Conversational AI for customer service - advantages, how to use & more

Conversational AI for customer service makes it easier for companies to engage with their audiences in a smart, efficient, and even fun way! Whether it’s chatbots answering common questions, virtual assistants helping with online shopping or AI-powered systems guiding customers through complex decisions, conversational AI is transforming the way businesses interact with people.

In this post, we’ll explore how conversational AI is being used in customer service today and how it can help businesses grow, streamline processes, and create more personalised experiences. 

Let’s get started!

What is conversational AI?

Conversational AI is the technology that powers chatbots, virtual assistants, and other systems designed to engage with people through natural, human-like conversations. It's what allows these systems to understand what you're asking, provide helpful responses, and even learn from each interaction to get better over time.

Here are a few statistics that showcase the growing popularity of conversational AI for customer service:

• Almost 25% of travel and hospitality brands use chatbots as part of their customer service. (Statista)
• The conversational AI market is expected to reach $14 billion by 2025. (Deloitte)
• Over 35% of consumers want to see more businesses using conversational AI bots. (Opus Research) 
• 62% of customers prefer communicating with a chatbot over waiting for a response from a human agent. (Tidio)

Benefits of conversational AI in customer service

According to Forbes, the number of companies adopting conversational AI for customer service can as much as double in the next 2-5 years. 

But what is it that makes conversational AI so lucrative for these companies? Let’s discuss the benefits:

1. It saves money

According to a report from Gartner, by 2026, chatbots and virtual assistants were expected to save businesses up to $80 billion per year in customer support costs. 

Instead of dedicating resources to answering repetitive questions or managing simple tasks, companies can use conversational AI to provide 24/7 support, freeing up human agents to focus on more complex issues. In the long run, this not only boosts efficiency but also significantly lowers the cost of operations—making conversational AI a smart investment for businesses looking to scale without breaking the bank.

2. It saves time

68% of users who have interacted with a chatbot reported that the speed of the bot’s response was a standout positive aspect. Conversation AI platforms for customer service help save up to 3 billion working hours for customer support agents.

Unlike human agents, chatbots don’t need breaks or time to look up answers—they can instantly provide solutions to frequently asked questions or assist with common tasks like booking appointments, checking order statuses, or troubleshooting issues. For customers, this means less waiting around for help, and for businesses, it means that agents are freed up to handle more complex inquiries that require a personal touch.

3. It is available all day, every day!

42% of users appreciated that chatbots could assist them outside of regular business hours.

One of the standout benefits of AI chatbots is their 24/7 availability. Whether it's late at night, during holidays, or in different time zones, chatbots ensure customers always have access to support when they need it most.

This round-the-clock service is crucial for customer-oriented businesses, allowing them to provide immediate help without relying on human agents. Customers can get answers, resolve issues, or complete tasks at any time, improving their overall experience and satisfaction. For companies, this means happier customers and a more efficient way to handle inquiries, no matter the time of day.

4. It boosts efficiency

80% of executives reported noticeable improvements in customer satisfaction, service delivery, and overall contact centre performance after implementing AI solutions.

By automating routine tasks and quickly handling common inquiries, conversational AI helps businesses provide faster and more accurate service. This reduces the burden on human agents, allowing them to focus on more complex or high-priority issues, which in turn elevates the quality of interactions. 

The result? Happier customers, smoother operations, and a more efficient contact centre that can manage high volumes with ease.

5. It helps with personalisation

According to a Hubspot survey, 64% of customer service agents believe conversational AI platforms for customer service help them give more personalised answers to customers.

With AI chatbots, businesses can analyse customer data and past interactions to provide more relevant and personalised responses. This means that instead of receiving generic replies, customers get answers and suggestions tailored specifically to their preferences and history. 

By understanding individual needs and behaviours, conversational AI helps create a more customised and engaging customer experience, making interactions feel more personal and relevant.

Voice-based conversational AI

Have you watched the movie “Her”? Or taken help from Siri or Alexa for one of your tasks? They are all AI-powered bots you can talk to, in your actual voice instead of typing commands with your keyboard.

