Chatbot use cases and examples

A chatbot is a program or script designed to interact and respond to humans in real-time conversation. Typically, these software applications chat with users through text or voice, answer questions and help humans complete numerous tasks.

Broadly, chatbots provide pre-written responses and information to handle basic requests or to get enough information from customers to connect them to a live agent for better and more specific service. More advanced chatbots use machine learning, artificial intelligence (AI) and generative AI technology to generate real-time responses based on user input. Chatbots have become a sort of Swiss-Army-knife for many organizations, one tool that fulfills many business needs.

Chatbots have been around since the mid-20th century. The first program that has been defined as a chatbot was created in 1966; Joseph Weizenbaum’s ‘Eliza’. In 1988, programmer Rollo Carpenter created a “chatterbot” named Jabberwocky, among the first “conversational AI” chatbots to learn new responses instead of serving pre-written language.

Since that time, chatbots have evolved at holding a conversation and dealing with customer interactions. The primary chatbot use case for most businesses is to ease communication between the organization and the customers. Still, they’re imperfect; even the best chatbot can’t successfully imitate a human indefinitely. Despite their occasional clumsiness, many customers now expect organizations to have a basic customer support chatbot to help them self-serve or troubleshoot issues before escalating to a live agent.

In recent years, agentic AI and AI assistants have evolved to integrate with chatbot-like technologies capable of conducting more complex business: For example, helping employees fulfill multi-step HR requests or allowing travelers to book hotels and airfare through conversational interfaces.

These technologies are widely expected to become more popular across industries: According to recent research from the IBM Institute for Business Value, twice as many executives now believe that AI agents integrate more deeply into day-to-day workflows over the next two years. 

While users tend to use the terms interchangeably, chatbots, AI assistants and AI agents represent different levels of technological sophistication.

Traditional chatbots tend to perform when assigned to specific, scripted interactions. They excel at handling predefined tasks like answering FAQs or routing customer inquiries. For example, a banking chatbot might help a customer check their balance or reset a password. These systems work well within defined parameters but might struggle when conversations venture outside of a programmed scope.

But AI assistants go beyond simple scripted conversations. They’re often used to manage tasks or set reminders, and power in-home smart devices. These tools are more proactive than basic chatbots, anticipating needs based on context like location or time of day. For example, a banking assistant might answer questions about recurring expenses. 

AI agents possess more autonomy. As agents retain memory over time, they can understand context and handle more complex requests. Modern AI agents break down sophisticated problems, drawing on multiple tools, APIs and predefined data sources to generate solutions with minimal human intervention.

To extend the example from the personal finance sector, a banking AI agent might not only answer questions about an account but analyze spending patterns and proactively suggest financial advice. Given the correct tools, it might also execute transactions on a customer’s behalf. 

There are two types of chatbots, rule-based and AI-powered. Rule-based chatbots use predefined rules and scripts to respond to specific keywords or phrases. They offer limited flexibility but are quick and efficient tools for simple tasks. AI chatbot examples use machine learning and NLP (natural language processing) to understand prompts and context. They can learn from past interactions and improve over time.

Most chatbots, even sophisticated ones like conversational AI chatbots, rely on a combination of critical elements. It starts with user input, where a user asks questions or prompts the chatbot through SMS, voice or some other interface. The chatbot then instantly analyzes the request by applying NLP techniques like tokenization, stemming and lemmatization to understand the meaning behind the request.

Based on the NLP analysis, the bot identifies the goal or purpose of the query. Is the user just chatting, do they want to book a flight or do they want to track an order? The bot uses intent recognition to decide and its internal logic and algorithms determine the appropriate response based on the recognized intent. Sometimes this process involves accessing a knowledge base, searching for relevant information or generating a creative response.

The most advanced versions of chatbots, such as those systems built on large language models (LLMs), engage in human-like dialog across a wide range of topics.

By and large, chatbots have become the go-to method for organizations to provide around-the-clock, accessible customer service—particularly as customers expect 24x7 service and rapid issue resolution. Increasingly, advanced technologies like AI assistants and agents have been deployed to offer more versatility to users and assist with complex tasks. These technologies give businesses an always-on channel to render service to customers and increasingly provide consumer-grade experiences to internal employees.

