Ideta blog image

The main 3 ways of using AI for customer service

What’s a Rich Text element?

The rich text element allows you to create and format headings, paragraphs, blockquotes, images, and video all in one place instead of having to add and format them individually. Just double-click and easily create content.

Static and dynamic content editing

A rich text element can be used with static or dynamic content. For static content, just drop it into any page and begin editing. For dynamic content, add a rich text field to any collection and then connect a rich text element to that field in the settings panel. Voila!

How to customize formatting for each rich text

Headings, paragraphs, blockquotes, figures, images, and figure captions can all be styled after a class is added to the rich text element using the "When inside of" nested selector system.

Using AI for customer service has become popular today. Forbes mentioned 15 ways to exploit AI power, but the list can become even longer. Among the big companies that utilize AI for enhancing their customer services, we have great brands, such as Apple, Facebook, Deloitte, Microsoft, Volvo, Kfc.

What is the meaning of using AI for customer service in practice? To answer this question, I clustered the advice of a few AI & innovation gurus like Vala Afshar, Steven Salim, and the expert panel of Forbes, and created three main areas that cover almost any possible application of this technology.

Area 1: assuring speed, comfort, and effectiveness

Calling traditional customer service is frustrating, especially from mobile. After a battery of questions that users have to answer by clumsy pressing the uncomfortable small buttons of their mobile keyboard, an unfriendly voice says: “please, hold on, we are putting you through an agent”.

This is not the end. Once having been put on hold, there is a long waiting. No information is given if it is going a matter of seconds, minutes, or hours.

on hold customer service phone

When an agent becomes finally available, the client is often so stressed and angry after the long call-waiting that becomes aggressive. Their bad mood can stimulate an aggressive counterreaction of the agent, with a consequent unpleasant discussion.

Results: a big loss in terms of time and happiness for any participant to the interaction, and, probably, some loss of business and reputation for the organization.

Using AI for customer service can change that. An AI text chatbot delivers immediate answers in a polite, friendly tone of voice and manages the basilar and more common questions of any customer. No surprise that a good 62% of users welcome the idea of having some bots to concierge them when reaching out to customer services.

Vocal AI can do more than avoiding call-waitings. For example, vocal AI can start a real conversation with a user at the first second of touch and avoid customers having the bad experience to have to fumble at the small keyboards of their mobiles, particularly while they are in situations like standing up on a crowded train or buses.

In short, using AI for customer service provides clients with a better and seamless user experience; and we all know that a better UX creates much more money for organizations in terms of more conversions and less traffic loss.

The evidence? According to the statistics of Truelist, $1 invested in UX results in a return between $2 and $100 (over 100%!), and 63% of people would consider messaging a chatbot to communicate with a business or a brand a positive experience.

How does AI help to speed and make customer service chatbots more efficient?

Coming to the technical details, how can AI enhance customer services? There are at least three ways:

  • AI Chatbots can handle multiple queries at once. They alleviate the pains of today’s busy call centers that get thousands of calls per minute. The time of response drops down significantly and it saves organization’s time.
  • AI can identify the best agent available to address the customer’s need. This means that your customers will get in touch in time zero with the person that is abler to help. Then, with a consequent increase in terms of time-saving and satisfaction.
  • AI allows further functionalities and ways of communication among people through digital channels and apps. Slack, WhatsApp, and other messengers, for example, suggest relevant actions, such as sharing a location or sending a sticker. This has created an utterly new way of communicating and exchanging messages that is typical of social networks.

Area 2: customers profiling and data management

AI needs data to understand customers’ needs and serve them the most appropriate resources. AI bots can drill the relevant data of a customer in two ways:

  • By profiling customers. This means mining thousands and thousands of bits of data that can be collected by tracking customers’ conversations, navigations, and, in general, their behavior.
  • By accessing the CRM data of the organization

Data need then to be processed. According to Vala Afshar, the typical operations that an AI bot does with data are the following ones:

  • Gain real-time insights across all customer contact channels.
  • Optimize agent availability, wait times, and opportunities for proactive service delivery.
  • Automatically escalate and classify cases using sensitivity and domain expertise predictive analytics.
  • Power chatbots to deliver knowledge using automated workflows.
  • Enable field agents to deliver service based on access to CRM data.
  • Deliver personalized services anywhere.
  • Optimize scheduling and routing using complete CRM data.

