The Future of AI in Customer Service

How is AI changing customer support? In a recent podcast, Alex Khoroshchak, CoSupport’s AI CEO, shared his insights into how AI-driven platforms are helping companies step up. These tools not only improve customer service but also improve operational efficiency. The knock-on effect can reshape the entire organization.

In this article, we go over the future of AI in customer service. We’ll also look at some examples of AI in customer service and their use cases. Finally, we’ll consider the challenges of implementing these tools and how to overcome them.

Barriers to the Future of AI in Customer Service

Before we look at the latest AI tools in more detail, let’s look at why companies might avoid using them.

Hesitation to Adopt AI-Based Tools

A lot of businesses see AI as a black box on an airplane. They know it works, but they’re not quite sure how. Like the black box on a flight, it’s a tool that they don’t feel they should use unless it’s a last step.

And, we can see why they’re nervous. AI tools like ChatGPT have highlighted some of the potential issues. The most serious of these is that AI tends to hallucinate. This means that when the machine doesn’t know the answer, it makes one up.

When you’re dealing with customers, this can prove damaging. If your bot gives your customers the wrong answer, they’re bound to be angry about it. Worse yet, it won’t be long before they go over to your competitors.

But, how much of a problem is this really? Alex pointed out that humans are more likely to make errors than machines. After all, a human consultant might be having a bad day, or they may misunderstand something the client asks them.

You’re better off with a custom-trained bot because you can teach it to get the answers from your knowledge base and manuals. The bot won’t feel sick or tired and will always give the right answer.

A critical step in smoothing the future of AI in customer service is to overcome this hesitation. As we start to see more successful examples of AI in customer service, we’ll see more companies come on board.

Organizational Shifts

When you start using AI-powered workflows, you’ll need to rethink your:

teams;
processes;
infrastructure.

Chatbots have been around for over a decade, making them old news. Agentic systems are breathing new life into this tech, but they do require buy-in from your employees and structural changes.

And that’s where the problem comes in. Many employees view AI-based tools with suspicion. There’s a lot of concern over whether or not machines will replace human agents. It’s a valid worry. There are a lot of benefits to merging customer service and AI. Agentic systems are a lot closer to delivering a human experience than any others before them. But you can reassure your team. While AI is taking over some roles, it’s creating opportunities in other areas. Team members who are willing to upskill themselves will always find a role.

You can also make the transition smoother by getting your team’s insights beforehand. Let them voice their concerns in a safe space. From there, you need to train them to use the technology properly.

Gameplay

Most of us use generative AI tools for fun. We use open-source products to generate crazy pictures, like putting our cats in a space suit, for example. From there it’s difficult to see how capable the technology actually is.

You need to overcome these issues so that your teams take the technology seriously. Otherwise, you won’t be able to set up new procedures and workflows properly.

Finding the Right Partner

There are a lot of free tools out there. The problem with these is that you get what you pay for. Unless you have significant tech skills, creating a workable bot with these tools is difficult. You risk having a chatbot that’s mediocre at best, or that hallucinates at worst.

In other words, if something can go wrong, it probably will. Which is why it’s so important to work with a company that knows what it’s doing. Hence, CoSupport’s unique AI architecture is patented. The company created its own architecture to eradicate hallucinations and improve performance.

What’s All the Buzz About Agentic Systems?

Let’s think of an analogy. Your traditional chatbot is something like a toddler that you taught using flashcards. If a toddler sees an apple, they’ll know it’s an apple because that’s what you taught them. But what if the picture was a red apple and the one they see is green or yellow? They may not be able to recognize it.

You train the older style of chatbots using historical data. Therefore, if someone presents a problem outside of this, they can’t assist. Agentic systems are more like toddlers you teach using a tablet. If they come across a question they don’t understand, they can look for the answer using several data sources.

Agentic systems are a huge stride forward because they can:

Access real-time data: These systems can search through systems like purchase history, chat logs, and others to provide accurate, context-specific answers.
Integrate with backend infrastructure: You connect these tools so that they can get the information they need to perform.
Perform complex tasks dynamically: These are chatbots that learn as they go along. Therefore, they become more capable over time. They can also understand the context of the query rather than just the words.
Multifunctional performance: Your typical bot can answer simple queries. Agentic systems can automate other functions like analyzing data and executing tasks. They can, for example, process refunds.
Standard operating procedure adherence: You train these systems to follow company policies just like your human agents. Unlike consultants, they’re unlikely to forget a procedure.

