Conversational AI for Customer Service: Our Detailed Guide cover

Conversational AI for Customer Service: Our Detailed Guide

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There’s a lot of buzz around ChatGPT and other AI-powered chatbots these days. Some businesses are even using this new technology to offer superior customer service experiences. But what does conversational AI for customer service look like, and can it work for your business? Does it replace the personalized service that live agents provide?

Artificial intelligence is a powerful tool that has the potential to revolutionize your business, if you know how to use it. In this guide, we’ll dive deep into the inner workings of AI so you can leverage it effectively for your business.

Let’s start with understanding the basics of how this technology works.

Understanding Conversational AI Mechanics

Conversational artificial intelligence can make your customer service stand out against the competition. But in order to fully leverage this technology, you need to understand the basics of how it works. This means understanding a little about input processing, language understanding, and response generation.

Here’s a brief overview of these three concepts in plain English.

Input processing

An AI response works entirely on a customer’s input. As you enter prompts such as text, voice, or video into an AI program, it sorts these into categories for analysis by an algorithm that prepares responses. Developers encode the program to process each specific type of prompt in a specific way. Effective sorting and classification is the foundation on which a good AI program rests.

Language understanding

Whenever the subject of AI comes up, it seems that the term large language models (LLMs) is not far behind. This is the point in the process when LLMs become integral. LLMs use statistics, a neural network of connections, and a training vocabulary of billions of words to predict the next word in a prompt.

Language understanding could easily be an article in and of itself (or even a series of books). The important thing to know is that these algorithms evaluate everything, including the context of a prompt or customer support request, when formulating a response. The best AI programs do this in a matter of seconds, and the response should be quick and spot-on.

Response generation

Once the model processes and analyzes the input data, conversational AI formulates the appropriate response. This response incorporates context, intent, and predefined knowledge bases.

If your team selects an AI tool for customer service purposes you’ll need to “train” it with information on your products and services, so it can provide detailed responses. More on that later. For now just know that AI for customer service can answer questions outright or direct consumers to a qualified staff member who can help. Either way, it saves your business time and money.

Key AI Technologies in Conversational AI

We’ve given a broad overview, but the process is a lot more complicated, of course. Just like your car needs interactions between several pieces of equipment under its hood to run, several key AI technologies must collaborate to create cohesive and context-aware interactions.

These underlying technologies can include natural language processing (NLP), machine learning (ML), and neural networks. Whole teams of technologists focus on each piece of these programs. Let’s take a deeper look at each of the key technologies, starting with NLP.

Natural Language Processing (NLP)

AI systems are quickly becoming adept at understanding and interpreting human language. Through techniques like tokenization, parsing, and sentiment analysis, a natural language processing program can craft just the right response to your queries.

In less than a second after a customer inputs a prompt, a sophisticated AI model assigns an ID number to each piece of the text. Programmers use millions of these bite-sized pieces of information, or tokens, to train the model. The model divides structured data like customer input or records into similar tokens for easier analysis. The AI program will also parse the sentence, analyzing word choice, grammar, and syntax, to derive general meaning.

At this point things become even more sophisticated. Based on this analysis and with additional sentiment analysis, the program categorizes the emotional nature of the prompt. The platform might label the prompt as happy, sad, angry, or something else. This all sets the stage for an appropriate response.

The most well-known tool using this technology, as of this writing, is probably OpenAI’s GPT. You’ve probably seen ChatGPT-generated text; you’ve probably even tried your hand at serving this chatbot a few prompts. As you can imagine the NLP capabilities of this chatbot are very sophisticated, which is probably why it’s so popular.

Machine Learning (ML)

NLP is a subset of AI. Machine learning (ML), is also a subset of AI, and often powers NLP technology.

ML is implemented through coding, but the end result is that computers are able to learn further information without human programming. With machine learning, a computer basically programs itself through experience.

You don’t need to know the technical stuff, but all this means that more experience equals better results. The longer the algorithm is working with your customers, the better it will become at anticipating their specific needs.

Neural Networks

Our third subset of AI is neural networks. Machine learning can be an aspect of natural language processing, but it also works in neural networks. Though the technology may be complicated to explain, today’s neural networks spring from an idea that has been in computer science for over 80 years.

Neural networks consist of thousands of densely packed processing nodes in a formation that’s inspired loosely by the human brain. Researchers train this web of nodes to find patterns from millions of examples. Developers feed data through the network in a single direction for analysis, from bottom to top.

