What is an AI Chatbot?

Artificial Intelligence 

Artificial intelligence is a branch of computer science focused on creating machines with human-level intelligence. The types of tasks they set out to accomplish are typically related to learning, reasoning, problem-solving, and understanding natural language.

Natural Language Processing (NLP)

Natural language processing (NLP) is a branch of natural language understanding (NLU), which is a branch of AI.

NLP enables machines to understand, interpret, and meaningfully respond to human language. It’s used not only in AI chatbots, but in language translation and speech recognition technology.

Generative AI

Generative AI refers to AI systems that can generate text, images, video, or other output.

Conversational AI

Conversational AI is a branch of AI that focuses on allowing computers to engage in human-like conversations. Through a combination of machine learning and NLP, conversational AI is designed to engage in dialogue with humans, either through text or voice.

AI Agent

AI chatbots overlap with the concept of AI agents. An AI agent is a software that performs tasks on behalf of a user. They can automate processes, make decisions, and intelligently interact with their environment.

Both AI chatbots and AI agents use NLP, LLMs, and vector databases. But they differ in their purpose and capability. Chatbots are designed to interact with humans, while agents are designed to complete autonomous tasks.

Many AI chatbots are unable to take autonomous actions, and some AI agents don’t exist in text-based, user-facing forms.

1. User input

Often, a user will prompt an AI chatbot by asking a question via text or voice. 

However, AI chatbots can be designed to be triggered by other events, like receiving an email from a certain sender, or observing a KPI hit a target number.

2. Natural language processing 

After receiving a trigger, the chatbot will use NLP to break down the user’s message into components, identify the user’s intent, and extract important information from the request.

3. Intent recognition

After processing the input, the chatbot will determine what the user wants – a product recommendation, resetting their password, or advice on crafting a resume.

4. Response generation

An AI chatbot will then generate a response using machine learning models. 

5. Context management

As the chatbot interacts with the user, it will keep track of the conversation to maintain context – this ensures its responses remain relevant.

6. Fetch data

If a user is asking for specific information – like how much a product costs, reviews from other users, or what a company’s HR policy says about vacation days – the AI chatbot will fetch the relevant data. 

It might do this by connecting to a database, retrieving information from a Knowledge Base, or making an API call.

7. Send response

After identifying the most appropriate, helpful, and relevant response, the AI chatbot will send its generated response to the user. This process repeats until the conversation is resolved.

Integration Capabilities

If chatbots exist in a vacuum, their purpose is drastically limited. A key purpose of chatbots is their ability to take action into existing systems.

For example, a lead generation chatbot needs to be integrated with a company’s customer relationship management (CRM) system, so that they can update records as they find and qualify new leads.

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Or take an e-commerce chatbot: it needs to be integrated with Knowledge Bases that contain information about current stock, return policies, and individual items or models.

Most chatbot platforms will have pre-installed integrations. And flexible platforms will allow chatbot builders to build integrations to any system or platform. 

LLM Routing

The best chatbots aren't clunky rule-based chatbots – they're LLM agents, a bot powered by an LLM.

An LLM 'brain' ensures your chatbot starts out with a host of advanced features. All LLM-powered bots are naturally NLP chatbots that understand what a human is trying to say (even with typos).

Omnichannel Deployment

The most useful AI chatbots are available to use across channels. An organization can deploy their chatbot to their website, but also to their WhatsApp or Facebook Messenger. 

It’s always best to meet your users where they are, so AI chatbots are particularly useful when they’re able to send emails or SMS messages. 

Advanced Analytics

If a chatbot is deployed in order to help achieve a goal – like increase lead generation, make more sales, or handle customer support calls – it needs success metrics.

Whether an organization builds their own chatbot from scratch or uses a platform, they’ll need to set up analytics to measure the outcomes of their chatbot. 

Typical analytics will include number and length of interactions, while advanced analytics can measure any facet of a chatbot’s workflow.

Data Security

Like any software project, an AI chatbot needs the proper security and privacy guardrails before being deployed to the public. 

Data security is particularly important for chatbots that handle any personal data – like names, phone numbers, or addresses. If your chatbot handles data from individuals in the EU, it will need to be GDPR compliant.

Multimodality

Chatbots used to be text only. But we no longer live in a text-only world.

Your chatbot software should often AI voice assistants and rich communication services (RCS). It should offer image-based search – like when a customer uploads a photo of a jacket they like and asks for similar products.

