AI in Customer Experience

1. Hyper-Personalizing Experiences at Scale

The vast majority of consumers – 80%, according to a 2024 Deloitte study – prefer brands that offer personalized experiences and report spending 50% more with them. And they feel happier, too, with a 2024 SAP Emarsys report noting that 64% of U.S. shoppers say AI has improved their retail experiences.

Additionally, it benefits businesses as well. The same Deloitte study found that 80% of consumers are likely to make another purchase from a brand after experiencing a personalized shopping experience.

There’s still plenty to achieve. The 2024 Deloitte study highlights a major perception gap: while 92% of retailers believe they effectively offer personalized experiences, only 48% of consumers agree.

Enter personalization at scale: an AI-powered system that continuously analyzes swaths of real-time customer data ingested from multiple channels. It reveals patterns in each individual’s behavior and identifies their preferences and needs to power hyper-personalized experiences, such as:

Personalized product/service recommendations for cross-selling and upselling
Tailored experiences based on real-time behavior predictions and look-alike models
Custom content feeds
Targeted marketing communications
Next best action recommendations
Rewards and recognition based on individual customer information

With AI, you can finally serve every customer as a micro-segment of one. As Oleksandr Trofimov, Chief Technology Officer at unicrew, puts it, “Thanks to its soft and multi-point touch, AI has been a game-changer for customer experience. Customers don’t appreciate brands’ intrusive chasing, and generative AI is capable of making customer engagement even more precise and natural than a human could.”

However, introducing an AI personalization at scale solution requires unifying all customer data by connecting its sources and ensuring real-time data ingestion. You need to get it done before introducing real-time segmentation, customer journey analytics, or AI at scale.

Who Does It Right?

Starbucks has its own AI platform, dubbed Deep Brew, that continuously analyzes customer data to tailor experiences to each individual. Besides personalized marketing messages, the system can also pinpoint the most suitable menu suggestions, offers, and rewards for each customer.

The Deep Brew system optimizes the customer journey based on both in-app and in-store interactions, increasing both customer engagement and the number of active Starbucks Rewards program members.

Calm, a meditating and mindfulness app, turned to Amazon Personalize to tailor content recommendations to users’ preferences. The recommendations engine was first put into place for Sleep Stories and then scaled to the rest of the content. It generates batch recommendations for every user once per two days.

Introducing personalized recommendations allowed Calm to increase daily user engagement by 3.4%.

2. Enabling Around-the-Clock Assistance

Executives consider customer self-service the most impactful AI use case, according to a recent CMSWire survey. 53% of them expect the technology to transform self-service at their organization, up from 45% the year before.

Oleksandr Trofimov agrees that this use case is another game-changer for businesses: “Another significant advantage of AI in customer experience is its scalability. In the past, many executives faced the issue of their processes being unable to address business growth. However, with AI, solutions can be scaled to quickly and efficiently address growing customer requests.”

Furthermore, according to 2024 data from Kaizo, 61% of customers would choose the prompt responses of AI over waiting for a human agent. A 2025 report from YourGPT also notes that 74% of customers prefer chatbots for simple questions.

With the help of generative AI, chatbots can respond to a wide range of customer requests in natural language. They can answer questions, help book flights, resolve simple claims, and more.

But 24/7 AI customer support isn’t the only AI application here. AI-powered chatbots can become personal finance management advisors or shopping assistants. The latter can even evolve into conversational commerce where users make purchases after a conversation with a chatbot.

Conversational commerce is already catching on. A 2025 report from ElectroIQ found that 36% of consumers in Vietnam, for example, had made a purchase directly via chat.

Who Does It Right?

Snaplore, AI-powered tool for knowledge management, empowers small agile teams to streamline knowledge management with a custom virtual assistant. This Snaplore Bot autonomously participates in meetings, transcribing and summarizing them to produce easy-to-navigate documentation with minimal human involvement.

Decathlon UK faced an unprecedented surge in customer inquiries in 2020. To handle it, the company turned to Heyday, a conversational AI platform. With its help, Decathlon created a digital assistant capable of discerning and handling 1,000+ distinct intentions in one-on-one conversations on Facebook Messenger.

96% of Decathlon customers reported being very satisfied with the digital assistant. The sporting goods retailer also saw a 55% increase in automated query resolution.

3. Anticipating Customer Needs & Catching Issues Early On

Neglecting customer churn is a costly decision, as it can result in missing out on a 25% to 95% increase in the company’s profits. That’s the impact of increasing retention by just 5%.

