AI in Customer Experience: How Businesses Use it to Increase Engagement And Lower Costs

So, how can businesses move beyond AI buzzwords and truly enhance Customer Experience (CX) with AI-powered solutions?

There are multiple ways to embed AI into your processes and improve the CX. Here, we cover ways to use this technology to your favor, its potential challenges, and real-life success stories.

Assessing AI possibilities to elevate your customer experience? We've got you covered! As your tech sidekick, we’ll help you identify the right areas for AI implementation and craft a clear roadmap.

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Table of contents

• Why and how companies use AI to improve the customer experience 

• Real-life examples of using AI to improve the CX

• Potential challenges of overreliance on AI

• How to use AI in customer experience practices

Why and how companies use AI to improve the customer experience

According to PWC, customers who have a positive experience are more likely to come back or recommend your product or service to others. This can also encourage them to spend more money on your business.

Also, 90% of AI early adopters see a positive ROI on AI tools for customer support agents and AI copilots, shares Zendesk. This is because AI tools can help you streamline processes and get your employees to focus on more strategic work—which can lead to a revenue increase. You also increase your revenue from more strategic organizational staffing.

Additionally, companies can use AI to improve the customers’ experience by:

Delivering hyper-personalized experiences

According to Zendesk’s 2025 CX Trends, 61% of users expect a higher level of personalization with AI. That’s because these systems allow you to collect data, build individual user profiles, cross-reference information, find insights, and offer a unique experience to each customer in seconds. 

Take Netflix as an example. This media streaming platform uses customer data to create a personalized home page for each user. As a result, there aren’t two identical Netflix homepages. This hyper-personalization of content encourages customers to take action and trust the platform. 

You can also use AI to deliver personalized emails, share unique product recommendations in your e-shop, or trigger timely in-app tutorials. 

Engaging users continuously throughout the lifecycle

Your customers will have multiple interactions with your brand over their lifetime. It’s on you to guarantee they have a great experience at each of those touchpoints, AI models allow you to offer exactly what your customers need at the right time.

Let’s say you have a tiered subscription-based solution. You offer a freemium version of the app but for the first 14 days, users get access to a trial of all of your premium features. When it ends, they can choose to convert to a higher plan or stick with the limited free version. 

AI lets you trigger smart in-app messages at times when the user is most likely to convert. For instance, if they use a premium feature twice, you can prompt them with an AI-powered upselling message at the best possible time.

Other examples could be adding a smart knowledge base that changes the feed based on the page the user is on or launching a shopping assistant to help customers find complex products. You can even write context-based AI-generated frequently asked questions or summaries of product reviews.

Offering 24/7 and self-service support

According to Zendesk, customers rate AI support 10% higher than last year, and this was already considered one of their top three ways of solving problems in 2024.

This proves that using AI for customer support is effective and beneficial both for companies and customers. But to avoid this going sideways, move away from the automated chatbot. Instead, try using generative AI chats for customers to have human-like conversations or use smart assistants to turn customer complaints into tickets or tasks.

Making data-based predictions

Spare your customers' time using predictive technology based on collected behavioral data. Think of auto-filling recurrent data, autocorrecting words, or defaulting to predetermined payment information. These simple acts use AI and can help increase your customers’ satisfaction.  

You can also use AI to review historical data and anticipate future customer issues. For example, imagine you’re a smart car manufacturer and collect massive amounts of data from multiple devices. Based on this information, you know that the battery needs maintenance every 100,000 miles. Using AI models to notify the user when they need to bring in the vehicle for service can help increase their satisfaction.

Also, companies that do data engineering right and stream data from multiple devices and tools can use AI to drive insights from multiple sources. For instance, collect all customer data from welcome surveys, in-app interactions, feature requests, IoT devices, and product consumption data. Then, use AI and ML models to organize the information, drive automated insights, and use the information to feed customer profiles and build segments. 

Building purposeful solutions

This is a consequence of using AI for data analysis. By driving insights from data, you can design purpose-built solutions that improve your customers’ life, and therefore, increase their overall experience.

