What is Conversational AI? Unraveling the Role of Artificial Intelligence in Enhancing Customer Interactions

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Machines used to be best known for their jankiness when it comes to movement, speech recognition and language. Now, machine learning technology allows for almost seamless mimicry of natural human behavior. One of these would be the “easy to learn, hard to master” behavior of human conversation.

As a business, you’d prefer to address every single customer concern. Unfortunately, it’s just impossible and inefficient to do so 1-on-1. No company has the staff to personally address every single query. This is where conversational AI comes in, where you need a bit of human touch that your humans can’t reach.

Picture this: A consumer is shopping online late at night, wondering which pair of shoes to buy. A chat window appears, and instead of scrolling through endless FAQs or waiting for business hours to get answers, the shopper engages in a chat with a friendly conversation with AI that knows the latest trends, the shopper’s style, and even their shoe size. That’s the magic of conversational AI, and it’s transforming the way humans connect with technology.

How does conversational AI work?

 Image of person using conversational ai
This is an AI-generated image by the TaskDrive team

A lot more goes into it than just typing in words and having more words pop out. It’s actually a combination of both technology and technique centered around natural language processing (NLP).

Here’s a breakdown of the conversational artificial intelligence process:

Step 1: Understanding the input

Ever had a chat with a bot and felt like it truly “got” what you were saying? That’s the result of the AI’s input understanding. Conversational AI technology is pretty sharp at comprehending and interpreting messages. They can recognize the intent behind your words, whether you’re asking a question, seeking information, or expressing frustration.

How does this work? First comes the contribution of words. The user would type out the input. The conversational AI then uses NLP techniques to understand human language and analyze the input.

Conversational AI does this by recognizing keywords, identifying grammatical parts of each word, and identifying things like names and dates. This all then builds up a context for which the AI can best understand your input. You can think of this as the AI maneuvering through the language barrier between your understanding of language and its own.

Step 2: Intent recognition

With the context confirmed, the conversational AI will now determine the intent or purpose of the input. It will refer to the examples built up by machine learning models to match the input to what those models have already learned.

This is no different from learning that when someone asks you the question “How old are you?” (the input). Then you refer to the times your parents taught you that people will ask you that question because they want to know your age (the intent).

Therefore, your reply is “I am X years old.”

In a person-to-AI context, this would be you asking “What’s the weather today?” (the input). The AI would then recognize the intent as a “weather query” then generate an appropriate response using human language.

Step 3: Entity extraction

Aside from recognizing intent, the conversational AI will also identify pieces of information relevant to your request. These are called ‘entities.’

Going back to the weather example, the entity would be the date or location. The AI will now understand that you might also want to know about the ‘when’ (date) and the ‘where’ (your location).

Natural language generation would then help refine the AI’s response, now considering that you want to know about the weather today in your location.

Here’s another example. When you say, “I need a flight to New York on September 10th,” Conversational AI doesn’t just hear words; it identifies key information like:

  • Destination: New York
  • Date: September 10
  • Service: Flight

This entity extraction superpower helps artificial intelligence give you more relevant responses by honing in on the specifics.

Step 4: Context management

Now the conversational AI maintains the consistency of the conversation between the two of you. It’ll keep track of the ongoing conversation—ensuring its responses stay relevant to the current context.

This context would include your previous queries, their own responses, and any relevant information to you as the user.

Imagine you’re chatting with a friend, and the conversation flows naturally from topic to topic. Conversational AI aims for the same smoothness. It’s all about context.

If you mention “it” in one message, AI remembers what “it” refers to in the next. So, when you continue the conversation, AI doesn’t go, “Huh?” It keeps everything coherent. Just like chatting with a buddy who remembers what you were talking about yesterday.

Step 5: Response generation

Based on steps 1 to 4, the conversational AI then generates an appropriate response. What sets it apart from interactive voice response and the usual search engine results is that it crafts its response to sound natural and almost like human speech.

Conversational AI tools don’t just pull pre-written responses from a dusty library. It crafts responses on the fly. It adapts its language to match your tone, whether you’re formal or chatty. It’s like having a conversation with an AI that’s a master of mimicry, making you feel heard and understood.

