AI Customer Experience: Transforming How We Interact

Consumers want increasingly personalized interactions with brands and are willing to stop working with a brand after a single negative experience. Businesses that adapt well to this shift in customer values are using new tech solutions like AI to improve the quality of customer experience.

This article will walk you through how your organization can provide better, more uniform service with AI-driven customer experience tech, from an AI CRM to ML-based analytics.

Why is AI in Customer Experience Important?

Customers worldwide want more personalized experiences. According to a 2024 BCG survey, they seek personalisation that can:

• Make the process of brand interaction more enjoyable.
• Make it faster.
• Make it easier.
• Help learn about the relevant products and services the brand provides.
• Most importantly, to find a suitable price point.

The findings of Five9’s 2025 study suggest that 79% of consumers want brands to predict their needs and have a more proactive approach to solving problems. The same study shows 72% are eager to use AI-powered solutions and agentic services if this means solving their issues faster.

AI-based software can help businesses keep up with customer demands in two main ways. AI analytics can predict customer behavior and needs. This improves personalization across the points most relevant to consumers.

AI chatbots, if configured right, can cut down the speed at which customers receive service.

Benefits and Challenges of AI in CX

Using artificial intelligence to improve user experience gives you a great advantage, but it does come with some drawbacks. Let’s look at each side of the coin.

Benefits of Using AI in CX

Here are a few major benefits of implementing AI in user experience:

• Lower overhead. Running an AI chatbot isn’t exactly cheap, but it can’t compare with having a fully staffed CS department. With self-service options, you don’t need as big a customer service department and end up saving on overhead.
• Better customer insights. AI-powered research tools can improve your level of understanding of your customers. Not only by quantitative analysis of large amounts of data, but also by interpreting qualitative data such as customer conversation logs.
• Seamless multichannel interactions. AI-driven CRM software can integrate chatbots across multiple platforms and provide the same level of service, thanks to being connected to the same database. Analysis of customer interactions across the channels makes customer interactions more personalized and relevant.
• Faster self-service. Since AI chatbots don’t require waiting for a human to join the chat, customers will have faster access to this self-service option.

Challenges of Using AI in CX

User experience improvements with AI tech have a few common challenges, too:

• Cost of implementation. Using an LLM for research can be free. But when you need to host an LLM on your servers and train it on your database, the cost can be rather high. The same applies to using ML-based BI tools.
• Possibility of bugs and wrong answers. Training an LLM poorly can lead to it providing wrong answers to customers, which is bad for brand image.
• Loss of human connection. Even though more than half of modern consumers embrace the effectiveness of AI self-service, not all people are fond of it. If your audience leans more towards talking to a human despite it being a less efficient way of receiving service, introducing a chatbot might upset them.

To avoid the latter, do a bit of research with your audience to make sure they would accept the implementation of AI-based customer service options. Also, consider having a button dedicated to adding a CS representative to the conversation in case some users prefer to only deal with a human.

Companies Must Prioritize Brand Visibility in AI CX

As more people are embracing generative AI as a way to get information online, brands should adapt and improve AI visibility on platforms like ChatGPT, Claude, or Perplexity. Here are a few strategies on how to do that.

• Brand consistency across third-party platforms. Many AI platforms use other websites to find brands suitable to mention for a user's question. Get your brand mentioned by high-authority websites, and you’ll appear more on AI.
• Structured data. AIs use structured data to understand the context of the page. Add Schema markup to help AI crawl your pages better.
• AI mentions monitoring. Using an AI brand visibility tool by SE Ranking can help you understand how much your brand pops up in AI search and in what context.

Being visible on AI platforms helps with lead generation and ensures consumers are led to your brand instead of competitors.

8 Ways AI Can Improve Customer Experience

Here are a few ideas on how AI technology can elevate your company’s CX.

Providing an Alternative Self-Service Option

A large part of consumers are now interested in self-service options and prefer to solve the issues they’re having themselves instead of contacting a representative. Those options typically include creating knowledge bases, customer portals, chatbots, and community forums.

AI can provide another way for customers to solve their problems. Traditional chatbots have predetermined conversation routes that users access by clicking on buttons. AI can transform that limiting experience into a human-like conversation, often even with the option of voice inputs.

This doesn’t mean an AI chatbot will be the primary mode of communication between you and the customers, but having an alternative way to communicate helps draw in more clients.

Instant Service

Customers value speed when it comes to receiving service. With average ticket response times being hours, unless you have a fully staffed customer service department and a live chat, you can’t meet most customers’ expectations.

Implementing AI in your chat support solutions effectively reduces wait time to zero. Customers who are fine with receiving support from a trained AI model will receive it instantly. This will likely lead to a higher level of satisfaction in your customer base.

Availability During All Hours

AI being available for customer support round the clock will also help you reduce the number of representatives that have to work the night shift, or not introduce one at all. Even without human employees available to provide support, your company will still be able to help customers after hours with AI.

You’ll need to add a short disclaimer that connecting with a human employee is not possible after a certain time to manage customer expectations.

