Top 17 Conversational AI Use Cases & Benefits

Olha Kanishcheva ML/NLP Engineer, Researcher

The conversational AI market is quickly heating up: valued in 2023 at USD 10.19 billion, it’s expected to reach USD 61.69 billion in 2032. Growing nearly six times over is no joke, right? That’s why today we want to explain what’s behind such rapid growth, and it’s much more than using ChatGPT for generating emails. You have more opportunities than you might realize, and it’s our job to help you discover them!

This article will explore conversational AI use cases, their benefits, and implementation techniques – all through the lens of CHI Software’s expertise as an expert in AI chatbot development company, working with organizations to drive innovation for their teams and customers.

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Article Highlights:

• Natural language processing and machine learning are two fundamental components of conversational AI chatbots. All other chatbots are simpler to implement and won’t provide a similar level of efficiency;
• 32% of customers are ready to switch brands after only one negative experience – AI chatbots can help your business avoid falling in the discard pile;
• Sales, marketing, HR, customer support, IT & operations, and product development departments can all benefit from conversational AI adoption;
• Not all businesses need a conversational chatbot solution right away. Coming up with a clear development strategy will help you better define use cases fitting your business needs, and make technology upgrades when the time is right.

The Power of Conversational AI for Business: Key Components and Workflow

At first glance, the term “conversational AI” may seem self-explanatory. If you’re already imagining artificial intelligence tools that can participate in conversations, then you’re on the right track – but there’s more to it than that. Let’s quickly cover some theory to help the next sections make more sense.

The Building Blocks of Conversational AI

When diving into the world of conversational AI applications, it’s crucial that you understand the key components that enable these systems to work as intended. 

The main technologies that make conversational AI adoption possible

At its core, conversational AI relies on natural language processing (NLP) and machine learning (ML) algorithms to interpret and respond to human language. How do these technologies work in tandem?

• Natural language processing serves as the backbone of conversational AI, enabling machines to understand, process, and generate human language. 

Thanks to NLP, conversational AI systems can process complex inputs from the user like slang, typos, incorrect punctuation, or colloquial expressions. This will help your tech tools understand your clients, even if their queries aren’t perfectly worded.

• Machine learning, in turn, complements NLP by continuously improving the performance of AI tools – enabling systems to literally learn on the go, with each interaction. 

Uncover the full potential of conversational AI – connect with our expert developers today! Get AI guidance

Together, NLP and ML can manage tasks that go beyond static rule-based chatbot responses. For example, generative AI in retail can not only answer basic product questions but also recommend different items based on user preferences, provide personalized discounts, and adjust its language style depending on the customer’s sentiment.

At this point, you might be asking…

Is There Any Difference Between Conversational AI and Chatbots?

It’s natural to wonder why engineers separate conversational AI and AI assistants (or chatbots) into different categories. Here’s our simple explanation.

Chatbots are pretty straightforward – they can easily cope with a clearly delineated set of tasks like answering FAQs or guiding customers through simple processes. Such chatbots are usually rule-based, meaning they work off pre-set scripts and can feel a bit rigid,  if a user asks something unexpected.

The Future Trends of AI Chatbots in 2025 and Beyond Read more

Conversational AI, though, is the brainpower behind more advanced chatbots, using natural language processing and machine learning that we’ve mentioned, allowing conversational AI to pick up on context and intent – so interactions feel like talking to a real person.

In short, while all conversational AI systems can be applied as a chatbot, not every chatbot is powered by conversational AI. Think of it like this: chatbots are the tool, and conversational AI is the tech that makes the tool smarter. At CHI Software, we focus more on custom chatbot development powered by conversational AI to help our clients cope with a broader scope of tasks.

The Conversational AI Workflow: From Input to Output

Now that we’ve covered the basics, let’s take a look under the hood to get a better understanding of how conversational AI applications work on a technical level. Here’s a simplified overview:

1. User input: The process begins when a user interacts with the system, either through text or voice;
2. Natural language understanding (NLU), a part of the NLP process, analyzes the input to determine the user’s intent and extract relevant information;
3. Dialog management: Based on the understood intent, the system decides on the most appropriate response or action;
4. Knowledge base: The tool draws upon its vast database of information to formulate a relevant answer;
5. Natural language generation (NLG), another NLP component: The system constructs a human-like response using natural language.
6. Output: The tool delivers its response to the user, either as text or synthesized speech.

