Your customers hate basic chatbots. They don’t like selecting pre-defined options, they don’t like being routed to an agent for simple queries, and they don’t like slow responses. In fact, only 45% of customers are “very satisfied” with their interaction with businesses. Clearly, there’s ample room to improve customer experience when it comes to communication.

That’s where conversational AI comes in. Conversational AI elevates customer experience with quick and personalized responses. It can answer complex queries and use natural language, which helps deliver a more immersive experience. But what exactly is conversational AI? And is conversational AI worth the investment?

Let’s find out.

What is conversational AI?

Conversational AI is a group of technologies that enable tools like customer service chatbots and virtual agents to understand and generate responses in natural language. Think about the last time you used a chatbot. Was your experience dull because the chatbot kept asking your binary questions? That’s exactly how your customers feel when using them. The previous generation of chatbots was incapable of understanding natural language.

Chatbots powered by conversational AI use natural language processing (NLP) to help the machine understand human language. They use machine learning and generative AI to generate highly personalized and accurate responses.

The result? Your customers can use bots to resolve queries faster, more effectively, and without involving a human.

How does conversational AI work?

Conversational AI primarily relies on two components to understand natural language and generate human-like text or speech. However, there are other components involved in conversational AI. We talk about these below.

Machine learning

Conversational AI uses machine learning to reinforce responses that it identifies as accurate. Machine learning is a technology that involves using algorithms and data to create autonomous or semi-autonomous machines. Machine learning algorithms improve at recognizing patterns and making data-driven predictions over time.

Suppose a customer sends a message — “I can’t log into my account. Please help.” The machine learning algorithm looks at the most accurate responses it has generated for that query until now to determine the response.

In this case, the response could be “Have you lost your password? Click here to reset your password.” Even though other potential responses exist, such as “Is your subscription active? Check here” the algorithm suggests a password reset based on past interactions.

Natural language processing

NLP analyzes the language in a customer’s message using machine learning. It uses a four-step process to accomplish this:

• Input generation: Customers provide text or voice input via a chatbot or over the phone.
• Input analysis: Conversational AI uses NLP to understand the text’s meaning and detect the message’s intention. When the input is speech-based, conversational AI uses automatic speech recognition as well as NLP.
• Dialogue management: Natural language generation (NLG), a subset of generative AI, formulates a response.
• Reinforcement learning: The machine learning algorithm refines responses to improve accuracy over time.

NLP uses natural language understanding (NLU), a subset of NLP, to identify language attributes like context, intent, sentiment, and semantics.

Conversational AI tools

You can deploy text or speech-based conversational AI tools to automate business processes in customer service, account management, and other areas. Here’s a quick overview of the most common ones:

Chatbots

Chatbots are used for text-based interactions. Traditional chatbots were essentially a decision tree. A predefined set of rules drove customer interactions — every question triggers relevant follow-up questions until the system finds a viable response. Slow. Tedious. Frustrating.

A ton of questions and a generic answer from rules-based chatbots translate to poor customer experience — that’s why you need a conversational AI chatbot. They can drive conversations and personalize responses using their ability to understand human language and intent. You can add chatbots to your website , app, or social media to ensure customers have the option to get in touch as easily as possible.

Voice assistants

A voice assistant converts audio into machine-readable text to understand the message in the audio and its intent. Suppose your finance team wants to request a copy of a specific invoice. They can use a voice command to retrieve the invoice — zero manual effort and quick retrieval. In fact, you can automate your call center with voice AI to drive down costs and improve productivity.

NLP models can add multilingual support to voice assistants. Multilingual support is a must-have if you have overseas offices, allowing staff to use the voice assistant in their native language.

You’ve likely “pressed 8 to speak to a customer service representative.” This traditional IVR (interactive voice response) system relies on menu options and keypads to navigate options or speak to a rep, while voice assistants are equipped to resolve customer queries by themselves using AI and natural language.

When a customer requests to receive their bank statement via email with a voice command, the voice assistant can interpret the message and trigger the email by itself. Voice assistants can also generate voice-based responses to customer queries. So if the customer hasn’t had any transactions in their account, the voice assistant can respond, “There are no new transactions in your account. Would you still like to receive a statement?”

Benefits of conversational AI

Conversational AI helps reduce costs, offer better customer service, and understand your customers’ preferences more clearly. Let’s dive deeper into how conversational AI offers these benefits to your business.

Cost savings

Conversational AI reduces costs across functions like customer service and account management. In fact, Gartner estimates that conversational AI can reduce agent labor costs in contact centers by $80 billion .

