What is a Key Differentiator of Conversational AI?

A study by Deloitte mentions the conversational AI market is expected to reach almost  US$14 billion by 2025 with a CAGR of 22% during 2020–25.

Industries are extensively using conversational AI applications to address various use-cases.

Yet, many still don’t understand the meaning of conversational AI in its entirety because most of us still confuse them with chatbots.

So what is the key differentiator of conversational AI?

Conversational AI includes additional elements that you wouldn’t find in chatbots. In other words, every chatbot is a conversational AI but every conversational AI is not a chatbot.

So yes. Conversational AI is what every chatbot would aspire to be.

The overachiever neighbor or maybe the class topper. 

Get the drill?

To first understand what is the key differentiator of conversational AI you need to take a step back from what you already know and let go of the myths surrounding it.

Now let’s take a look at its definition, and how it is different from traditional chatbots.

What is Conversational AI? 

Conversational AI describes communication driven by AI technology. These can include chatbots and virtual assistants that we find in Amazon’s Alexa or Apple’s Siri.

The keyword here is AI. AI-backed communication leverages data, machine learning (ML), and Natural Language Processing (NLP) engines to recognize user inputs. They are also the closest to mimicking human interactions and include a variety of conversational technologies such as ai-driven voice bots, and voice and text assistants.

Let’s break the definitions down and understand what are the principles of conversational AI.

Natural language processing: NLP facilitates bots to interpret the message and formulate a human-like response to the user.

Machine learning: This is an algorithm where technology automatically learns by itself through repeated use.

Automated Speech Recognition (ASU): This is a piece of tech that interprets the voice-based inputs. You don’t need this for text-based inputs.

Text-to-Speech (TTS):  This component responds to a machine-generated voice. 

How does Conversational AI Work? 

Since conversational AI leverages NLP to mimic human-like interactions, its process of responding varies greatly from typical chatbots. It also combines different technologies. Let’s take a look at this process in detail:

1. Receive Data

The process starts with the user having a query and putting forth their query in the form of input via a website chatbot, messenger, or WhatsApp. Unlike chatbots that just have text-based inputs, input generation in conversational AI can be both text-based and voice-based inputs. 

2. Analyze Data

This is where NLP and NLU elements come into play. After the user inputs their query, the engine breaks the texts and tries to understand the meaning of those words.

In case the user has used a voice-based input, the AI will understand the input using the Automatic Speech Recognition that we discussed before. The tool first applies to the voice note to analyze the input into a language that is recognized by the machine. It then processes the input and analyzes it to understand the intent behind the query.

The intent analysis is also where training plays an essential role. Based on how well you train the AI, it will have the ability to recognize multiple intents and utterances. 

3. Generate Response

A key differentiator of a conversational AI chatbot is that it uses Natural Language Generation (NLG) to respond to users based on intent analysis.

NLG takes it a notch higher since instead of just generating a response, NLG fetches data from CRMs to personalize user responses. Before generating the output, the AI interacts with integrated CRMs to go through the profile and conversational history. This way it narrows down the answer based on customer data and personalizes the responses.

As for voice bots, the response is converted from text to speech and the user gets a response in the same format as their query. 

4. Machine learning

Machine learning algorithms are a distinct quality of conversational AI. When the AI generates responses, it’s possible that it may not be able to interpret the query and gives out a wrong response. It’s because the AI is not trained yet.

Think of machine learning in the same way as teaching a language to a child. They will make errors but they get better with time as they start practicing. The same applies to AI. As it converses more with users, it will learn the most accurate responses to user queries. 

Conversational AI Vs Traditional Chatbots

We finally get to the most pertinent question of this piece — what is the key differentiator of conversational AI?

Now that you know what conversational AI is, you need to understand what conversational AI isn’t and what chatbots are.

Traditional chatbots or button-based chatbots have certain limitations. They’re not always inclusive of AI and sometimes follow a rule-based format. They are built using a drag and drop interface and designed to follow the decision tree format.

It also means that a chatbot can only give answers to predefined questions which is what makes them distinct. They’re great for smaller businesses that have straightforward questions and answers.

Rule-based chatbots don’t have the machine learning algorithm which means they don’t need extensive training. The implementation process is also fairly easy and less complicated.

But such chatbots have limitations in executing complex queries and that’s where a conversational AI chatbot steps in, especially when the user doesn’t follow the expected path and asks for a live agent instead. Let’s take a holistic view of what is the key differentiator of conversational AI when compared to chatbots.

Benefits of Conversational AI

1. Recommendations and Upselling Opportunities

37% of CEOs leverage conversational AI to deliver exceptional customer experience. And it aims to achieve that through intensive personalization.

AI has the ability to take into account customer preferences, demographics, weather, and buying history before conversing with the customer. It provides the business with an opportunity to accurately upsell and recommend products that the customer would be interested in buying.