Sequence-to-sequence machine learning models are best for generating these responses. Think of these models as virtual storytellers. When you give it input, it puts all its learned examples together and gives a response.

Step 6: Personalization

Remember how your barista knows your favorite coffee order? Conversational AI does something similar—but with data. It personalizes interactions based on your past behavior, preferences, and profile. So, when you engage with it, it’s not just any AI; it’s your AI, serving up an experience tailored just for you.

Once you’ve interacted multiple times with these systems like conversational AI chatbots, they may even pick up your tone, emotion, and communication style.

Step 7: Dialogue management

These techniques can help the AI use user input, system responses, and contextual cues to decide the best course of action in the conversation.

Conversations can be like a twisty river, flowing from one topic to another. Conversational AI doesn’t get lost in the currents due to dialogue management techniques. It manages dialogues by handling multiple topics and shifting contexts smoothly.

Step 8: Learning and improvement

Last but not least, conversational AI is a perpetual learner. It’s not one of those ‘set it and forget it’ gadgets. It constantly learns from interactions with you and others, improving its responses over time.

So, the more it chats, the smarter it becomes. Having more conversations with AI over time help.

7 benefits of using conversational AI technologies for your business

Image of an online consumer conversing with a conversational ai solution instead of a human agent
This is an AI-generated image by the TaskDrive team

If you’re a small business, integrating AI into your operations has huge benefits. But what if you’re already a large and established company? Is AI still relevant to you?

The answer is a resounding yes! AI can still play a crucial role in your business. In fact, one of AI’s biggest strengths is its scalability. (More on that later!)

Here’s a rundown on the benefits companies both big and small can get from using conversational AI solutions:

1. Enhanced customer engagement

This is where conversational AI’s ability to personalize responses shines. It does more than just answer and respond to queries. It can even provide product recommendations and guide your customers through the buying process—all in a conversational manner, which leads to higher customer engagement and satisfaction.

Example: On a travel website, a chatbot pops up and asks the online user if they need assistance. They inquire about the best time to visit Bali, and the bot provides not only the information but also suggests a personalized itinerary based on user preferences.

2. 24/7 customer support and query handling

Conversational AI technologies can fill the role of round-the-clock customer support aside from being able to quickly and accurately handle large volumes of customer queries at the same time.

By having your AI handle all the common and basic support tickets, you can ensure your customers receive assistance whenever they need it. This can help you reduce the burden on your human agents while still giving prompt responses to customers. In turn, it improves your customers’ overall shopping experience. After all, for 44% of survey respondents, receiving fast responses during a purchase is the most important feature of a website.

Example: It’s 2 AM, and an online user is troubleshooting a technical issue on a website. Instead of waiting until morning, they engage with a chatbot that guides them through the solution step by step.

3. Seamless shopping assistance

For a more personalized eCommerce experience, conversational AI solutions can tailor their responses according to customer preferences and shopping history.

Artificial intelligence and eCommerce really seem to be a match made in heaven. They can help your customers discover products they might be interested in adding to their cart.

This means your AIs can also open up cross-selling and upselling opportunities. They can suggest complementary products or upgrades, leading to more sales. In fact, business leaders have claimed that chatbots helped increase sales by 67% on average.

Example: A student is shopping for a new laptop, and the AI chatbot on the eCommerce site asks about the requirements. It then suggests a list of laptops based on the student’s needs, complete with user reviews and price comparisons.

4. Reduced cart abandonment

With as much as 70% of online shopping carts being abandoned, cart recovery is something that a lot of eCommerce businesses are focusing on. Interestingly, conversational AI can help prevent this issue from happening by addressing uncertainties and concerns that customers have which might lead to cart abandonment.

Your AI systems can provide additional information, address questions, and maybe even offer discounts. And with all concerns addressed, your customers can push through with their purchases.

Example: A shopper has added items to their shopping cart but hesitates before checkout. The chatbot pops up with a special offer or answers last-minute questions, persuading the shopper to complete the purchase.

5. Reduced support costs and scalability

Thanks to conversational AI, businesses can automate the more routine customer support tasks. Doing so reduces the need for a large support team of human agents, resulting in cost savings. Additionally, it effortlessly scales to handle an influx of customers during peak times—without the additional cost.

According to Gartner, conversational AI will reduce labor costs associated with support agents by $80 billion in 2026.