Now that you have an idea of what technologies are involved and why the most efficient chatbots require conversational AI capabilities, let’s see what this technology has to offer to your business.

The Key Business Advantages of Conversational AI

Conversational AI tools are all about efficiency and resource optimization. No matter which industries apply the technology, it can significantly enhance your workflows and streamline operations. But that’s just the beginning. 

Providing Unsurpassed Customer Experience

According to PwC, 32% of customers are likely to switch a brand after just one negative experience. In Latin America, this number is even higher, at 49% of respondents.

Conversational AI use cases can help companies cut the number of unsatisfied clients.

Users want to feel that they are being heard, and their questions being answered – and that’s where conversational AI chatbots step in – ready to help them by providing instant and, more importantly, personalized responses whenever your clients need them. And don’t forget about machine learning capabilities: as a chatbot interacts with more customers, it better understands context and nuance.

Boosting Operational Efficiency

Beyond just customer-facing roles, conversational AI is also improving internal operations, even in complex industries like banking and healthcare. AI assistants can take up the lion’s share of all repetitive tasks that your team is burdened with on a daily basis. Naturally, you can also optimize your resources by directing human’s effort to tasks that require more creativity and non-standard solutions. 

Facilitating Data-Driven Decision Making

One of the often-overlooked benefits of conversational AI adoption is the wealth of data it generates. Every interaction can provide insights into customer preferences, pain points, and behavior patterns. Harnessing this gold mine of information can make a big difference in informing your product development process, marketing strategies, and overall business decisions.

Improving Accessibility and Inclusivity

Finally, conversational assistants can make services more accessible. Voice-activated assistants, for example, can help individuals with visual impairments or mobility issues interact with digital platforms. Furthermore, multilingual AI tools can break down language barriers, allowing businesses to reach a more diverse customer base.

As we explore the business benefits of conversational AI, it becomes clear that this technology is not just a passing trend. But let’s move further to find out how exactly virtual assistants are changing traditional workflows.

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Virtual Assistant Use Cases Across Business Operations

In this section, you’ll see exactly how chatbots can enhance just about every business department – from sales and marketing to customer support – helping your team to work more efficiently than ever.

Conversational assistants can be helpful in any department that deals with repetitive tasks.

1. Sales: Closing Deals Faster and Smarter

Corporate sales often require a significant amount of careful effort and attention to successfully close a deal. And what about the B2C market? Reaching thousands of potential customers with a single sales team is unrealistic. What can chatbots do to help here?

• Lead qualification: A conversational AI chatbot can ask targeted questions, like, “What kind of product are you looking for?” or “What’s your budget?” Based on the answers, it qualifies leads and sends only the most promising ones to your sales team, saving a lot of working hours. This use case is particularly helpful in financial services, where AI can assess a customer’s needs and recommend them suitable products;
• Upselling and cross-selling: Virtual assistants can analyze purchase patterns and recommend additional products to upsell. For instance, if a customer buys a smartphone, the chatbot might suggest cases, screen protectors, or wireless earbuds;
• Round-the-clock sales support: Conversational AI adoption ensures your business is “always on.” Whether it’s midnight or a public holiday, customers can get their questions answered and make purchases without any delays.

You can check how CHI Software manages customer queries with our own chatbot or visit our case study page for details on our portfolio of solutions. 

The knowledge base of CHI Software’s AI assistant contains everything you need to know about our company’s experience and workflow.

2. Marketing: Personalized Engagement at Scale

These use cases are especially valuable for businesses with a diverse audience, such as banks or hospitality companies. Just a few years ago, personalizing offers and analyzing customer behavior at scale seemed impossible. Now, AI makes dreams into reality.

• Real-time personalization: Imagine a situation when a potential customer is browsing your website, and a chatbot pops up to offer a discount on the exact product a customer is looking at. Such an example of conversational AI analyzes behavior to create unique interactions that can create that extra push that drives up sales, taking the individual approach to the next level;
• Market research simplified: Do you want to know what customers think about your latest product? Instead of sending a long survey, use an AI chatbot – it gathers feedback conversationally, making the experience less intrusive and more fun;
• Social media capabilities: Managing dozens of comments and DMs on social platforms is overwhelming for employees – especially when there’s a deluge of new activity. Conversational AI can intervene instead, responding to inquiries and starting meaningful conversations with your followers – all while keeping the tone on-brand.