Streamlining Routine Tasks

Apart from taking on the bulk of customer interactions, AI can help customer support representatives take out the routine from their tasks. For instance, it can be used to analyze CS tickets and categorize them without the need for screening by an employee.

It can also automate updating the CRM based on customer interactions, whether they’re done by AI or by a human. This significantly reduces the time spent on data entry and frees up your employees for more productive tasks.

Real-Time Personalized Recommendations

AI can analyze customer behavior and their purchasing patterns to form personalized recommendations for products and services based on their past actions. This analytical framework can be integrated into both a chatbot, if it’s used for making orders, or into the product suggestion system.

Many tools that offer personalization features can work in real time. This may result in improved user engagement and increased conversion rate.

Predictive Analytics

Tools that use AI can do more than help customers in a customer support chat. They can also do quite a lot of advanced analytical tasks that can propel your business forward.

One of those is predictive analytics. Based on previous sales patterns, predictive analytics can build a picture of how future sales can look. This is extremely useful for planning seasonal inventory or predicting the lifetime value of different customer cohorts.

Of course, like any other analytical approach, it’s restricted to judging from historical data and can’t take new developments in the industry into account.

Sentiment Analysis

As AI can understand human-generated texts well, it’s a uniquely useful tool for qualitative analysis, such as sentiment analytics. It can analyze conversations with customers to find the general sentiment towards your brand.

This can help you understand the level of frustration with your product, and plan necessary changes to your product, services, or brand messaging.

Call Transcriptions

One of the major benefits of AI technology is that it can transcribe audio to text pretty well. In customer support, this feature can help with turning calls with customers into text documents, potentially shaving hours off of employee time.

These transcriptions can then be analyzed to add more data to the CRM and to enrich your understanding of the quality of customer interactions over the phone.

AI in Customer Experience Use Cases

The practical application of AI-driven customer experience is better understood by looking at examples. Let’s look at four that highlight the best use cases of AI in CX.

Amazon

When it comes to large-scale use of AI to drive revenue through product recommendations, there probably is no competition to the online retail giant Amazon. It uses AI to analyze the purchase patterns of each shopper to produce personalised recommendations like these.

• Customers like you bought this. By placing customers into cohorts based on their behavior, Amazon is able to suggest products that fit the cohort.
• Because you viewed this. Thanks to predictive analytics, Amazon can judge how likely a customer is to purchase a product based on their browsing history.
• You might need this soon. Based on customers’ purchasing patterns, Amazon can remind them about recurring purchases of their favorite products.

Netflix

Another example of product recommendations that work well is the video streaming platform Netflix. Thanks to using machine learning to analyze the behavior patterns of millions of its users, it can suggest content not based on reviews of critics, but based on how different cohorts of customers interact with video content.

It also uses insights into customer preferences to present different thumbnails to them. Using this simple marketing trick, Netflix can frame the same show from different angles, presenting the thumbnail that a user is more likely to engage with.

Bank of America

Bank of America launched its virtual assistant, an AI chat called Erica, back in 2018. Since that date, it has helped with over 2 billion queries and is estimated to help customers 2 million times a day.

The assistant helps the bank’s clients with analyzing their financial habits, finding out about the bank’s programs, and doing routine tasks like finding a specific transaction or making a money transfer. For more advanced questions, the company’s employee is always present and can be contacted through chat.

This is a great example of using AI in customer service that companies should look up to. Especially in terms of introducing agentic capabilities.

EdisonOS

Not all examples of successful use of AI in CS are from large organizations. This one is from a smaller edtech company, EdisonOS.

They have implemented an AI in their knowledge base to streamline looking for simple answers. Instead of having to read through several pages of documentation, an AI can provide a short summary from different documents.

This solution improves the quality of self-service and lowers the bar to entry for less advanced users.

Future of AI-Driven Customer Experience

AI is an actively developing technology, both in terms of improving its capabilities and finding new applications for existing tech. It’s hard to know what the AI space will look like in five years, but here are three trends that are likely to emerge in the coming years.

• Raising the bar for CX. Customer expectations are high as they are today, but a wider adoption of AI-based customer service solutions is likely to raise the bar ever higher. In particular, consumers are likely to develop higher standards for the speed of receiving service and the level of personalisation of suggestions.
• Agentic AI. Right now, most organisations let users place orders via the website or a mobile app. With AI getting integrated with customer service, it’s likely that using AI agents will become a popular way of placing orders.
• Potential for legislative control. Despite AI technology being nowhere near its peak of development, many countries are already thinking about limiting it legislatively. The potential limits connected to intellectual property rights are unlikely to touch analytical and CS AI, as those rely on proprietary data. But legislation on workers’ rights might be introduced to limit job loss due to AI implementation.

Keep in touch with industry news to be among the first to catch new trends before they become widespread and capitalize on them.

Conclusion

AI technology can elevate the customer experience your brand provides by expanding analytical capabilities with machine learning and providing fast service with generational AI. It’s not an easy task to implement these well, though.

If you want to try using AI in customer experience at your company, start with ready-made solutions that can integrate AI with your CRM or customer service software. This helps you receive the most benefits of the AI technology without having to invest in developing a proprietary solution.