Remeber that personalization is the strong point. As Afshar writes in his article, AI-Powered Customer Service Needs The Human Touch:

AI allows companies to deliver these smarter, more personalized and predictive experiences that customers have come to expect

Another possible utilization of AI for customer service is biometrical, facial, and vocal recognition of users of a service, emotion analytics and intent prediction.

While users’ recognition through AI shows still problems, intent prediction is nearly safe and already massively used by many platforms. Just to give an example, it is the intent prediction that allows Google to autofill a search query with just a couple of user’s digits.

In customer service, AI intent prediction means to guess the customer’s next step or requirement. An AI bot can perform that by translating customers’ signals (clicks, views, purchases, scrolling, tapping) into predictions that enable the bot to deliver answers even before a customer asks for them, as calling the appropriate agent.

Emotion analytics is related to understand customers’ feelings through analyzing a set of signals that are usually the text- analysis of what customers write on socials and the emoticons or other nonverbal signals they digit.

The data that is collected through the emotion analysis can be used to route customers to the right team on the basis of their moods. If a client is angry, for example, the retention customer team is more appropriate than the sales department. The opposite is with happy and satisfied customers.

Area 3: utilizing Natural Language Understanding

Natural language understanding is the last area I have detected in using AI for customer service. This is a kind of new frontier for artificial intelligence. The goal is to have bots that speak and communicate like humans.

The reader is going to find a short explanation of what natural language processing and understanding is in my posts: A quick and Easy Introduction to Natural Language Processing.

According to Oded Agam and NextLeap Ventures, two members of Forge panel for using AI for customer service, NLU is something that goes further the mere answering a phone call. They wrote:

By using real-time analysis of customer service calls, chats and emails, AI bots can understand the conversation between the customer service representative and the customer. AI can offer ways to improve the customer experience via understanding the customer's level of frustration, the need for escalation and quicker resolution of problems.

Therefore, what is at stake with NLU is, as always, data mining: an AI bot can understand much more than a human operator through listening to a conversation. This is possible because of the possibility for an AI bot to perform cross analyses with other data at a pace that is impossible for humans.

Examples of chatbots using AI for customer service

What are some good examples of using AI for customer service? A first idea could be to use AI bots to enhance chatbot Facebook messenger.

To understand this point, consider that FB messenger is increasingly utilized and integrated into customer service management because of its wide diffusion and popularity.

Thanks to its Facebook chatbot, Sephora has eliminated five steps from the booking process, which led to a much higher booking rate at their stores, and Bud Light has created a chatbot that is able to order and deliver a case of beer in under an hour.

Another idea could be helping customers shop through a conversation with an AI bot on a chat messenger (it is called conversational marketing). This solution has been adopted by H&M, Vine, Funny or Die, Sephora, and other brands that have launched a virtual store on the app Kik that is very popular among teenagers.

While the brands that we have mentioned above show a strong selling attitude, Slack is an example of the traditional way of using AI for customer service. Slack AI bot simply tries to be helpful. Another famous example of this kind of apps is Alexa that goes well far beyond being a mere virtual assistant.


In conclusion, using AI for customer service has become one of the most robust trends of the new millennium because of the power of AI to solve the most annoying issues of traditional call centers, or, to say it simpler, because AI marks a big progress in customer service technologies.

As always, figures give evidence: according to a survey of Tata Consultancy Service, 32% of major companies around the world use AI customer service technologies, with an investment of over 4.5 billion dollars.

Best Articles

Written by
Marco La Rosa

Marco La Rosa is a blogger, writer, content creator, and UX designer. Very interested in new technologies, he wrote Neurocopywriting, a book about neurosciences and their applications to writing and communication‍

Try our chatbot builder for free!