NLP, they can understand regular conversations and craft human-like responses using generative AI. Because these systems are typically built on LLMs trained with vast amounts of data, they can engage in more nuanced and context-aware interactions.

AI chatbots are widely used in various digital spaces requiring intelligent conversation. They enhance customer service through the web and messaging apps with personalised and complex support. They also power virtual assistants on devices and are being adopted in healthcare, education, and business for smarter communication and automation. Their ability to understand and learn makes them versatile for many interactive applications.

However, AI chatbots are limited compared to AI agents that make decisions and use tools like RAG (retrieval-augmented generation) because chatbots primarily focus on conversation and lack agents' autonomous decision-making and action-taking abilities. Furthermore, while chatbots can process information, they typically don't proactively retrieve and integrate real-time external knowledge like RAG-based agents, which allows those agents to handle tasks requiring up-to-date information beyond their training data and mitigates issues by providing guardrails and citations that minimise hallucinations and maximise accuracy.

AI chatbots are an example of AI agents that have been built with the purpose of interacting with humans, often in a customer service-type role. But AI agents can do a lot more than that. They can act as AI assistants, with the ability to augment human capabilities across a wide range of tasks. These virtual agents can understand and generate natural language, and process and analyse large amounts of information. They can use this to perform activities that take place behind the scenes of a business, like coding, analysing large amounts of data, and performing quality control tests.

Here’s a comparison of different chatbots at a glance:

AI chatbots use a combination of advanced machine learning and deep learning techniques to generate responses that seem almost human.

These are the key features that make chatbots work:

With machine learning, algorithms learn from data to make predictions or decisions. AI chatbots analyse large datasets of human conversations, quickly learning patterns. Deep learning is a more complex form of machine learning. It uses neural networks with many layers — or "deep" networks — to understand how humans ask and answer questions, ultimately generating more natural and coherent responses.

Taking cues from how the human brain works, AI chatbots can generate contextually appropriate responses by paying attention to which parts of the previous conversation are important. It does this by using neural networks, which are computational models that consist of layers of nodes (or neurons) that process and transform information.

AI chatbots also use transformer models, which are a specific type of neural network designed to handle sequences of text more effectively. These transformers use a mechanism called "attention" to weigh the importance of each word in a sentence. This kind of NLP helps AI chatbots understand the context and relationship between words.

Zero-shot learning happens when an AI model predicts something that it has never encountered before by generalising related knowledge. For example, an AI model might be able to respond to customer inquiries about new services without needing explicit training on those specific topics.

Few-shot learning works differently. The AI model learns to make accurate predictions by training on a small number of examples. For instance, a customer service chatbot for an online shoe store might learn how to handle common issues, like when a customer receives incorrectly sized boots. But even if it hasn't encountered that exact case before, it can apply what it learned from similar situations, like helping a customer who received damaged boots. Based on that experience, it knows how to respond effectively, offering solutions such as an exchange or refund.

Fine-tuning involves taking a pre-trained AI model and training it on a smaller, task-specific dataset. This allows the model to improve performance in a specialised domain while retaining general knowledge from its original training.

Domain-specific models are either pre-trained on a particular industry’s data or extensively fine-tuned to perform well in a specific field.

Not every AI chatbot is created equal. Some can handle only simple requests, with a decision tree guiding its actions. Others can manage multiple tasks and improve over time.

Some common types of AI chatbots include:

• Transactional AI chatbots: These task-specific bots help with straightforward tasks like checking order status and updating shipping information. They streamline routine transactions, freeing up human service representatives for more complex cases.
• Conversational AI chatbots: These bots use conversational AI to engage with people in a natural, human-like way, which is why you’ll commonly find them in customer service.
• Decision support AI chatbots: These virtual advisors help users make informed decisions by presenting relevant data and possible outcomes, especially in complex situations.
• Workflow automation AI chatbots: These bots automatically handle repetitive tasks within a business workflow. It interacts with users through conversation to gather information, make decisions, and trigger actions within other systems.
• Informational AI chatbots: As the name suggests, informational bots provide knowledge-based support, answering FAQs and offering relevant resources to help users find answers quickly. They also assist teams in ramping up self-service support.
• Problem-solving AI chatbots: These bots are used for diagnosing and resolving customer problems. They can guide users through troubleshooting steps and escalate issues to human reps if necessary. By addressing urgent concerns, they minimise resolution times and contribute to higher customer satisfaction scores (CSAT).
• Feedback and survey AI chatbots: Used to send customer satisfaction surveys and collect feedback, these bots gather valuable insights to improve products, services, and customer experiences. This data helps teams analyse and understand customer feedback, crucial for ongoing strategy and communication refinement.
• Adaptive learning AI chatbots: These bots continuously improve, adjusting responses and strategies based on the feedback they receive, enhancing their performance over time.
• Hybrid AI chatbots: Combining features from various chatbot types, hybrid bots are highly versatile — but also costly to develop. They can switch between transactional, informational, or problem-solving modes based on user needs, handling a broader range of issues with higher volume.