However, there are a few drawbacks. When an organization decides to employ a chatbot, the onus is on them to ensure that it provides valuable customer service and is customized for that purpose. A confusing or unhelpful chatbot can drive a customer away and damage an organization’s brand reputation.

In addition, organizations that don’t provide a connection to a live agent and rely solely on conversational chatbot technology might face adversity. Customers often become frustrated with their inability to speak with a person.

The wide array of chatbots and AI-enabled platforms in the market today showcases the versatility that these tools bring to different industries. Chat GPT’s release, in particular, helped mainstream public interaction with the technology.

Lyro, for instance, is revolutionizing customer service with its deep learning capabilities, handling up to 90% of common inquiries and improving response times significantly. Kuki, available on Facebook Messenger and Google’s Meena, set new standards in daily interaction and open-domain conversation with advanced language processing and context understanding.

Facebook’s Ada, developed by ServiceNow offers a friendly and personal approach, often relying on emojis and GIFs to express empathy and humor. Agentic AI APIs for messaging apps like WhatsApp act as personal shopping assistants and proactively send users reminders.

The variety of use extends to specialized bots like Domino’s Messenger bot, which simplifies food orders to Insomnobot 3000, which provides quirky nighttime companionship. These use cases illustrate chatbots’ growing role in improving user experience, streamlining operations and providing support across sectors.

But the benefits extend far beyond automation. Chatbots and virtual assistants, sometimes integrated with agentic AI, are quickly becoming co-workers for human agents highlighting urgent issues and completing routine workflows so agents can focus on solving complex problems. At IBM, for instance, an internal HR tool handled more than 11.5 million interactions in a single year.

With global reach and 24x7 availability, these technologies can break down language barriers and provide instant support, building trust and satisfaction at all levels. By combining AI insights with sentiment analysis, companies more quickly understand the customer journey, identify customer (or employee) pain points, improve offerings and predict needs before they arise.

Customer service chatbots provide a seamless experience and enhance customer and employee satisfaction. These AI-driven assistants offer self-service support across web and mobile platforms, making them readily accessible to most people.

Chatbots and associated technologies also play a major role in gathering customer feedback and intent signals automatically, a vital aspect of service improvement. Often, chatbots and agentic AI systems integrate seamlessly with customer relationship management (CRM) systems and other databases.

These tools track orders, answer customer queries and check on shipped items, adding transparency and trust to the buying process. Online sellers like Amazon have extended chatbot automation to refund and exchange processes, streamlining operations and reducing manual intervention throughout the sales journey.

Standard chatbots, AI-powered chatbots and virtual assistants are becoming increasingly crucial for enterprises in enhancing customer service and business operations. Their popularity stems from the ability to engage customers quickly with website content and self-service support options, reducing the need for face-to-face interaction with service representatives. This capability allows customers to solve problems on demand and reduces the workload on service teams, enabling companies to expand their customer support team’s bandwidth.

Some organizations use these tools to proactively engage customers and inform them about available help. They offer immediate support upon a user’s site visit and guide users through the site, tailoring the customer experience to their needs. Some businesses integrate their chatbots with a knowledge base for efficient support query handling. Others use chatbots to determine customer needs before routing complex issues to their support team and human agents.

Internally chatbots, virtual assistants and AI tools assist with employee support, answering queries and providing timely information. Chatbots liberate customer service reps from the time-consuming task of answering basic questions. By automating these tasks, chatbots enable faster customer responses and free up reps for more creative, intuitive support roles. This efficiency improves customer satisfaction and presents a cost-effective pricing solution for understaffed service teams, as chatbots do not require salaries like real-life human agents.

Integrated with agentic AI, AI tools also facilitate multi-step processes like filing necessary compliance paperwork or generating personalized employee training tutorials. With human-agent collaboration, HR departments significantly limit the hassle of routine tasks and allow talent managers to focus on creative endeavors.

Chatbots and AI-enabled assistants are helpful in automating lead generation in sales, enabling robust marketing campaigns and ensuring a consistent flow of potential customers. They collect information from website visitors or mobile app users upfront and convert anonymous site visitors into leads. They nurture these leads effectively, providing timely and relevant information to guide them through the sales funnel.