For instance, let’s say that you have an HR tool and you mostly serve the tech industry. Developers are always short on time, so HR processes like having one-on-ones, setting career goals, and giving feedback aren’t always a priority. You see that the tool’s feedback module has a low usage rate, and after conducting research, you identify that your customers don’t have the time to write feedback within the app. So, you build a generative AI tool trained specifically to turn pointers into feedback. 

This example shows how building a solution with a clear purpose can simplify the users’ experience and increase product adoption.

Listening to customers at a scale

Another benefit of using AI to improve the customer experience is collecting customer feedback. This can happen by automating contextual in-app surveys, building a bot to skim reviews’ websites and social media to see what customers say about your product, or conducting a sentiment analysis on your user interviews.

All of these use cases allow you to understand how your customers feel about your product or service without all the manual work. You can then use the information to build solutions and improvements that answer their needs and improve their experience. 

Real-life examples of using AI to improve the CX

Now that we've seen AI's impact on customer experience, let’s take a look at real-world examples of companies that successfully integrated this technology into their processes:

Nissan

Nissan is a renowned car manufacturer that adopted cloud-native data engineering solutions and AI to improve the customer experience. Among these developments, Nissan built a unified data platform to improve car connectivity. This allows customers to have a 360 view of their cars, get over-the-air (OTA) updates, and access autonomous driving (possible thanks to AI and ML models). 

By collecting car data on Snowflake, Nissan’s internal team can keep track of the car’s performance and offer better solutions to car owners. Also, by creating AI and ML models, Nissan now provides predictive analysis, assesses the performance of multiple car parts before building, and shares end-user applications powered by these technologies. This improves the overall car owner experience as Nissan vehicles are now more connected and reliable.

Additionally, Nissan is also using Snowflake’s CortexAI to improve the CX at the dealership. It does so by collecting prompted and unprompted customer feedback, analyzing it, and comparing the Nissan CX to other brands—all with over 87% accuracy. This process now only takes a few hours and gives Nissan workers the confidence to improve its processes and the overall experience at dealerships. 

Cisco

Cisco, the world-famous Unified Communications as a Service (UCaaS) solution, launched a product to improve information security when building and interacting with AI systems. Cisco AI Defense detects security threats in real time, which prevents breaches and ensures that AI applications remain trustworthy.

This product leverages threat intelligence from over tens of billion daily events to prevent employees from putting company data at risk when using AI. It also integrates data from tools like Cisco Talos and other third parties to proactively identify and neutralize security risks.

Additionally, these AI-driven guardrails ensure that AI models operate safely within ethical and legal boundaries, preventing misuse or harmful outcomes.

The stakes are incredibly high: according to Cisco's 2024 AI Readiness Index, only 28% of organizations feel fully equipped to detect and prevent unauthorized AI tampering. This highlights the need for robust security measures to safeguard both AI systems and customer trust.

Cisco’s proactive approach to security translates into a smoother, safer experience for customers, as they can trust the AI systems they use. They can feel confident that these are secure and continuously monitored, reducing the risk of disruptions or breaches that could impact their experience.

IBM watsonx assistant for health

IBM’s watsonx technology allows companies to develop, train, and deploy AI assistants for multiple purposes, including healthcare. IBM watsonx for healthcare processes large amounts of medical data to help oncologists diagnose patients and offer treatment recommendations.

Watsonx uses natural language processing (NLP) to understand patients’ information, analyze their history, and access published data. It also uses machine learning models to identify patterns in the patient’s history and suggest treatment. This has been proven efficient and has accurately treated cancer patients.

By improving diagnosis accuracy and treatment recommendations, IBM’s watsonx technology increases healthcare efficiency and patient satisfaction—which can make a real difference in their lives.

Potential challenges of overreliance on AI

While AI can streamline tasks and enhance the customer experience, overreliance without careful planning can lead to potential challenges affecting performance, trust, and more:

Inaccurate AI outputs

Using AI without fully training it or replacing existing processes with underperforming AI solutions can lead to negative outcomes, such as inaccurate product information. 56% of users find receiving incorrect AI-generated product details very or extremely negative.