Recurrent neural networks manipulate and analyze this sequential data. Finally, with the help of a piece of technology called a transformer, they spit out a sequential and sensible response. When things are going well, this response should be indiscernible from a real human response.

How does AI technology make conversations realistic?

Tools like ChatGPT are revolutionizing the business landscape and inspiring new conversations about the nature of work. But before we get carried away, are these tools really all that reliable? Customers are essential to your business, so you’ll want to answer that question before committing to using a customer support chatbot.

In this section, we’ll take a look at whether AI is truly trustworthy.

Is Conversational AI trustworthy?

Consider the liabilities involved in using a new piece of technology carefully before signing on. Some factors to consider before using conversational AI in your business include:

  • Reliability
  • Accuracy
  • Biases
  • Data Privacy

We’ll take a deep dive on all of these issues, starting with reliability.


If an AI model is interfacing with your customers, you’ll want to make sure it’s consistently providing accurate and relevant responses to user queries. Not all AI tools are the same. The key factors in this outcome is robust training data and ongoing optimization. Make sure to ask about this process before choosing an AI tool.


Accuracy of conversational AI avoids potential errors in understanding user intent, misinterpretation of queries, and providing incorrect or irrelevant responses. Here again training data and consistent optimization is key. This is particularly true if you have a very technical product or one that is constantly evolving. Your team should update your chatbot periodically and inform it of these changes just as you would any other employee.


Bias is one of the hardest liabilities to detect when testing an AI product. But technology is not created in a vacuum. Increasingly, people are realizing that programs, models, and algorithms operate with implicit bias, and AI is no exception.

For an example of bias in artificial intelligence, consider facial recognition. If an AI model is trained to recognize faces from only one ethnic group, it will not serve all customers equally. This is an example of bias in training data. Further along in the process, the algorithm used to parse user input data may well be programmed with ingrained bias. Indicators like income or vocabulary may unfairly weight responses for certain users.

Each of these biases (and others) could potentially impact the fairness and inclusivity of generated responses. It’s impossible to say how bias may play out in your particular use case, but it could be a dealbreaker for your team. Consider biases before introducing an AI program to your customer service team.

Data Privacy

Data privacy is important in all technology, but AI tools must protect customer data in user data collection, storage, and usage too. Vendors must also ensure compliance with privacy regulations such as Europe’s GDPR and California’s CCPA.

According to the FTC, AI companies are not exempt from the laws on the books regarding data privacy. Third parties using this technology via customer service chatbots are also not exempt. Your team should have a robust security plan in place to safeguard user information before rolling out your AI chatbot.

Advantages of Conversational AI for Customer Service

Now that we’ve covered the basics of artificial intelligence, let’s focus on how AI can help customer service for your business.


For most businesses, scalability is key. Savvy deployment of conversational AI enables businesses to handle a large volume of customer inquiries at once. Without the need for additional customer support agents, your business can scale up quickly without adding considerably to customer support costs.

Best of all, service won’t suffer. You can trust the right conversational AI for customer service to field inquiries, answer questions, and direct website traffic to the right places, all for a low monthly cost.


AI tools often reduce response times, streamline tasks, and enable round-the-clock availability to your customers. In today’s fast-paced marketplace, this will set you apart from competitors.

A chatbot can automate routine tasks and even improve customer engagement and interactions. It enables your business to have faster response times to frequently asked questions.

Probably most importantly, a chatbot frees up your human staff to focus more on mission-critical tasks. The added human power will lead to more complex tasks being completed in a timely manner with fewer distractions.


Conversational AI can actually personalize interactions with customers by leveraging data insights. By looking at individual preferences, purchase history, and browsing behavior, a good chatbot tailors responses to the customer. This often leaves customers with a positive impression of the interaction and may even help your company score a great review. In fact, some customers may not even know they’re dealing with a bot if your team uses the right tool.

Down the road, generative AI models could become so flawless in natural language generation that they could provide product recommendations and support responses that integrate customer information.

As you can see, automation has its advantages. It’s also a reasonable assumption that AI chatbots will have even more complex use cases in the future. But too much automation can send the message that you don’t care about customer service. AI should be used to free up your human support teams, not as the end all and be all fix. In the next section we’ll explore how conversational AI can augment your team, not replace it.

Augmenting, Not Replacing: AI and Human Support Teams

Often, automation is necessary, but sometimes your business could benefit from a human touch. Consider taking a two-pronged approach to customer service by hiring a virtual assistant. Wing can be a good complement to your AI initiatives.

