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October 25, 2022

Top 10 Best Use Cases for AI

AI gets a lot of hype these days, but what is it actually used for? We try to answer that question by providing this list covering the best use cases for AI:

Automatic Support Ticket Tagging

Classifying and tagging support tickets is extremely important in any form of customer support because it provides a detailed picture of why customers are contacting you. This is where you'll see what can be done to permanently solve problems that recur regularly, find areas that your support team struggles with and need more training in, or where it would rather be wise to prioritize the creation of self-service services that allow your customers to help themselves. However, a common problem with classification is that it is time-consuming for both the support staff and the customer to have to categorize each ticket. Therefore, the categorization that takes place manually is often unreliable and lacking in scope. Recent developments in machine learning and AI easily solve this challenge. With automatic ticket tagging, AI does the work for you. This means that all tickets are categorized in a consistent manner immediately when they arrive.

Document Classification

Using AI for document classification means that you train Natural Language Processing (NLP) models to go through sections or your entire documents and assign appropriate categories to each document or section. This can be used to improve the internal processes but can also an important step in certifying external requirements such as ISO standards or other regulatory requirements. By structuring the data that lives in your PDF:s and other documents, you can make them much easier to explore when looking for specific information across large archives.

Automated Product Tagging

If you are a retailer and have lots of products in your catalog, you might find that you spend huge amounts of time and resources on tagging new products correctly. Your subcontractors might be supplying you with some type of product tag, but seldomly these make sense in your taxonomy. AI canhelp you automate this tedious process. Integrate AI models into your retail management system in order to automatically tag new products within the correct taxonomy. You can also run your model on all historical products in order to find products that are incorrectly tagged today.

Chat and Forum Moderation

Unfortunatley free text can bring out the worst in people. Toxic and anti social behaviour is common on many forums and chat spaces. This is a major issue as it lowers the experience for everyone and can also lead be a cause of bullying and mental health problems. Therefore many forums, chats and other social spaces need some type of moderation. Custom and multilingual NLP models is a great tool for this in order to filter through large amounts of texts in many languages instantly and remove content that is not wanted.

Social listening

Are you wondering what your customers are saying about you on social media. Scrape your social channels and train an that can tag each mention in different categories without you having to read through it all. Example categories can be "Ambassador/Dectractor" or "Talks about product X, Talks about ad campaign Y". Using this information, you can take better decisions and prevent angry customers from causing damage to your reputation.

Perform CSAT Analysis

Customer Satisfaction Score (CSAT) is a way to measure the customer experience and shows the percentage of customers who are satisfied. Apart from this, customer surveys often contain free text fields where you want to capture special customer wishes that cannot be accommodated in a scoring or yes/no question. By training an AI model to read these free text fields to find, and classify, the various requests, you build an automatic pipeline for your CSAT analysis. This AI model can then analyze incoming new customer surveys and work completely automatically.

Identifying Customer Sales Stage

A buying journey is the process your customer goes through from when a need arises to the purchase itself, any changes and questions regarding orders and delivery. The buying journey can then continue into a deeper relationship. The process exists in both B2B and B2C, the difference often lies in how complicated it is, how much time is spent on it, and how many people are involved in it.When a customer contacts the support team to ask for information about a product, this usually indicates an interest in buying that product. Smart AI tools can identify the sales trigger and label the ticket for quick routing to the right channel. By automatically identifying such tickets, your company can increase conversion rates and decrease returns significantly.

Identification of Frustrated Customers

An unavoidable part of selling to customers is dissatisfaction and complaints. It is important to handle these as quickly and efficiently as possible. When customers are dissatisfied with their experiences with the company, they often tell those around them about it. Also online. DSAT is an abbreviation for Customer Dissatisfaction. It is usually used as a percentage of customers who are dissatisfied and the metric is actually the opposite of the CSAT score (or customer satisfaction score). A smart AI model can help you flag frustrated, angry or dissatisfied customers and thereby create an automatic pipeline for your DSAT analysis. Your model will learn from new cases over time. It will know how your customers usually react to thereby find deviations that point to anger or frustration.

Support Ticket Routing

The first step in handling customer support inquiries (tickets) is to assign these requests to relevant agents (routing). Instead of leaving it up to the customer or a human agent to make the allocation, a method based on smart classification using AI and automation is recommended instead. By predetermining the criteria (labels), automated ticket routing helps you efficiently assign the requests to agents with the right expertise and knowledge. With an automated ticket routing, your company gets shorter response times, higher operational efficiency and likely better customer satisfaction.

Find Sales Leads

Some customer service employees are terrific at identifying customers who need more products or services, even when the conversation or chat has started in a negative tone. An AI model can learn to find the same pattern as the human brain – only much faster! For example, AI can identify buying signals based on customer conversations and customer reviews. This provides the opportunity to effectively assign the inquiries to the right agents who have the expertise and knowledge to elevate the case to more sales – and a happier customer!


You're in luck! All of the above use cases can be accomodated by training AI models using the Labelf AI platform. And the best thing is that you don't need any data science knowledge or programming skills at all to get started. Sign up and try it yourself for free at

The Labelf Team

Labelf team

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