Some chatbots will even generate images or videos to better communicate, like a sales bot at a dealership that changes the color of the car the user wants.

Customer Support Chatbots

Everyone has a customer service chatbot these days. And for good reason – AI chatbots are perfectly suited for customer support use cases. 

A customer service chatbot can answer customer questions, dispense information, or share videos about products. They can use AI ticketing to prioritize and route support tickets.

AI chatbots are able to divert significant percentages of calls away from call centers, making them one of the most common use cases. 

Internal Employee-Facing Chatbots

While most AI chatbots are external-facing, there’s a growing adoption of internal chatbots within enterprises. More and more often, we're seeing HR chatbots taking vacation requests and IT chatbots troubleshooting an employee's tech issue.

Internal chatbots often act as the first stage of assistance for employees seeking information about internal procedures. They may interact with a chatbot to schedule days off, call in sick, learn more about their benefits, or get support with a procedural task.

Since internal processes like HR requests take up the time of two employees – the employee and the HR representative – an AI chatbot greatly reduces the cost of internal operations.

Sales Chatbots

The majority of chatbots deployed on our platform are part of an organization’s sales process.

A sales chatbot can answer questions, compare models, and give out pricing information. These chatbots are typically part of an AI-enhanced sales funnel, all the way from lead generation to post-purchase follow-ups.

A sales bot might look like a retail chatbot that recommends products based on a customer's purchase history, or a website widget that facilitates a customer's payment transaction.

Lead Generation Chatbots

A lead generation chatbot is one of the most common use cases for AI chatbots. They often send emails or messages on WhatsApp or Facebook Messenger, as well as sync up information with a CRM (customer relationship management) system.

A lead gen chatbot might offer advice or information to users – like explaining which laws are relevant in a legal dispute, or suggesting the best country to travel to based on their interests – and then offer further services based on a user's response.

They might also chat back and forth with a website visitor to qualify a lead before booking a sales meeting.

Booking Chatbots

Bookings are typically a straightforward process. A booking chatbot can handle an entire booking flow from start to end – no employees needed.

For a restaurant chatbot, a customer just needs to enter their name, contact info, and select a time and day. They can even be built as simple FAQ bots.

But some bookings are more complex. Take a hotel reservation: Guests can investigate the available room options, look at amenities and services, and book their room. It’s a task that’s easily offloaded to an AI chatbot.

That’s why chatbots for hotels are skyrocketing in popularity – they can handle bookings, streamline housekeeping requests, and sell extra services. Our partner organization has used AI chatbots to solve 75% of guest requests without human involvement and sell extra services to 20% of guests before they arrive at the hotel.

Government Chatbots

Government services have traditionally lagged behind the speed and quality of private services – and some are looking to improve the gap with AI chatbots.

Government chatbots have been deployed to help guide citizens through forms and applications, provide status updates, provide voter registration and election information, and provide information about public health or public transport.

Chatbots for Healthcare

Healthcare is another industry that is rapidly adopting chatbots. Medical chatbots often assist patients by answering basic medical questions, scheduling appointments, and providing information on symptoms and treatments.

By handling routine inquiries, the best healthcare chatbots are able to reduce the burden on medical staff, while simultaneously improving access to information for patients.

Finance Chatbots

Banking chatbots are nothing new: Bank of America's Erica has been around since 2018, monitoring subscriptions, guiding spending behavior, and assisting with account and transfer information.

But not all finance chatbots are bound to banks. Some finance chatbots assist in compliance monitoring for financial professionals, track expenses for businesses, or onboard customers to a fintech app. Chatbots for insurance might help with claims processing, guidance on policies, or fraud detection.

Real Estate Chatbots

Real estate has one of the most widespread adoption rates across industries, due to its high volume of conversational interactions and the need for constant, up-to-date information. 

Real estate chatbots can suggest properties, keep track of paperwork, and manage client relationships. They can also coach real estate agents on pitching individual properties or neighborhoods, and qualify leads before setting up a meeting with a realtor.

24/7 Availability

One of the standout features of AI chatbots is their ability to operate around the clock. Unlike human employees, chatbots don’t need breaks, sleep, or vacations. They’re always on, ready to assist customers at any time of day or night. 

Their 24/7 availability ensures that businesses can provide support whenever it’s needed, enhancing customer satisfaction and ensuring that no query goes unanswered.

Cost-Effective

By automating routine tasks AI chatbots can reduce costs of running or scaling an organization. 

the need for large customer service teams. This means companies can cut down on labor costs while still delivering high-quality service.