Survey shows that almost 56% of adults in the US are using one or another form of voice AI system in their smartphones. 

But what about businesses? Are there any benefits of using voice-based conversational AI for customer service? Let’s find out:

Voice automation for customer service: facts and benefits

Did you know that over 50% of customers across all age groups still prefer using the phone to reach customer service teams? According to Zendesk, the phone remains the most-used channel for support. This is where voice automation comes in as a game-changer.

By integrating AI-powered voice automation into customer service, businesses can streamline phone interactions, handle a high volume of calls, and reduce wait times. Automated systems can quickly direct customers to the right department, answer FAQs, and even resolve simple issues without the need for a human agent. This not only speeds up service but also frees up agents to handle more complex requests, improving overall efficiency.

Challenges while building conversational AI for voice vs. text

While voice and text-based AI share common benefits and several use cases, the challenges you’ll face while building either are vastly different. The basic reason is how humans speak as opposed to how they write.

Here are a few key differences that can impact development:

• Understanding accents & dialects: Voice AI needs to recognise and accurately interpret a wide range of accents, regional dialects, and speech patterns, which can be more complex than handling typed text.
• Background noise: Unlike text-based AI, voice AI must filter out background noise, echoes, or other audio disturbances to ensure it accurately processes spoken input.
• Speech speed & clarity: People speak at varying speeds and with different levels of clarity. Voice AI must quickly adapt to these differences in real time, making it more challenging than interpreting well-formed written queries.
• Handling filler words: Voice conversations often include fillers like "um," "uh," or "you know." The AI must learn to ignore these without losing track of the conversation.
• Spontaneous input: Voice queries are often less structured than text-based ones, requiring the AI to interpret fragmented thoughts and incomplete sentences without the chance for users to refine or edit their input.
• Real-time processing: Voice AI needs to process and respond to queries much faster than text-based AI since users expect a smooth, conversational flow without noticeable delays.
• Pronunciation variations: Different ways of pronouncing words (e.g., regional variations) add another layer of complexity that voice-based AI needs to account for, whereas text-based AI only deals with spelling differences.

Here's a comparison table highlighting the key differences when building voice-based AI vs. text-based AI:

How does conversational AI work?

Sure, ChatGPT’s ability to whip up sonnets has been blowing up people’s minds, but how conversational AI works is still a mystery to many.

While most behind-the-scenes details may be too technical, there are two basic concepts that everyone should know:

1. Natural Language Processing (NLP)
2. Machine Learning (ML)

Natural Language Processing (NLP)

Natural Language Processing or NLP is the technology that helps AI understand what human conversation to respond accordingly. It is basically that part of its brain that converts words into a code that it can interpret.

When you send a message to a chatbot or speak to a voice assistant, NLP breaks down the words, grammar, and sentence structure to figure out what you're saying. It identifies:

• Intent: What are you asking or wanting to do? (e.g., "What's the weather?")
• Entities: Key details in your message, like names, dates, or places (e.g., "London" in "What's the weather in London?").

NLP makes sense of both written and spoken language, so the AI can understand what you mean—even if you don't type or say things perfectly.

Machine Leerning (ML)

Machine Learning is where AI truly impresses. 

A bot can store all the conversations it has had with humans and use that data to improve its responses over time. 

Machine learning lets computers improve over time by "learning" from data. Instead of following a strict set of rules, they look at examples (like tons of pictures or numbers) and find patterns. The more examples they see, the better they get at making decisions or predictions.

For example, if you show a machine-learning model thousands of pictures of cats and dogs, it learns to tell the difference between them. So, when you show it a new picture, it can guess whether it’s a cat or dog—just like you would after seeing many animals.

Holding natural conversations using NLP and ML

NLP and ML work together to create smooth, natural conversations. NLP understands your words, and ML uses past experiences to give a smart, relevant reply. Over time, conversational AI becomes more "human-like" by learning the nuances of how people talk, such as handling follow-up questions or understanding casual language.