Let’s walk through some numbers to see how conversational AI saves money. Suppose you pay $6,000 per month to a customer service representative to resolve customer queries. Then, you decide to use a conversational AI chatbot starting next month. Let’s assume the chatbot reduces the volume of queries the rep handles by 95%, allowing them to focus on more strategic parts of the business.

Assume you receive an average of 500 queries per month. That’s $12 per query ($6,000 / 500). At that rate, you save $5,700 (500 x 95% x $12). You can assign the rep to a more strategic function of customer service like monitoring CSAT scores and coming up with a blueprint to improve CSAT.

Reassigning the employee to strategy generates a higher ROI for the $5,700 you save on resolving customer queries.

Faster and immersive customer service

Waiting hours for a response is not the kind of customer service that will help your business grow. A study by Jay Baer , a customer experience and marketing expert, suggests that two-thirds of customers think response speed is as important as price, and more than half of the customers hired the first business to respond even if it was more expensive.

To win more deals, offer quick and personalized responses to make a good impression. Remember that fast and personalized responses are no longer “nice to have.” They’re necessary to provide a customer experience that doesn’t leave your customers frustrated.

Conversational AI helps scale customer service. Think about peak season — do you hire temporary staff to handle customer calls? Scaling up and scaling back to meet seasonal demand is tedious and expensive. Using an AI-powered chatbot empowers you to offer personalized customer service at scale during peak season — take Tile for example, a company that managed the holiday rush with an Ada chatbot that resolved 52% of the queries and handed off the rest to the right agents.

Multilingual support

A study highlights that 40% of global consumers won’t buy from a company that doesn’t speak their native language. Multilingual support is essential when you have clients outside the US. Your client in the Netherlands might want to interact with your chatbot or voice assistant in Dutch.

Instead of hiring service reps to offer multilingual support, take the more cost-effective route by deploying conversational AI. Conversational AI tools can interact with your customers in their preferred language and generate personalized messages, just like they do in English.

Insights into customer service

Conversational AI can pick up valuable data points while interacting with your customers. Suppose you’re an ecommerce brand. Many customers have been asking if your bestselling t-shirt is in stock in black. Currently, you don’t have those t-shirts in black, but conversational AI can monitor these interactions and figure out that there’s demand for the product.

Similarly, conversational AI can pick up on your customers' most common problems when using your product or service, identify the demographic your product is most popular with, and more.

Challenges of conversational AI

Conversational AI is a powerful technology. It can transform how your business interacts and make processes more efficient. But before you derive these benefits, you need to understand the challenges of conversational AI and mitigate them to maximize your ROI.

Privacy and security

Conversational AI tools collect massive volumes of data. This includes your customer’s messages, location, and sometimes confidential information like bank details. Before you start using conversational AI, think about how to protect user data against security threats like data breaches and impersonation attacks and privacy concerns like unauthorized user profiling.

Privacy and security threats can pose great risks to your business as we explain in our generative AI toolkit for customer service leaders. For example, unauthorized profiling can expose you to risks like loss of business resulting from incorrect assumptions, or the AI tool or LLM provider could leak, use, or sell your company’s profile.

Ethical considerations

When creating conversational AI tools, it’s important to keep ethical issues related to communication in mind. A chatbot shouldn’t generate responses that are disrespectful, inappropriate, or insulting to any group.

Conversational AI tools are trained using large datasets of human conversations. Bias and prejudice often seep into these conversations and AI could amplify them. For example, a chatbot could be trained to respond differently to customer service queries based on location or demographics.

Using a reputed tool is vital to minimizing the probability of the tool generating a response that harms your reputation.

Conversational AI use cases

Think the use of conversational AI is limited to customer service? Here’s the truth: conversational AI can boost efficiency across functions — whether it’s customer service, HR, or accounting. Let’s discuss how you can use conversational AI to drive efficiency in multiple areas of business.

Customer service

Customer service is the perfect place to use conversational AI. That’s no surprise — high-growth brands need to offer personalized responses at scale to sustain growth and build a loyal clientele. In fact, 40% of leaders said they expect to realize substantial AI value to improve customer experience.

Even established brands like AirAsia have used a multilingual chatbot to automate customer service to offer faster responses. Our chatbot helped AirAsia handle 75% of interactions without involving a live agent and increased AirAsia’s CSAT from 60% to 90%.

Human resources

The HR workflow has multiple repetitive tasks — like scheduling interviews and responding to employees’ queries — that you can automate with conversational AI. Chatbots can even provide training and development resources to employees and feedback to enable them to improve consistently — essentially putting employee development on autopilot.