We provide fully-managed, dedicated remote support staff for companies in many different industries. Wing VAs are based all over the globe, fluent in English, and typically have a college degree. Wing VAs are versatile, trained, and overseen by our operations team. You can assign unlimited tasks to your virtual assistant during working hours and cancel your service anytime.

Having a confident, well-trained human associate on the team to complement your AI will also free up your other core staff to do complex work. Your Wing Assistant can provide proactive support in many tasks, including:

  • Customer service
  • Bookkeeping
  • Content moderation
  • E-commerce tasks
  • Web and app development
  • Real estate assistance
  • Graphic design
  • Sales and outreach
  • CSR and reception

Companies of all sizes, from small startups to multinational corporations are using Wing to streamline repetitive tasks that chatbots can’t handle.

Implementing Conversational AI: A Step-by-Step Guide

If you’ve carefully considered the potential downsides to AI and are still committed, you’ll need a roadmap for implementing conversational AI for customer service.

Here are some important steps to cover as you roll out this new technology:

  • Planning
  • Tool selection
  • Training
  • Integration
  • Testing
  • Optimization

Before getting into the weeds on training and optimization, let’s discuss how you can plan to use your AI tool.


Just as with other software tools, begin your search for an AI tool by looking at your team’s pain points.

Are you looking to save time on customer conversations, looking to boost customer satisfaction, or something else? When researching conversational AI platforms, consider features, scalability, integration capabilities, and cost alongside your list of metrics. It can also help to look at what the competition is using and speak with your industry contacts to see which new tools may be on the horizon.

Training Data

Once you’ve found the right tool for your business, it’s time to collect training data for the model. Of course you’ll have to consult the software provider, but typically they benefit from a diverse array of training data, including historical customer interactions, FAQs, and industry-specific terminology. It’s good to have that information on hand to make the rollout process as smooth as possible.

Your vendor should have the infrastructure available to integrate the chosen conversational AI solution with existing customer service infrastructure such as CRM systems and helpdesk software. If not, you should go back to the planning phase and choose another tool.

Ongoing Optimization

Think of AI as a long-term investment in your team. Investments need maintenance and upkeep, and your chatbot is no exception. Unfortunately, AI is not at the stage where you can simply “set it and forget it.” Your team must continually monitor and analyze the conversational AI performance of your AI tools using predefined KPIs. This ensures progress and helps avoid embarrassing PR errors. Check in early and often, especially in the first few months after the rollout.

User Training and Support

Consider offering training and support to the human customer service team to effectively use and manage the conversational AI system.

Compliance and Data Privacy

Throughout the implementation process, your team should comply with the relevant regulations such as GDPR and CCPA. Consult your legal team to make sure you’re doing this right.

Choosing the Right Conversational AI for Customer Service

There are a few factors when considering an AI tool specifically for customer service. Let’s start with customization options.

Customization Options

Any good piece of software should be customizable. Businesses may require tailored solutions to match their unique branding, tone, and specific use cases. Chatbot personas, conversation flows, and UX/UI elements are all features that should be considered.

Integration Capabilities

Integration capabilities offer seamless collaboration with existing systems and tools. If your new chatbot isn’t compatible with your team’s CRM software, helpdesk platforms, messaging apps, or other communication channels, your IT Team is bound to have many headaches. Make sure your IT team has had plenty of opportunities to ask potential AI vendors about API availability, webhook support, and compatibility with third-party applications before choosing a product.


No matter what type of business you’re in, metrics are crucial. AI platforms provide analytics and reporting features, including real-time dashboards, sentiment analysis, and user journey tracking. Some AI metrics worth tracking include: conversation completion rates, user satisfaction scores, and response times.

Compliance Standards

Since these tools will need to interact with the general public, data privacy and compliance is of paramount importance. Any AI tool that is being used for customer service must comply with GDPR, CCPA, and all other industry-specific standards. Anything less is a major liability for your business. Make sure to clear these issues with the sales rep before purchasing a product.

Conclusion: Conversational AI for Customer Service: A Game Changer

Now that you know the pros and cons, you should be well prepared to choose the right conversational AI platform that meets your team’s unique requirements and objectives.

If you’re considering augmenting your AI capabilities with human support, consider hiring a Wing VA as well. Wing Assistant provides reliable, vetted, dedicated virtual assistants for businesses in a variety of industries. To learn how a Wing virtual assistant can help your business, set up an appointment to chat with a Wing Customer Success Manager today.

Wing VAs are trained on popular communication tools like Slack and MS Teams and respond within minutes during working hours. You can also choose a Wing VA with industry-specific software knowledge like Salesforce, Later, and Hootsuite.

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