AI Chatbots Explained: What They Are and How to Use Them

AI chatbots are software applications that simulate human conversation. They use artificial intelligence to understand and process natural language, enabling them to interact with users in a conversational manner.

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 73% of service teams already using AI chatbots, it's clear they are here to stay. But if you're new to AI, you may be wondering what an AI chatbot is, and what benefits they offer. Let’s take a look.

• AI chatbots defined
• The difference between chatbots, AI chatbots and AI agents
• Best AI chatbots of 2025
• How do AI chatbots work?
• Types of AI chatbots
• Benefits of AI chatbots
• How AI chatbots are used across businesses
• How F1 fast-tracked fan support with Agentforce
• 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 enquiries 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.

However, as AI becomes more visible in customer interactions, trust is critical. We found that 61% of customers say it’s more important than ever for companies to be trustworthy as they adopt AI. This means businesses need to be clear when customers are speaking to a bot, ensure AI responses are accurate and helpful, and avoid using automation in ways that feel misleading.

On top of providing customer support, AI chatbots can also support your internal team. One way a sales AI chatbot can do this is by acting as a coach, allowing your reps to practise 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.

Traditional chatbots follow a set of rules to give scripted responses. They’re great for handling simple, repetitive tasks like answering FAQs or collecting customer details.

However, they can’t understand context, answer questions outside of their limited scope or hold natural conversations like AI-powered tools can. They were traditionally faster to set up and more cost-effective, but now with tools like Agentforce, teams can get more advanced AI support just as fast and well-priced.

AI chatbots are built to have conversations that feel real and helpful. They use machine learning and natural language processing to understand questions, respond in a human-like way, and improve over time.

They’re now widely used across websites, apps, and messaging platforms to deliver more personalised, helpful support. However, while they’re great at chatting, they aren’t able to make decisions or take action like AI agents can.

AI agents go beyond conversation. They’re built to understand what needs to be done and then follow through, often without any human input. For example, a customer service AI agent could pull data from different systems, draft a personalised service summary, and send it directly to a customer.

Since they operate with more autonomy, transparency is incredibly important. In fact, we found that 72% of customers want to know when they’re interacting with an AI agent. Letting people know they’re speaking to AI sets up the transparency that builds trust.

AI chatbots are now part of day-to-day operations for many teams. They help answer customer questions, reduce manual work, and give sales and marketing teams more time to focus on high-value tasks. Here’s a look at some of the most widely used chatbots in 2025 and what they’re being used for.

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 makes a prediction about something that it has never encountered before by generalising related knowledge. For example, an AI model might be able to respond to customer enquiries 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 learnt 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 delivery 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.

AI isn’t perfect, but many customers are seeing the upside. In fact, half of the customers we surveyed said they’re more optimistic about AI’s ability to improve product quality and deliver faster, more efficient service. 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 enquiries, 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 enquiries 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 baskets. 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 enquiries, 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 enquiries. 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.

F1 has one of the fastest-growing fanbases in the world, with more than 750 million fans and more than 1.5 billion TV viewers in 2023. Most of these fans never attend a race in person. Instead, they engage online through platforms like F1 TV, their app, video games, and the online store.

The challenge F1 has was delivering fast, personalised support as their fan base boomed. Previously, F1’s support team was dealing with long queues, scattered data, and no easy way to track past interactions. The delays this was causing in resolving simple issues like login errors or streaming problems were starting to affect satisfaction.

That’s when F1 turned to Agentforce to upgrade its service. F1 now uses AI to handle routine enquiries in real time. Fans can get instant help through the self-service portal, and if live support is needed, support reps get a full fan profile and an AI-generated reply ready to go.

The result is faster, more personalised service with much less effort. Since implementing Agentforce, F1 has reduced response times by 80%, increased first-call resolution to 95%, and seen an 8% year-on-year lift in fan satisfaction.

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 quickly. 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’ll be able to do more with your customer data. Your teams can focus on higher-level work, as AI can handle the menial, time-consuming tasks.

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.

To see first-hand how an AI agent could help your business, try the ‘Quick Start: Build a Service Agent with Agentforce’ module on Trailhead. In the module, you’ll learn how to create a service agent that can assist with recommendations and bookings in only a few steps.