Examples of AI in Customer Service

So, where can you find examples of AI performing a support function? If you look, you’ll see them in action everywhere. This could be in the form of a bot for instant chat support or movie recommendations.

Alex mentioned that CoSupport offers three basic levels of support:

CoSupport Customer: This AI-powered chatbot handles queries from clients without needing any human intervention.

CoSupport Agent: This bot assists human consultants by giving them answers to queries clients ask. The team member then delivers the responses in an empathetic manner, maintaining the relationship.

CoSupport BI: This business intelligence solution allows managers to scan through different data sources for custom reports and actionable insights.

Other Examples of AI in Customer Service

Now let’s look at some examples of AI in customer service you’ve probably used.

Amazon

Amazon powers its recommendation engine with AI. The machine suggests products based on your activities on the site. With customer service and AI, it incorporates Alexa to handle simple queries and help with orders, returns, and account issues.

The advantage of these systems is that they decrease response times and give you a highly personalized shopping experience.

Netflix

Netflix uses AI to anticipate your needs. It recommends content and helps you resolve subscription issues. The AI analyzes your viewing history to help you solve streaming issues and find something to watch.

The benefit is that you get almost instantaneous support 24/7. Netflix doesn’t need as many human consultants as a result.

Sephora

Sephora uses AI-powered chatbots and virtual try-on tools. Their Virtual Artist lets you try on their makeup virtually. You simply upload a photo or take a selfie and then choose the shades you like.

There are two benefits to this system. For starters, it reduces the chances of customers returning makeup. It makes it easy for them to choose products they like and increases engagement.

From a business perspective, this gives the bot a lot more information about what products to recommend. It also allows the company to give more personalized recommendations that are likely to result in sales.

Starbucks

The Starbucks app predicts your preferences based on your order history. It makes it easy to order and track your order. You can also query your account or store location. The company benefits from increased customer satisfaction and streamlined order management.

Walmart

Walmart’s conversational AI chatbot helps you navigate the store online, track orders, and reorder items. Its natural language programming is particularly good, meaning that you don’t have to hit the exact keywords. You can even integrate their assistant with your Google Assistant.

Agentic systems streamline operations and reduce the workload for your customer support desk. They can handle repetitive requests like, “Where’s my order?” Better still, they give your clients instant answers, even, where necessary, in different languages.

They can potentially save your company a lot of time and resources by incorporating customer service and AI. Think your help desk’s the only section to benefit? The future of AI in customer service goes beyond real-time help to give valuable insights.

Customer Support as a Gateway to Business Intelligence

One area most people overlook is how AI can help you unlock the hidden potential of customer correspondence. Imagine if you could analyze every customer interaction with your support team or on your website. You could see which products were most sought after and where there were issues.

Now, technically, we could do this for decades. But manually reading through all of this information takes forever. You would need a huge team to get through it in a reasonable timeframe, making it prohibitively expensive.

Now with AI, we have a quick and easy alternative. Tools like CoSupport’s Business Intelligence solutions:

Turn conversations into insights: These systems analyze emails, chats, and social media interactions to identify trends, customer needs, and product feedback. You simply set the parameters and AI creates the report within minutes.
Drive smarter strategies: By processing large volumes of customer data, you can make informed decisions, such as identifying top feature requests or addressing recurring complaints. You can get ahead of issues before they snowball if you regularly scan your customer interactions.

You can ask questions like, “What features do customers request the most?” AI’s ability to scan through swathes of data almost instantly is of great benefit here. You can ask your human consultants to flag issues manually, but what if they miss one?

These tools also help you identify the most pressing concerns your customers have. Say, for example, you develop software and notice people aren’t using all the features. You might think it’s because they don’t need them, but what if they don’t know all about the extra features?

You could ask AI to scan customer interactions to see if that particular feature is mentioned. And that should allow you to see if it’s something that clients don’t think they need or not.

Should You Buy or Build Your AI Solution?

Ten years ago, this wouldn’t even have been a question. You would need to build your own product from scratch and at great expense. This is one barrier to entry that fortunately no longer exists. You can now either train a generic model yourself or partner with a company to create a custom solution.

The Pros and Cons of Using a Generic Model

Today, we have a variety of open-source solutions that allow you to train an AI application. You can, for example, start with a large language model and refine it by training it using your company’s specific data.