Top Chatbot Use Cases for Different Industries Read more

One of the projects in CHI Software’s portfolio was on a complex personalization solution featuring an in-built recommendation engine, powered by computer vision and a ChatGPT-based chatbot. This addition to e-commerce apps simplifies and transforms the whole online shopping experience.

This is an advanced example of conversational AI adoption with computer vision functionality.

3. Human Resources and Recruitment: Making Work Life Easier

HR specialists and recruiters typically spend their days communicating with people, but keeping up with an influx of requests while maintaining productivity can at times prove nearly impossible. What if chatbots could take over the routine tasks, leaving your HR team to focus on things that are best left to humans?

• Efficient recruitment: Conversational AI can act as a virtual recruiter, answering frequently asked questions like “What’s the salary range for this position?” or “What’s the next step after applying?” Chatbots can even pre-screen candidates, asking about their skills and experience, which can save recruiters hours of work;
• Smooth onboarding: New hires often have a lot of questions. AI assistants can guide them through the onboarding process, from helping newcomers in everything from acclimating to the workplace culture, to filling out forms, and explaining company policies; 
• Employee support: Does your employee need to know how many vacation days they have left or how to file an expense claim? Instead of digging through endless documents, employees can get instant answers from AI chatbots.

For example, integrating conversational AI technology with an ERP system helped our client’s employees better navigate corporate data and, consequently, improve customer satisfaction.

Integrating ERP with a conversational chatbot solution helped our client decrease manual work by 30%.

4. Customer Support: Faster and Friendlier Service

What if we told you that you don’t need a massive customer support team to handle hundreds of customer inquiries per day? Believe it! Delegating routine tasks to intelligent bots not only makes your customers happier, but can also eliminate barriers to reaching every customer possible.

• Quick issue resolution: Nobody likes waiting on hold. Conversational AI technology can promptly address repetitive issues, like resetting passwords, tracking orders, or troubleshooting simple problems, without requiring a human agent to respond.
• Breaking the language barrier: Do you have customers from all over the world? AI chatbots can provide support in multiple languages, ensuring everyone gets the help they need without delays or misunderstandings.
• Proactive updates: Imagine sending a message to one of your customers: “We noticed you’ve been browsing our troubleshooting page. Would you like assistance from a support agent?” Conversational AI excels at sending these notifications, keeping customers in the loop and increasing their engagement in the future.

How to Set Up an AI Recommendation Engine for Personalization Read more

KLM’s BlueBot is a great example of a chatbot providing users with proactive updates. BlueBot automatically sends flight information and check-in reminders via WhatsApp and Facebook Messenger. If a customer browses the KLM help center, BB can proactively ask them if they need further assistance. 

BlueBot keeps customers informed in real time, reducing confusion and the need for travelers to call customer support.

5. IT & Operations: Increasing Efficiency

There are many more examples of conversational AI for workflow optimization – IT support is definitely one of them. Answering the same repetitive questions all day isn’t an efficient use of your team’s time. But AI chatbots are perfect for handling routine tasks.

• Automated helpdesk: When employees run into IT issues (e.g., needing a password reset or access to specific software) an AI chatbot can resolve these problems in seconds. For more complex issues, AI assistants can create and escalate tickets to a human agent.
• System monitoring: Conversational AI can monitor critical systems and alert teams when something goes wrong. For instance, “The server’s CPU usage is unusually high” or “There’s a potential security threat.”
• Knowledge at your fingertips: Do you need to find an internal document but don’t know where it’s stored? Just ask your chatbot, and it’ll retrieve the right file or answer in seconds.

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6. Product Development: Building Better Solutions

Some of these use cases might not be the first that come to mind, but chatbots can even assist in product development by gathering insights directly from end users.

• Customer feedback collection: Conversational AI technology helps gather insights directly from users about what’s working and what’s not. It can ask questions like “What features would you like to see next?” and analyze the responses for actionable trends.
• Testing with real users: When launching a new product, intelligent chatbots can guide testers through the process, ask for feedback in real time, and document issues automatically.