These tools facilitate easy booking and scheduling appointments, simplifying the interaction between the business and the customer. Moreover, they offer personalized recommendations that enable customers to purchase online at their convenience, increasing conversion rates and aligning with the growing e-commerce trend. AI-enabled messaging programs, enabled with agentic AI, also proactively offer suggestions based on user history and help consumers search for products through natural language.

These technologies offer enhanced customer engagement for marketing efforts, creating interactive and personalized experiences. They are increasingly used for automated news aggregation, helping businesses stay ahead of global news and trends, which is crucial for timely and relevant marketing strategies. Healthcare institutions are also beginning to use these technologies to streamline administrative tasks. In addition, e-commerce chatbots provide automated product recommendations based on users’ interests, improving the overall shopping experience and boosting sales.

Chatbots have become central in the domain of social media engagement and messaging platforms. They form the backbone of business messaging, facilitating efficient and effective communication both internally within organizations and externally with customers.

In human resources, chatbots and AI-enabled tools streamline recruitment through automated pre-screening, efficiently filtering candidates and saving time. They offer conversational onboarding and interactive FAQs, providing instant answers to common questions and queries, which enhances the candidate and employee experience. Moreover, these technologies provide automated progress updates, keeping candidates informed and engaged throughout the recruitment process.

Overall, chatbots are transforming various aspects of business operations, increasing productivity, enhancing customer and employee experiences and contributing significantly to the digital transformation of enterprises.

Implementing a chatbot within an organization requires careful planning and consideration. What follows are the best practices an organization should consider when implementing a chatbot successfully.

1. Define your goals and objectives: Be clear about what your organization wants the chatbot or agent to achieve. Is it for customer service, employee assistance, internal information retrieval or something else? Having well-defined goals can guide your development and implementation process.

2. Identify your target audience: Understand who is likely to interact with the technology. What are their needs, expectations and preferred communication styles? Tailoring a chatbot or AI-enabled assistant’s tone and capabilities to your audience is crucial for user adoption and satisfaction.

3. Choose the right platform and acquire the right data: Research different platforms and tools based on the organization’s needs, budget and technical expertise. Consider factors like scalability, security, integrations and ease of use. Decide what data is to be integrated into the chatbot or agent and ensure it’s consistent as well as high quality.

4. Design the experience: Plan the flow of your chatbot’s conversation. Create user stories and map out potential scenarios to ensure intuitive interaction and efficient resolution of user queries.

5. Develop the chatbot: This phase can involve building a knowledge base, scripting dialogs and training the AI model (if using a machine learning chatbot). Make sure the tool’s language is clear, concise and error-free.

6. Testing and refinement: Rigorously test the chatbot or AI tool before deployment. Identify and address any bugs or inconsistencies. Gather user feedback and refine the chatbot based on their experience.

7. Launch and promotion: Develop a communication plan to introduce the new technology to your target audience. Provide users with clear instructions on how to access and interact with it.

8. Monitor and maintain: Monitor the chatbot or AI-enabled messaging tool’s performance, gathering user feedback. Analyze data to identify areas for improvement and update data, APIs or knowledge base continuously. 

• Data privacy, security and governance: Ensure your chatbot or adheres to privacy regulations and securely protects user information and customer data.

• Accessibility: Make your chatbot accessible to users with disabilities.

• Human oversight: Remember, chatbots and AI are not a replacement for human interaction. Ensure that you have human support agents who can handle complex or sensitive issues that require a human touch.

By using these resources and frameworks, organizations can confidently integrate chatbots or AI agents into their operations and unlock tangible improvements in communication channels between customers and businesses. Allowing an organization to answer questions and schedule appointments more easily, as well as offer more personalized recommendations to boost user satisfaction and sales.

These innovations are just a glimpse of the possibilities. Increasingly, organizations offload entire workflows to AI tools or create new ways for customers to discover products through a combination of associated technologies, all seamlessly. With careful planning and implementation, along with a strong North Star vision, organizations stand to transform how business is done.