Example: During a product launch, the website experiences a surge in traffic. The AI chatbot handles the increased volume of inquiries without the need to hire additional support staff.

6. Omnichannel support and integration

Be it your website, messaging apps, voice assistants, or even social media, conversational AI technologies can be integrated into various platforms. This way, your customers can interact with your brand through their preferred channels—ensuring consistent support and communication.

It’s not just about having a presence on multiple channels, but it’s about ensuring that communication across those channels remains smooth. Businesses that implement omnichannel strategies actually have customer satisfaction rates that are 23% better. As more businesses use conversational AI systems to level up their omnichannel support and customer experience, it’s likely that customer satisfaction will increase too.

Example: An online user initiates a conversation with a business on their website and later continues it on their Facebook page without missing a beat. The AI remembers the context and history, providing a cohesive experience.

7. Early issue detection

Preventing a problem is always better than dealing with its aftermath. Conversational AI technologies can detect potential issues, such as customer dissatisfaction or technical glitches, and address them before they escalate into major headaches.

Using the data from how your customers interact with your conversational AI can help you identify these emerging issues. Because of this sort of early pattern detection and warning system, you can be more proactive with your solutions.

Example: A customer expresses frustration about a delayed delivery in a chat. The AI recognizes the issue and offers an immediate solution, such as a partial refund or expedited shipping, to turn the situation around.

What are some conversational AI systems and platforms you can use right now?

Photo of a customer support agent using a conversational ai platform for work
This is an AI-generated image by the TaskDrive team

It really depends on what you’re looking for. What goal or objective do you need to accomplish using conversational AI? What business operation are you looking to streamline? Here are the top suggested AI platforms you can check out per category:

Customer support and engagement platforms

  • Zendesk: This is the go-to platform for delivering exceptional customer support. It offers live chat capabilities, allowing businesses to interact with customers in real time, solve problems, and answer questions promptly. With Zendesk Chat, you can turn website visitors into happy customers by using AI-powered chatbots for automated responses.
  • Live Chat: Live Chat is another fantastic tool that enables instant communication with website visitors, making it easy to provide assistance and support when it’s needed most via live chat support, ticketing, and AI chatbots.
  • Intercom: Intercom is a versatile platform that combines live chat, email, and automation. It’s not just about answering questions; it’s about nurturing customer relationships throughout the entire journey and converting leads into loyal customers.

Conversational marketing and sales

  • Drift: Drift specializes in conversation-driven marketing and sales. It focuses on helping businesses turn their website visitors into qualified leads through personalized conversations. Drift’s chatbots and live chat enable businesses to engage with potential customers, answer inquiries, and guide them toward making a purchase.
  • Chatfuel: ChatFuel is an excellent platform for creating chatbots on Facebook Messenger. It’s a great choice if you’re looking to automate your customer interactions, sales, and marketing campaigns
  • Conversica: Conversica uses an AI-powered sales assistant to engage leads through natural, two-way email conversations. Other tasks it can help with include automating sales conversations, qualifying deals, and boosting conversation rates.

eCommerce integration

  • Tidio: Tidio provides customizable chatbots and live chat with integration capabilities for popular eCommerce platforms so brands can provide real-time support and handle the recovery of abandoned carts more efficiently.
  • Shopify Ping: This one’s designed for Shopify users and while it’s not exclusively a conversational AI platform, Shopify Ping provides eCommerce integration options for chatbots and virtual assistants. It allows businesses to seamlessly integrate AI-powered chat and support into their online stores, enhancing the shopping experience.

AI-powered virtual assistants

  • Nuance: Nuance is a leader in AI-powered virtual assistants and voice recognition technology. It’s widely used in healthcare, automotive, and customer service industries to create intelligent virtual assistants capable of handling complex tasks and providing voice-based interactions.
  • Ada: Ada offers an AI-powered virtual assistant that helps you automate your customer support so you can handle a wide range of customer queries. It’s primarily known for its flexibility in designing AI-powered conversations, making it easy for businesses to build and deploy virtual assistants to handle future interactions.
  • Inbenta: Inbenta focuses on natural language understanding and aims to improve customer service by providing accurate and context-aware responses.