Poor performance

While AI can automate processes and provide efficiencies, it lacks the human touch needed for complex or nuanced situations, which can negatively impact customer experience. Additionally, AI systems are only as effective as the data they are trained on, and inaccurate or biased data can lead to flawed predictions and decisions. Overusing AI without human oversight can stifle creativity, reduce flexibility, and ultimately hinder performance and innovation. Balancing AI with human expertise is key to achieving optimal results.

Security and data compliance

Adopting AI systems raises concerns about security, data compliance, and transparency. Customers want clarity on how their data is used, especially if it's used by AI tool for training. They want to know what happens to the data they share in conversations, and what other information systems can access. 

While these challenges are significant, they can be addressed with careful, gradual AI implementation, building trust with both your team and customers for long-term success.

He also explains that your AI features should still allow users to be in control so they can close it and go back to previous processes if needed. 

The same happens if you’re introducing AI into business processes. You need to train your team on these systems and allow them to trust them over time.

Also, launching AI features after both your team and customers tested them in beta and trusting them, reduces security risks because you know exactly how the information is being stored and processed—and can be open to customers about it.

How to use AI in customer experience practices

You have three options to embed AI into your processes and improve the customer experience:

Using an out-of-the-box solution

No-code solutions like Userpilot or Intercom let you add AI features on top of your product. For instance, Intercom allows you to design product walkthroughs and add a smart support chat. You can also use Fin (Intercom’s AI bot) to turn conversations into tickets for your support team to follow.

These tools are easy to set up and use but lack customization. You can edit the font, color, and UI patterns but can’t truly make your design look the same as your brand. Also, these can get expensive with time, e.g., Intercom charges you a fee every time Fin completes an action.

Designing AI from the ground up

Developing AI in-house gives you full control over how it’s built, performs, and integrates with your systems. You can tailor it to your exact needs, ensuring it aligns with your brand, data, and customer workflows. However, this approach comes with challenges:

• Higher upfront costs – You'll need a team of AI specialists, data engineers, and developers, plus infrastructure to train and deploy models.

• Longer time to market – Unlike out-of-the-box solutions, custom AI takes time to develop, test, and refine before it’s production-ready.

• Ongoing maintenance – AI models require continuous monitoring, updates, and retraining to stay effective, adding to long-term costs.

• Scalability concerns – As your business grows, you’ll need to ensure your AI solution can handle increasing demand without performance issues.

If you have the resources and need a truly tailored solution, building in-house can be a strong long-term investment. But for faster implementation, we recommend the NaNLABS Way.

Building AI-powered systems for data-driven growth with NaNLABS

As your tech sidekick, we assist you on your AI journey by integrating, fine-tuning, and deploying AI solutions into your processes. We help you identify the right areas for AI implementation, turn your innovative AI concepts into actionable solutions that deliver value, and make your data AI-ready so you don't miss opportunities and waste resources.

With hands-on experience fine-tuning existing AI models, we help our clients unlock AI's full potential to optimize their operations:

• Generative AI Tool for Reporting: Our team partnered with TGG Accounting to design a generative AI and large language model (LLM) solution that automated 30-page financial reports. This led to a significant reduction in report creation time by developing a custom system to accelerate content generation. After testing, we refined the AI outputs to ensure actionable insights and user trust.

• GenAI Solution for Summarizing Data: We collaborated with HeyMirza to simplify access to childcare subsidies for vulnerable U.S. communities through generative AI. We developed a tool that transformed complex government program documents into clear eligibility criteria and built a user-friendly single-page application with React.js. Using LLMs for criteria extraction enhanced both user experience and internal team efficiency, creating a scalable process for handling intricate documentation.

From predictive insights to automation, we ensure your AI initiatives drive real impact, ensuring fast deployment, controlled costs, and precise solution.

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