Using a generic solution puts you at risk of developing a mediocre application. It may not be able to deal with outlying cases very well. You also have to worry about the safety of the information you put in. If you plug in your customer’s details, there’s a chance that the model might spit them out later when giving a solution to someone.

Without the right technical expertise, you’re navigating a minefield blind. Basically, you’ll only know you’ve hit a problem when it blows up in your face.

The Pros and Cons of Building a Custom Model

Ten years ago, only large companies could afford to go this route. While it’s still slightly more expensive than training a generic version, it’s a lot more affordable today. Besides this, the difference in quality is worthwhile. The model is unlikely to hallucinate because you control all the inputs.

Of course, the model's success in either case depends on the skill of the team training the AI. Your company may have the resources to develop AI, but creating a robust customer support solution is challenging. In some cases, the difficulties outweigh the benefits of building the system from scratch.

One way around this is to work with an experienced team. They have both the expertise and resources to develop a custom solution in a cost-effective and timely manner.

Now, let’s look at how to find that team.

Finding the Right Team to Work With

Now we come to the real meat of this post. How do you find someone to develop a customer support solution for you? This is another challenge to overcome because it can be overwhelming. Everyone wants a piece of the lucrative AI market, so there are hundreds of companies offering to help you.

You need to set aside some time to wade through the clutter. While you’re probably keen to get started as soon as possible, it’s worth doing your research now. That way, you won’t be left trying to scramble to bring a mediocre bot up to scratch.

Here’s what to look for:

An established team: Ideally you’d want a company with over 20 years of experience. But, considering how new this tech is, you’re not going to find that. Instead, look for a company that has a traceable track record and valid reviews.
Innovation: Do you want to work with a company that uses an open-source large language model? If they can do it, you can too. It’s better to look for a firm that works from scratch. CoSupport uses its patented architecture to create robust applications.
Proven expertise in AI and machine learning: They should specialize in conversational AI in particular.
Customization and scalability: Can the company deliver the features you need? Can they handle an increase in volume if necessary?
Strong data security: As an example, CoSupport anonymizes the data it uses to train your model. It also stores your model for training on its own server. This is the kind of data security you’re looking for. Basic encryption is just the beginning.
Payment on implementation: Never pay the full fee upfront. If you do, you don’t have any comeback if something goes wrong. Reputable companies will ask for a deposit to cover the costs of the basic training. They’ll only start charging you a usage fee once the system goes live. This gives you plenty of time to trial the application and tweak it as necessary.
Clear expectations and service standards: You want to get everything in writing so that there’s no room for misunderstandings. Reputable companies will want you to be clear about your expectations so they can meet them.
Good communication: You need a company that keeps you up to date with how things are going. Look at how they treat you when you first reach out. If they’re slow to get back to you, that’s a bad sign overall.
Multi-channel support: You need to make sure that the new assistant can work seamlessly across email, social media, and other online channels.
Continuous learning: You want your app to learn as it goes along. Look for a company that encourages the AI to tweak its performance.
Support and maintenance: What happens if something goes wrong after implementation? What if you need to update your app? If so, you want a company that offers solid after-sales service.
Transparent pricing: Look for a company that’s upfront about their pricing. They should be able to tell you what development will cost after discussing your needs. Also, ask about ongoing charges. Do they charge a flat fee or per answer, the AI gives going forward?

It’s worth remembering that this company is developing a bot that will be an extension of your brand. You want them to be capable and innovative. It’s worth spending more time during the research phase so that you find the right partner.

From there, think about what you want your bot to be able to do. Set up the goals you want to achieve and write down any concerns you have. Be sure to discuss these with your team, so that they know what your expectations are.

When you implement the solution, be sure to put it through its paces. It’s a good idea to periodically review its performance and update it. Your partner should be willing to help with this maintenance as part of the after-sales service.

The Bottom Line

AI isn’t just about improving customer experiences or cutting costs—it’s about enabling enterprise-wide transformation. From agentic systems to BI-driven insights, AI is changing the way we engage with customers and leverage data for strategic growth.

If your organization is considering the future of AI in customer service, the time to act is now. As Alex aptly put it, “Customer service is no longer just a cost center—it’s the battlefield for enterprise success.”

Reach out to CoSupport for your free demo and see how AI can improve service delivery and customer satisfaction.

The Future of AI in Customer Service: Smarter Support Solutions