AI chatbots can efficiently handle a wide range of tasks, freeing up human employees to focus on more complex issues. The result? Lower operational costs and a more streamlined workflow, making AI chatbots a smart investment for businesses of all sizes.

Scalability

As businesses grow, so do their customer service demands. AI chatbots are highly scalable, meaning they can handle an increasing number of customer interactions without a drop in performance. 

Whether a company is dealing with a few dozen customers or thousands, chatbots can manage the load efficiently, ensuring that every customer receives timely support.

Consistency

Unlike humans, chatbots never have a bad day. They’re able to deliver a high-quality standard of service every time, never varying on tone or accuracy. 

Consistent service ensures that users can receive reliable support, no matter when they need it (or how many of them need it at the same time). It builds a strong brand image and higher trust with users, increasing satisfaction across the board.

Data Collection and Analytics

AI chatbots don’t just interact with customers; they also collect valuable data from every interaction. This data can be analyzed to gain insights into customer behavior, preferences, and pain points. 

Businesses can then use these insights to refine their strategies, improve products, and personalize customer experiences.

1. Define your scope

The first step to create an AI chatbot is simple – scoping. What’s your chatbot going to accomplish? Before building, your team needs to develop a chatbot strategy that includes the predicted ROI of your chatbot.

The purpose of your AI chatbot will determine what capabilities it will need, which will determine the platform you use.

If you use an extensible platform, the world is your oyster. A well-designed AI chatbot can take on any conversational AI task you can dream of. 

Once you have your scope down pat, it’s time to pick a platform.

2. Pick a platform

There are plenty of free AI chatbot platforms, for any need or use case. You can check out our list of the 9 best AI chatbot platforms for a rundown.

There are a few considerations to keep in mind when selecting a platform for your project. Make sure you pick a platform that:

• Has a broad swath of educational resources. There’s always going to be a learning curve, so ensure you’re well-equipped for it.
• Matches your intent. Don’t pick a conversational AI platform that specializes in customer service if you want a sales bot.
• Includes a free tier, so you can test it out before (or without) making a financial commitment.

3. Build your AI chatbot

You’ve made it: you settled on an idea for a chatbot, you found a platform, and you’re ready to build your own AI chatbot. 

The AI chatbot that you build is going to be entirely unique – you have your own vision and your own needs. Part of the process will involve familiarizing yourself with your platform and applying your understanding to your unique roadmap. 

4. Integrate

If you’re looking to connect your AI chatbot to another system or platform – like Hubspot, WhatsApp, or your website – then part of your building process will include integrating your bot to the necessary systems.

If your AI chatbot is going to give out information about which products your company has in stock, you’ll want to connect it to your internal source of truth, typically known as a Knowledge Base. 

A Knowledge Base can be a table, document, or website that includes the information your AI chatbot will draw from. 

For example, an HR chatbot will use a company’s key policy documents as its Knowledge Base. When an employee asks how to handle a specific situation, the chatbot can use the policy documents to inform its answer.  

5. Testing and iteration

Once you’re done building your AI chatbot, it’s time to make it better. Some builders forget to account for testing and iteration time, but this is a crucial step to deploying a successful chatbot.

Whichever AI chatbot platform you choose, it should have a simulator within the studio that allows you to practice conversations with your chatbot. This is the first step of testing that you’ll use throughout the building process.

Once your build is complete, you’ll be able to send a sample version of your AI chatbot to your friends or colleagues using a URL. You should do this to test out your bot’s functionality before officially deploying it.

6. Deploy

Once your bot is in a final form, you can release it unto the world. They are a few options for AI chatbot deployment:

• One of the most common deployments is via webchat, typically found on a company’s website
• An SMS chatbot that can send text messages
• An email chatbot that sends, receives, and summarizes messages
• A platform like Slack or Microsoft Teams
• A messaging channel like Telegram, WhatsApp, Instagram, or Facebook Messenger

7. Monitor

An AI chatbot project doesn’t end after deployment – in fact, deployment is just the beginning. Once it’s out in the world, your AI chatbot starts working for you. 

Any AI chatbot platform worth its salt will provide you with continuous chatbot analytics – when people are using it, the topics they ask about, the containment rate, and the platforms people are using to engage with it.

If you want to better understand how to manage and improve your chatbot after deployment, you can check out our free course on Managing your Chatbot.