AI in Customer Services: Definition, Examples, Benefits & Implementation

Businesses train these chatbots according to their product and services. You type out a question and get an answer instantly, or there could be a form that you have to fill, or there could be some options to choose, and more. Now they use natural language processing too, so you don’t even have to type exactly what you are looking for.  

2. Personalized Recommendations to Customers 

You have definitely purchased something from Amazon, so have you noticed it shows you what you’ve already bought. The same thing happens in quick-delivery apps too. They knew about your purchase pattern so well, because AI is running in the background.  

This also happens in customer service chats too, where suggestions get shaped around what you've done before, not some generic list. So, if you're chatting about a return, it might also point out a similar item that fits better or reminds you about a related product you bought last year that could use a refill or upgrade. It's not just sales either; it can help support agents know what to expect before they even start typing a reply. 

3. Email and Response Automation 

Companies get a ton of emails every day, and for one person sorting these is a tough process. Here AI helps, it can read, understand what the email is about, and either send a reply or send it to the team.  

Like if someone asks about their refund, AI can check the system and share the details. But if someone asks three problems in one single email, then it is difficult to put it in a single category. So, AI will sent it to the team for a manual response.  

4. Sentiment Analysis 

In queries, AI tries to understand what the user is feeling based on what they wrote or said in the message. It can even transcript the calls whether it’s positive, negative, or neutral. For example, if someone says, ‘this is the fourth time, I am asking for a solution,’ the system can understand the frustration. The support teams also prioritize an upset customer before it becomes a bad review.  

5. Customer Service Analytics 

With all the support data a company collects, AI recognizes patterns from it. Pattern like how long a customer have to wait before someone responds, how long it takes to solve the problem, how many customers are having same problem, and which agent is handling most cases.  

No, manually filling data into a spreadsheet and looking for spot trends. The goal is to turn raw numbers into a pattern that a manager can check and act on.  

Also read: How Big Data and Customer Experience Improve Engagement 

Examples of AI in Customer Service 

Below are some of the real-world examples that show how AI is used in different industries for different tasks. 

1. AI Chatbots: H&M uses AI chatbots to help online shoppers find products, check their size, and track orders through a conversational interface on their website and app. 
2. Virtual Assistants: Vanguard and other financial firms use virtual assistants to help customers check their account balances, review investments, and get answers to policy questions. 
3. NLP-Powered Support: Spotify uses NLP to interpret customer complaints and questions written in casual, everyday language. This is for accurate and relevant responses.  
4. Sentiment Analysis: Delta Air Lines monitor social media and support chats for signs of frustration during flight delays or cancellations. Helps the staff step in before issues escalate publicly. 
5. Recommendation Systems: Netflix suggests shows and movies based on viewing history, and to guide customer support toward likely account or streaming issues based on usage patterns. 
6. Voice AI / IVR: Insurance providers like ICICI Lombard, voice bots to let customers file simple claims or get policy information by speaking naturally instead of navigating a phone menu. 

Also read: How to Learn Artificial Intelligence and Machine Learning 

Conclusion 

The AI in customer services is not only about taking queries from customers. It also helps businesses understand the pattern in customer interactions and improve overall experience.

However, AI is not a replacement for human support. Artificial intelligence can help a team but not work as complete, especially for complex tasks. Businesses are using AI for repetitive tasks but keeping human intact for queries that need empathy and judgment call.

With the improvement of technology, the gap between ‘AI solve it’ and ‘a person solves’ will keep shrinking. But the best customer service setups are the ones that include both AI and human agents.

If you want to learn AI, and don’t know which AI domain you should learn. Book a consultation call with upGrad experts for one-on-one call and personalized recommendations.