Implementing conversational AI in HR can produce massive savings. As Nancy Hague, the Chief People Experience Officer at Automation Anywhere, explains:

IT service management

IT service management teams can automate processes like handling diagnostic dialogues, including FAQs and more complex transactions while ensuring enterprise-grade security. 58% of companies say that their team spends over five hours per week handling repetitive IT requests. 98% of the participants in the study agreed that these repetitive tasks contribute to a higher attrition rate and low employee morale.

Conversational AI can effectively address these concerns. Don’t take our word for it — a recent report from The Hackett Group confirms:

Sales

71% of sales leaders say that teams must make data-driven decisions and personalize sales conversations through digital channels. Excellent, but how exactly do you personalize conversations over a digital channel where you can’t see the customer or play off their reaction?

Here’s the simple answer: segmentation. Conversational AI can auto-create segments and learn to personalize conversations for each customer segment. Conversational AI can also automatically qualify and optimize the lead routing process and nurture leads through personalized messaging.

Want to supercharge your CRM? Integrate conversational AI into your CRM and watch it automatically update customer information, trigger emails, and deliver real-time information to your sales team.

Conversational AI evaluation and metrics

Evaluating conversational AI helps you monitor how much it’s helping your business, or if it’s helping at all. While there are many metrics out there, let’s talk about some vital metrics you should track after implementing conversational AI.

Containment rate

The containment rate measures the percentage of queries that weren’t routed to an agent. The problem with the containment rate is clear: it doesn’t account for chatters leaving the conversation without a resolution.

Some customers might leave the conversation because they were interrupted by another task with a higher priority. However, a good chunk of customers might leave the conversation because the chatbot or voice assistant failed to provide a satisfactory response.

Customer satisfaction (CSAT) score

CSAT tells how satisfied your customers are with your product, service, or support. It’s essentially the percentage of positive responses given by customers in a survey. Unlike the containment rate, CSAT measures resolutions. Measuring CSAT helps you track how effectively your conversational AI tool is at providing satisfactory responses.

Automated Resolution Rate

We created the automation resolution metric to address a key shortcoming of the containment rate : accounting for resolutions. Automated resolution measures two things — it measures whether your bot automatically resolved a query without involving an agent and whether it addressed the reason why your customer used the chatbot in the first place.

Ada’s chatbot selects conversations at random to analyze the chatter’s intent and the bot’s response. The chatbot classifies the conversation as Resolved or Not Resolved based on its analysis. To be considered Resolved, conversations must be:

• Relevant - Ada understood the query and provided a direct, relevant response.
• Accurate - Ada provided accurate information.
• Safe - Ada’s responses were respectful, and the conversation was free from topics that cause danger or harm.
• Contained - Ada resolved the query without involving an agent.

F1 score

The F1 score measures a model’s accuracy. It’s calculated as the harmonic mean of precision and recall:

• Precision: Put simply, precision — or the positive predictive value (PPV) — is a measure of the probability of the model giving a correct answer. So if a model answers “no” 10 times and it’s correct only seven times, the precision is seven out of 10.
• Recall: Recall measures the number of correct answers a model gives out of all the correct answers it could have given. Suppose you have a list of 10 questions. The correct answer for six of them is "yes,” and the correct answer for four of them is “no.” The machine answers “yes” to three questions. The recall in this case is 50% (3 divided by 6).

The F1 score is a great way to understand how often your model is right (precision) and how often it doesn’t miss correct answers (recall). The higher the F1 score, the more accurate your model is at answering questions correctly and not missing correct answers.

Ada has the highest F1 score (93.8) and accuracy score (93.1) in the industry. We use three filters to ensure maximum safety, accuracy, and relevance. These filters allow our tools to understand your customers better and offer quick and helpful responses.

User surveys

Request users to complete a short survey after the conversational AI answers their questions. The feedback will allow you to understand ways to improve your conversational AI toolkit. It can also flag causes of frustration among users such as slow responses or the AI completely misinterpreting the users’ queries.

Ask questions about ease of use, experience using the tool, and overall satisfaction with the generated responses. Offer them the opportunity to provide qualitative feedback with an open-ended question.

Transform CX with conversational AI

Many of your competitors are already using conversational AI to improve CX. Differentiate yourself with an advanced conversational AI tool that allows customers to solve problems faster and more immersively. While businesses race to transform CX with AI solutions, you can stay ahead of the curve with Ada, where our mission is to revolutionize CX with AI.