Multi-channel communication

  • HubSpot Conversations: As part of the HubSpot suite, this provides live chat and chatbot tools. They’re integrated with HubSpot’s CRM and marketing automation features for easy multichannel communication.
  • Freshchat: It’s a modern messaging platform that offers chat website chat, mobile app messaging, and social messaging apps. This helps businesses deliver real-time support, automate responses, and provide a seamless customer experience on various communication channels.

Strengths and weaknesses of conversational AI

Cellphone showing a chatbot conversation
This is an AI-generated image by the TaskDrive team

Yes, integrating conversation AI into your business is starting to sound like the best idea ever. Is there anything that can go wrong? With all its strengths, it still has its weaknesses—or some things to watch out for since it’s still essentially a new technology. So understand that while it can be the Superman of your business, it still has its fair share of kryptonite.


  1. 24/7 availability: Conversational AI operates round-the-clock, providing instant assistance to customers without time zone restrictions or the need for breaks—ensuring your business is always accessible.
  2. Scalability: It effortlessly handles high volumes of inquiries and interactions, making it ideal for businesses that experience fluctuating demand without the need to hire additional staff.
  3. Consistency: Conversational AI delivers consistent responses, so customers receive accurate information and assistance every time—regardless of who they’re chatting with.
  4. Data analysis: It collects valuable customer data during interactions, helping businesses understand customer preferences, pain points, and behavior for informed decision-making.
  5. Personalization: Conversational AI can personalize interactions based on user data, creating a more tailored and engaging experience for each customer.
  6. Cost-Efficiency: Automating routine tasks and queries reduces support costs and improves the efficiency of customer service operations. Chatbots can handle routine queries, helping you save costs in customer support operations.
  7. Operational efficiency and Speed: With conversational AI, you can provide instant responses to customer queries. This results in reduced waiting times—enhancing customer satisfaction.


  1. Lack of empathy: Conversational AI lacks the emotional understanding and empathy that human agents can provide, making it less suitable for handling sensitive or emotionally charged situations. Customers may also prefer interacting with humans instead of voice assistants for important or sensitive matters. Interactions with AI can feel impersonal to them.
  2. Complex queries: It may struggle with complex or nuanced queries that require deep understanding or creativity, often resulting in generic or inaccurate responses. Conversational AI may struggle with handling complex, nuanced, or context-dependent queries that require human judgment and reasoning.
  3. Learning curve: Implementing Conversational AI requires time and resources for training and optimization. This applies to both the business and its customers. For the business, developing and training AI models can require technical expertise and resources. This also includes all the maintenance and updates in between. On the customer side, they might find it challenging to interact with chatbots and end up feeling frustrated if the system misunderstands their queries.
  4. Language and culture limitations: Language barriers and dialect variations can challenge AI’s ability to understand and respond to diverse audiences accurately. Conversational AI may also have difficulty understanding slang or cultural nuances, leading to misinterpretations or incorrect responses.
  5. Dependency on data: Effective AI responses rely on the quality and quantity of data available, potentially leading to subpar performance when data is limited or biased.
  6. Security concerns: Conversational AI can pose security risks if not adequately protected against malicious use, potentially exposing sensitive customer data or becoming a vector for cyberattacks.
  7. Over-automation: This can lead to a depersonalized customer experience, causing frustration and alienating customers who prefer human interaction.

The verdict: Conversational AI is a tool that excels best in the right hands

A person using AI to handle customer inquiries
This is an AI-generated image by the TaskDrive team

Conversational AI has emerged as a powerful ally, transforming the way businesses connect with their customers. From enhancing customer engagement to providing 24/7 support, reducing cart abandonment, and seamlessly integrating into various industries, conversational AI is more than a trend—it’s a game-changer.

But like any tool, it comes with strengths and weaknesses. While it excels in consistency, scalability, and cost-efficiency, it may fall short in empathetic understanding and handling complex user queries well.

So, what’s the key takeaway here? It’s about striking the right balance. Combining the strengths of AI with the human touch where needed to create exceptional customer experiences. It takes an experienced operator of AI tools to bring out their full potential for your business. Skip the learning curve of AI setup and operation and leave it to our AI-powered Virtual Assistants!

Book a call with us now and bring out the full profit potential of conversational AI to your business.

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