All these conversational AI examples are impressive and truly transformative. But how can you make the AI adoption process as smooth as possible? We’ve put together some proven best practices to help.

Conversational AI Adoption: CHI Software’s Recommendations

As you consider implementing conversational AI solutions in your business, it’s crucial to approach the process strategically. What does this mean when it comes to chatbots development? Here are the insights we have to share based on our experience.

Each of these steps is essential for successful conversational AI projects.

1. Start with a Clear Strategy

Before jumping right into implementation, take a step back and define your goals. What specific problems are you trying to solve with your conversational AI projects? Is your goal to improve customer service, boost sales, or simplify your team’s internal communication? Having these questions answered will guide your decision-making and help you measure success after implementation.

Remember that the advantages of conversational AI are broad and numerous, but they’re most impactful when aligned with your business objectives. 

Essential Chatbot Requirements for AI Projects Read more

2. Choose the Right Use Cases

Chatbots are not a magic bullet that will fix every workflow bottleneck – not all business processes are equally suited for conversational AI applications. You should identify high-volume and repetitive tasks that don’t require complex decision-making. 

As you may already know, customer service chatbots can deal with frequently asked questions, freeing up human agents for more complex issues. When it comes to sales, AI assistants can qualify leads and schedule appointments. The key is to choose use cases where conversational AI can truly add value and enhance human capabilities, rather than replace them entirely. 

Not sure which chatbot use case fits your business? Our analysts will find the perfect match! Find your best use case

3. Prioritize User Experience

When implementing conversational AI, always think about your end user. Your AI assistant should feel natural and intuitive for your customers to interact with. To achieve this user experience, we usually design logical conversations, using language that matches your brand voice, and make sure your chatbot can gracefully handle unexpected inputs.

Don’t forget to provide clear options for users to escalate to human support when needed. The final goal is to create a superb experience that combines AI’s efficiency with the empathy and problem-solving skills of human customer support agents.

4. Continuously Train and Improve

Conversational AI is not a “set it and forget it” solution. To write your own success stories, you’ll need to commit to ongoing training and improvement. Regularly analyze conversation logs to identify areas where your AI assistant stumbles or misunderstands user intent. Based on these insights, you can refine the tool’s responses and expand its knowledge base.

Remember, successful examples of conversational AI learn and evolve over time. If you continuously fine-tune your chatbot, it will remain relevant and valuable to your users.

Keep your chatbot running 24/7 – hire engineers who’ve got your back! Get reliable AI guidance

5. Measure and Optimize Performance

To justify chatbot costs, it’s crucial to track key performance indicators (KPIs), which might include customer satisfaction scores, resolution rates, or time saved on routine tasks. By quantifying the impact of your AI solution, you can demonstrate ROI and identify areas for further optimization.

Don’t be afraid to experiment with different approaches and conduct A/B testing on your conversational flows. The beauty of AI is its ability to rapidly iterate and improve based on real-world data.

By following these best practices, you’ll be well on your way to successful conversational AI adoption. Remember, the goal is not just to implement a new technology, but to leverage it to create meaningful, efficient interactions that benefit both your business and your customers.

Conclusion

As you’ve seen, conversational AI applications are finding their place in many business departments, from sales and marketing to product development. If some of the described use cases reflect your current needs, don’t hesitate to start your innovation journey.

Successful chatbot implementation starts with identifying specific bottlenecks and areas for growth that match chatbot capabilities. You can start small, trying out only the basic set of features to see if conversational AI chatbots can cover your goals. 

Be it a discovery phase or a fully featured AI model implementation, CHI Software has the team to help you at every project stage. We have both proficient business analysts and generative AI engineers that help businesses feel confident in their development efforts. So, why wait any longer?

For starters, you can leave a message to our team, and we’ll reach out to you to schedule a consultation. Successful collaborations begin easier than you may think. Don’t postpone your success – use every opportunity now.

About the author

Olha Kanishcheva ML/NLP Engineer, Researcher

Olha boasts a decade-long journey in NLP, currently serving as a researcher at Jena University and a Consulting ML/NLP Engineer at CHI Software. Her expertise extends to various realms of NLP, including text summarization, named entity recognition, and keyword extraction. Olha's Ph.D. thesis explored knowledge representations and information retrieval in librarian systems.