There are advantages and disadvantages of AI, but here are some examples of clear benefits of using AI chatbots:

AI chatbots help save your customers and agents time. They can quickly assist with basic customer inquiries, solve straightforward issues, and collect and share information. If an issue escalates beyond their capacity, they can facilitate a resolution by connecting the customer with a live agent — and getting them up to speed before the interaction starts.

Chatbots can help with simple issues 24 hours a day. During busier times when live representatives may be tied up, chatbots can support by handling routine requests. They can reply to customers during off-hours and collect information to be handed off to human reps, ensuring that inquiries don't go unanswered and are addressed promptly when agents return. This helps your team deliver consistent support and can improve customer satisfaction.

AI chatbots can anticipate customer needs, deliver useful messages, and suggest next steps, helping to enhance the customer experience. Their ability to provide personalised recommendations, such as new products or services, can increase your value to customers and keep them engaged with your company.

Our research found that 61% of customers would rather use self-service options for simple issues. AI chatbots can point them toward resources such as FAQs, knowledge base articles, and help centres. This is a win-win for organisations; they can cater to customer preferences while saving resources.

AI chatbots can provide a much-needed boost to all your teams, allowing them to provide better customer care and work on higher-priority tasks. Here’s a look at how different groups within your company could benefit from AI chatbots:

• Sales: An AI chatbot can help your sales team generate a greater return on investment (ROI) through autonomously answering product questions from customers. This way, your reps can provide more attention to more complex cases.
• Customer service: AI chatbots can provide personalised 24/7 support, gather and analyse customer feedback, and offer self-service options. They can also automate repetitive tasks, allowing reps to focus on more challenging issues.
• Marketing: With AI chatbots, your marketing team can get more done. AI chatbots can quickly and efficiently collect survey information, reduce friction in the customer journey, and engage potential customers with targeted ads. They can also manage campaigns and generate promotional content that captures your brand voice while remaining relevant to each specific channel.
• Commerce: Your AI chatbots can offer personalised product recommendations based on previous purchases, track orders, and provide reminders about abandoned carts. They can also assist with fraud detection and inventory management.
• Healthcare: AI chatbots can answer patient questions, provide appointment scheduling, and offer 24/7 assistance within compliance guidelines. More advanced AI agents can help with medical diagnostics and imaging — and even offer personalised treatment plans.
• Automotive: This technology can assist customers with product inquiries, vehicle troubleshooting, and service bookings.
• Financial services: AI chatbots can provide instant customer service, help with account management, and deliver personalised financial advice. They can also assist with fraud detection, risk management, and identity verification.
• Transportation: Your team can use AI chatbots to manage bookings, provide real-time updates on routes and schedules, and help with customer inquiries. For municipalities, AI agents can analyse real-time traffic data to optimise turn signals and reduce congestion.
• Hospitality: AI chatbots offer a seamless booking experience, comprehensive guest support, and personalised recommendations. They can also help with contactless check-in, predict when rooms need cleaning, and suggest menu items based on guest preferences.

Here are some important features to consider when choosing an AI chatbot:

• CRM integration: Look for a chatbot that can integrate seamlessly with your customer relationship management (CRM) software. This will allow you to speed up issue resolution, automate routine tasks, and provide personalised, intelligent, and multilingual service based on your stored customer data. CRM integration also makes deploying these bots more efficient because relevant data is already present.
• Omnichannel ability: The ability to meet your customers where they are is key in customer service. An omnichannel contact centre chatbot can support your service efforts by being available for live chat on websites, mobile apps, SMS messaging, and more.
• Analytics: Good AI chatbot software will give you access to reporting, KPIs, and analytics so you can measure success. Using bot performance metrics, chat data, and cost information, you can track and optimise your bots' value against your customer metrics goals. You can also use data to improve your strategy and your bots' understanding of customers.
• Templates and customisation: A chatbot solution that offers templates can help you launch your bots quicker. Templates give you tailored solutions to choose from (suited to common and more specific situations) that can be built in seconds.

AI chatbots have evolved from simple beginnings to a force for business. As this technology continues to grow and get more complex, you can do more with your customer data. Your teams can focus on higher-level work, as AI can handle the menial, time-consuming tasks.

Companies across all sectors are recognising the substantial advantages of integrating AI, including sophisticated chatbots, into their workflows. To explore how your organisation can move beyond basic chatbots and harness the full power of intelligent automation and agentic AI, take a closer look at Agentforce and unlock a new level of productivity and performance.