AI, through predictive analytics and modeling, can help you reap the benefits of improved customer retention.

An AI system can continuously track the likelihood of a customer churning or detracting based on real-time behavior data. In addition to traditional behavioral metrics, AI (in the form of NLP) can analyze unstructured text data to pinpoint the expressed sentiment in support tickets or social media posts.

Once a customer is identified as a churn or detractor risk, a personalization engine can pinpoint the right incentive for them to stay. An AI system can also alert customer support agents about the risk, prompting them to reach out to the customer personally.

Who Does It Right?

Buildertrend, a construction project management software, used Churnkey to develop and deploy a custom retention model. This model continuously assesses churn risk and flags at-risk accounts to be targeted by tailored marketing messages later.

The predictive model allowed the company to achieve a 45% reactivation rate for customers initiating cancellation.

PayPal turned to H2O to become better equipped at identifying and reducing churn. The H2O solution integrated with R and Python allowed the company to run a variety of models on the entire customer base. Combined with Hadoop, H2O also enabled PayPal to set up a predictive modeling factory to speed up the development and deployment of new models.

As a result, the company can identify churn risk and its root causes faster and with greater accuracy.

4. Enhancing Real-Time Interactions

Over 60% of customers are ready to switch brands after a single instance of negative customer service experience. Long response times, lack of personal touch, and overly complicated resolution paths can all make for a bad experience – and cost you a customer.

Customer support agents already consider AI a valuable tool. According to a 2024 study from Intercom, 68% of support teams observe that AI is directly influencing customer expectations. Furthermore, 56% of support teams are “considerably more optimistic” about AI than in the previous year. A recent study done by MIT and Stanford University, in turn, found that generative AI tools increase agents’ productivity by 14%, on average.

As a customer support copilot, an AI system can:

Automatically transcribe calls to preserve their content for future reference
Provide personalized answers and suggestions in real time
Summarize support tickets and identify key issue(s)
Detect sentiment and tailor responses based on it
Classify queries and route them based on urgency, language, and intent

On top of that, generative AI can create personalized messages en masse and in real time, allowing for customized updates on, for example, flight delays.

Who Does It Right?

United is a prime case study in leveraging generative AI for scalability. It uses technology to send more detailed texts to customers when flights are delayed. As an average of 5,000 flights a day get delayed, crafting each message would be too much for human staff to handle.

Alaska Airlines, on the other hand, turned to AI to triage customer inquiries more efficiently. Its AI system summarizes every email to identify the key issues and prioritizes inquiries by urgency.

Liberty London, a UK-based premium department store retailer, leverages ZenDesk AI to categorize and triage support tickets. The solution uses AI (in particular, natural language processing) to identify ticket intent, sentiment, and language. Implementing AI customer support allowed Liberty London to cut down ticket resolution time by 11% and first reply time – by 73%.

5. Unifying Experiences Across Channels

62% of consumers want to engage with brands across multiple digital channels. Still, lack of internal data sharing remains a problem: 77% of consumers wish they could stop repeating themselves when reaching out to brands.

Omnichannel experiences aren’t limited only to mobile apps and social media: in-person visits and phone calls remain a part of the equation. For instance, customers are twice as likely to visit a business in person to resolve a complex/nuanced problem (13%) compared to a general issue (6%). They’re also almost twice as likely to call customer support in such cases (29% vs 16%). A 2024 study from the Qualtrics XM Institute confirms that consumers strongly prefer human channels, like phone or in-person support, for more complex issues.

Leveraging AI for customer engagement across multiple channels can mean:

Continuously analyzing customer data from multiple sources to gain full-picture insights into every individual customer
Personalizing communications across all channels
Transcribing and summarizing calls to make that information available in the future
Offering intelligent chatbot support for run-of-the-mill queries to reduce the strain on customer support agents

A 360-degree customer view is vital for customer support agents. According to Zendesk’s 2025 CX Trends Report, 57% of customers would switch to a competitor after a single bad customer experience – a common result of agents lacking a unified customer view.

Who Does It Right?

Ulta Beauty, a U.S. beauty retailer, implemented the SAS Customer Intelligence 360 to unify its online and in-store customer experiences. The platform allowed the company to centralize all of its first-party data in one place and gain insights into its customers.

At the same time, the platform’s AI capabilities allowed the company to create personalized marketing campaigns with targeted product suggestions at scale.