Instead of manually storing this data and expecting the employee to fetch customer history before recommending products, AI helps you automate the process.

Whole Foods is an intriguing conversational ai example. Whole Foods has a messenger chatbot that is popular for providing product recommendations and cooking inspiration that helps shoppers find recipes based on their choices. The bot identifies what resonates with the prospective customers and builds recommending features to drive the conversation to a positive outcome. Using this tactic also drives a lot of traffic to its website from messenger and improves customer experience. 

2. Wider Understanding of Contexts

For a long time, one negative attribute that was associated with chatbots was that they didn’t have the intellectual bandwidth to understand complex queries. With conversational AI, that’s no longer the case.

Chatbots don’t receive requests that aren’t fed into the systems which can hamper the entire conversational experience for the user.

Instead of forwarding the conversation to a live agent which usually happens in a chatbot when it fails to recognize the intent, conversational AI attempts to understand the context behind a customer query while the customer is talking to AI.

How does it do that?

It focuses on prior discussions, chats, and customer history to take into account the context of the customer query.

Conversational AI possesses a greater contextual maturity and lets the user decide the conversational narrative instead of driving them on a pre-designed path.

3. Higher Scalability and Accuracy

In industries like eCommerce and banking, scaling your business while keeping the personalization intact is challenging. While chatbots take care of the basic FAQs, you need to have a mechanism that lets you still reach out to every customer and provide them the same experience as they would want in a physical space.

Conversational AI ensures that every visitor that lands on your website or any other platform will be addressed with a tailor-made conversation. As soon as users input their queries, they get a response via a voice-based bot or a chatbot.

A well-trained AI bot will provide accurate responses paving the way for a self-service query resolution. It also offers consistency in the quality of the conversations since it can understand the intents with better accuracy. 

4. Greater Reachability with Multilingual Chatbots and Voice Assistants

Any conversational AI that we have today showcases multilingual prowess that allows businesses to cater to markets that they couldn’t have before because of language barriers.

Multilingual bots detect, interpret and respond in almost any language. Hence, no service or customer interaction is limited by linguistic differences, making your business accessible to a wider range of customers.

A key differentiator of conversational AI is also a voice-based service. It adds a layer of convenience since the number of voice searchers is consistently increasing. Over 43% of smart speaker owners now use the tech to shop. It helps you fill the market gap and add a medium of access. 

Conversational AI Use-cases 

Conversational AI is making a mark in various industries because of the niche benefits it offers to them. Check out some of the most common use-cases of conversational AI.

1. Online Retail

Since online shopping has taken over the retail industry by storm, it has greatly benefited from conversational AI. Researchers believe that 70% of conversational ai interactions will be related to retail by 2023. 

Online retailers have been able to add a new engagement tool. It’s helping them in providing product recommendations, gaining customer insights from previous purchases, and providing personalized customer support across the globe. 

2. IoT Services

Smart speakers have become an integral part of our daily lives. Because of their ability to sound human-like and having the convenience of voice search, AI-enabled devices are becoming valuable helpers to customers. They’re using it to control house remotes and speakers, plan their days, get weather updates, and manage their tasks. 

The speech pattern encourages a natural conversational flow in devices like Amazon Alexa and Google Home. 

3. Healthcare

Conversational AI is assisting healthcare professionals in diagnosing health issues online by asking relevant questions to patients. It also helps healthcare institutes schedule medical appointments while having the symptoms and diagnoses beforehand. 

It reduces the wait time to get in touch with a medical professional and allows the professional to get to address the patient's issue faster. Overall, conversational AI also provides access to crucial patient data. 

4. Finance and Banking 

In banks and financial institutions, conversational AI and voice bots can provide answers to user balances and process transactions. They are also the go-to banking assistants that provide tips on how to make smart investment decisions. You can automate key functions and reduce your operating costs to a great extent. 

5. Customer Support 

Regardless of the industry, conversational AI has proved its capabilities in customer support. From order management, providing access to order tracking to complain management, and collecting customer feedback, conversational AI is only enhancing the customer experience and making it wholesome. 

6. Sales and Marketing 

Using conversational AI, you can entirely automate your lead generation and qualification process. It significantly reduces the load of the sales team in filtering the leads and improves the coordination between the marketing and sales departments. Conversational AI is also widely used for conversational marketing efforts which aim at engaging prospects through human-like conversations. 

Finishing Thoughts 

Now that you know what is the key differentiator of conversational AI, you can ensure to implement them in the right places.

When we say conversational AI is more advanced, it means that the AI is able to understand the nuances in human interactions which isn’t possible in chatbots. Of course, it takes time to get there but once it learns the ropes of human interaction, it catches on quickly leaving less room for errors.

But conversational AI is still a new phenomenon and industries are still learning its mechanisms. Similarly, if you need assistance in getting started, you can get in touch with us, and we can help you get acquainted with the tech and assist you with the implementation process.