Conversational AI: Accuracy and reliability

When trained on large, diverse datasets, AI can achieve high levels of accuracy, often performing tasks faster and more consistently than humans. It’s especially effective in handling repetitive tasks, analysing large amounts of data, and finding patterns that might be hard for people to see.

However, it’s important to recognise that it still has limitations:

• Data dependency: AI learns from the data it's given. If the data is biased, incomplete, or outdated, the AI can produce incorrect or biased results. For instance, if an AI is trained on biased hiring data, it might unintentionally favour certain groups over others.
• Context understanding: AI, even the most advanced, can struggle with understanding complex human emotions, cultural contexts, or nuanced language. A chatbot, for example, might misinterpret sarcasm or subtle jokes.
• Human oversight: In critical areas like healthcare, finance, and law enforcement, AI’s accuracy and decision-making should be closely monitored by humans. AI can assist, but human judgment is needed to ensure fairness, ethics, and accuracy.

In short, while AI can be highly accurate and reliable in many applications, it’s important to approach it with a balance of trust and caution, knowing that human oversight is often necessary to ensure the best outcomes.

What does conversational AI look like in customer service?

Now moving on to the real question: What does conversational AI look like in customer service?

Here are a few ways businesses use conversational AI for customer service:

• Chatbots and virtual assistants: They act like customer service agents – that are robots! They hold conversations with customers, listen to their questions, and respond with help.
• Voice-based AI bots: They are more helpful for businesses that have phone calls as their major channel of customer service inquiries.
• Self-help portals: Many businesses incorporate AI as a guide for their self-help knowledge bank. Customers can just choose through a menu of FAQs that the bot provides and find answers themselves.

What types of customer service tasks can conversational AI handle?

Here are a few customer service tasks conversational AI can handle to boost efficiency and enhance customer satisfaction:

1. Answer FAQs: Provide quick responses to common questions.
2. Order tracking: Help customers check delivery status.
3. Product recommendations: Suggest items based on user preferences.
4. Booking & reservations: Assist with scheduling appointments or making reservations.
5. Returns & refunds: Automate the process for handling return and exchange requests.
6. Troubleshooting: Guide customers through simple technical issues.
7. Lead qualification: Gather and assess customer info for sales teams.
8. Billing & payments: Help with invoice queries and payment issues.
9. Live agent handoff: Transfer complex queries to human agents when needed.
10. Surveys & feedback: Collect customer opinions through quick, automated surveys.

Conversational AI use cases in customer service

Let’s look at a few use cases of conversational AI platforms for customer service:

1. Call routing

A voice-based AI system can take a caller’s vital information like name, order number, related product or service detail, etc and route the call to the right agent. This saves the agent’s time and avoids repetitive questioning for the customer.

2. Orders and bookings

Chatbots and voice bots can automate the process of taking or cancelling orders, making reservations, and booking services after taking customer’s information. They can also track orders, calculate shipping, and provide a tentative delivery date.

3. FAQs

Conversational AI platforms can answer customer FAQs without ever having to transfer the call to a human agent. They provide accurate information and are quick at troubleshooting.

4. Customer account

AI bots can automatically change a customer’s name, address, or other details requested without a human agent spending their time on a simple task.

5. Data & analysis

AI systems can record all customer service conversations and use the data to analyse performance for improvement.

Conversational AI industries

Several industries use conversational AI for customer service. Here are a few of our favourites:

1. Finance

The conversational AI market in banking, insurance, and other financial services worldwide is expected to reach $7 billion in value by 2030. 

Here’s how the finance sector uses chatbots:

• Account management: Help users check balances, review transactions, and manage accounts.
• Fraud detection: Notify customers of suspicious activities and confirm transactions.
• Loan applications: Assist with loan pre-qualification and application processes.
• Customer support: Answer FAQs about banking services, fees, and policies.
• Personalized financial advice: Offer tailored savings, investment, and budgeting tips.
• Payment reminders: Send notifications about upcoming bills or payments.
• Transaction disputes: Help customers report and resolve transaction issues.
• Credit score monitoring: Provide updates and tips for improving credit scores.
• Investment portfolio management: Offer updates and recommendations for managing investments.