As a result of the platform’s implementation, Ulta Beauty managed to reach and maintain a 95% sales penetration rate.

After a surge in phone calls, TGH Urgent Care, a U.S. urgent care center, decided to tackle the challenge with LivePerson’s help. With response rates dropping to as low as 20% on inbound calls, TGH Urgent Care used LivePerson’s solutions to deflect certain calls to text messaging. It also implemented an FAQ AI chatbot to handle inquiries around the clock.

This multi-channel approach to customer service resulted in a 40% decline in inbound calls and an 80% rise in call response rates.

6. Streamlining Feedback Collection & Analysis

Consumers want brands to listen to them, so much so that a 2024 study from Deloitte found that 80% of consumers prefer personalized experiences and reported spending 50% more with those brands. However, easily quantifiable data like a rating on a scale from one to five can only take you that far in understanding what-annoys or delights your customers.

AI, in the form of large language models (LLMs) and sentiment analysis, can come to your rescue. AI-powered systems can:

Identify sentiment and classify opinions as negative, positive, or neutral
Detect the mentioned CX issues, classify them, and alert customer support agents about them
Track customer opinions on multiple platforms (e.g., review platforms, social media) in real time
Assess the risk of churn or detraction based on the sentiment
Identify patterns in customer opinions in real time to catch trends early on

Furthermore, generative AI chatbots can proactively ask for feedback in natural language form. The LLM can then analyze the unstructured data to gauge the sentiment and track the overall satisfaction rates enterprise-wide in real time.

Who Does It Right?

Whataburger, a U.S. fast food restaurant chain, decided to switch its outdated approach to review analysis: counting the frequency of specific words. To enable more advanced sentiment analytics, the company opted for a GPT model and Dataiku.

The new LLM model enabled Whataburger to aggregate analytics on over 10,000 new reviews incoming weekly from three different platforms in a single, high-visibility dashboard.

Mercedes-Benz used Brand24, a social listening tool, to monitor the sentiment toward its marketing campaign for a new car model in Poland. The tool filtered out entries written by non-Polish users and collected the brand’s mentions in real time.

7. Safeguarding Customer Data & Preventing Fraud

Consumers are growing concerned about their data security. A 2025 Usercentrics report found that 92% of Americans are concerned about their privacy online. Over half of customers – in fact, 70%, according to a 2024 Vercara report – would be willing to leave a brand behind if their personal information were exposed during a data breach.

At the same time, fraud remains a source of anxiety, with a 2024 report from AARP finding that four-in-five (82%) U.S. consumers have experienced some type of fraud this year – and it leads to hundreds of billions of dollars in losses every year.

In cybersecurity, AI can automate certain tasks, such as:

Detecting anomalies that could be a sign of cyberattacks using data across multiple sources and devices
Analyzing swaths of data during incident response and delivering insights in natural language using generative AI
Identifying patterns in sign-in behaviors to detect suspicious login activity
Analyzing cybersecurity metrics to produce recommendations and insights on improving security for the in-house cybersecurity team

As for fraud prevention, it is already a common use case for AI in fraud-prone industries like banking and finance. AI systems continuously monitor transaction data, identify suspicious and anomalous activity, and flag it for further review or trigger an additional authorization stage.

Who Does It Right?

PayPal is among the pioneers in using deep learning models to combat fraud. These models, promptly developed, updated, and deployed in-house, allow the company to consistently stay one step ahead of the latest fraudulent schemes.

PayPal’s AI-driven approach to fraud prevention helps it maintain a fraud rate of 0.17% (significantly lower than the industry average of 1.86%) and block $500 million in fraud per quarter.

American Express turned to NVIDIA’s GPU computing platform to speed up fraud detection in credit card transactions. With the help of advanced LSTM models, American Express’s system met the two-millisecond latency requirement and delivered a 50X improvement over a CPU-based configuration, improving fraud detection accuracy in specific segments.

Conclusion

From hyper-personalizing interactions at scale and across channels to preventing churn and detraction, AI is already a key ingredient in the recipe for outstanding CX.

However, if you decide to invest in AI-powered CX solutions, whether they’re bespoke or off-the-shelf, revisit your data management first. Any AI solution is incompatible with data silos. Ergo, you’ll have to ensure your organization’s handling of data across sources is mature enough for AI implementation.

Ready to transform your customers’ experiences with AI? Let us help you find the right solution to your challenges – and ensure ethical AI use. Explore our full-cycle AI integration services to learn more about what we can do for your business.