2. Health

The healthcare, retail, and banking sectors have realised an annual cost saving of $11 billion with AI chatbots.

Here are some common conversational AI healthcare use cases:

• Appointment scheduling: Patients can easily schedule, reschedule, or cancel appointments using chatbots integrated with healthcare systems.
• Medication reminders: Chatbots send reminders for medication adherence, improving treatment outcomes for patients with chronic conditions.
• Mental health support: AI chatbots provide mental health support through conversational therapy, offering emotional assistance in real time.
• Patient monitoring: Chatbots help monitor patients with chronic conditions by collecting health data and offering timely insights to healthcare providers.
• Billing and insurance queries: They assist patients in understanding medical bills and insurance claims, streamlining payment processes.
• Post-treatment follow-up: AI chatbots conduct follow-up consultations, reminding patients of care instructions and gathering feedback on recovery.

3. Hospitality

Chatbots help reduce customer service costs by 30% by automating repetitive tasks and freeing up staff​. 

These use cases demonstrate how AI chatbots are improving operational efficiency while also enhancing the guest experience:

• Optimising the booking process: Chatbots guide users through the booking process, helping them find the best deals and offering personalised recommendations.
• Virtual concierge services: Chatbots can act as a 24/7 virtual concierge, assisting with local recommendations, dinner reservations, and event planning.
• Customer support: Chatbots quickly answer common guest queries, such as hotel amenities, restaurant hours, or transportation options.
• Personalised guest experience: AI chatbots use guest preferences to deliver tailored suggestions and special offers, enhancing guest satisfaction.
• Event coordination: For business travellers, chatbots sync with work calendars, keeping them informed about key events or last-minute changes.

4. Automotive

• Purchase assistance: AI chatbots can assist potential buyers by providing instant answers to questions about pricing, financing options, available models, and delivery timelines, streamlining the buying process.
• After-sales support: Chatbots handle common post-purchase inquiries, such as scheduling service appointments, resolving warranty questions, and providing vehicle maintenance tips​.
• Proactive maintenance: AI chatbots remind customers of necessary vehicle maintenance, predicting when servicing is needed based on real-time data from vehicle sensors​.
• Test drive scheduling: Customers can easily schedule test drives with chatbots integrated into dealership websites, improving user experience and conversion rates​.
• In-vehicle assistance: AI chatbots provide voice-enabled assistance for tasks like navigation, infotainment, and vehicle settings customisation, enhancing driver safety and convenience.
• Virtual product demos: Chatbots engage users with interactive features, such as personalised vehicle configurations and virtual test drives, offering immersive experiences before purchase.

Approaches to deploying conversational AI for voice

There are 4 methods to deploy a voice-based conversational AI for customer service:

1. From scratch

You can build a voice-based chatbot from scratch using NLP models, speech recognition technology, dialogue management systems, multimodal interfaces, and voice synthesis systems.

You can create a highly customisable bot, but the DIY route is extremely technical.

2. With third-party vendors

You can avail of services from third-party vendors like Google’s DialogFlow and integrate their voice-based bots into your system.

3. Turning chatbots into voice bots

It is possible to port your existing chatbots into voice-based bots by using text-to-voice interpreters and technology.

4. Call Trengo for help!

A specialised conversational AI platform such as Trengo can help you transform your customer service operations with its innovative and multi-use AI bot, the AI HelpMate.

Conclusion

Experts predict that AI-based solutions and technology are how modern businesses can stay competitive. Employing conversational AI for customer service not only makes customers happy but also saves your team’s time and effort.

Are you ready to take your customer service up a notch? Schedule a demo with Trengo today and see how our AI HelpMate can help you resolve 80% of your customer conversations instantly.