12 Popular Used Cases of Artificial Intelligence & Machine Learning

By Abhishek Amin May 28, 2021, 10:56:56 AM , In Automation
12 Popular Used Cases of Artificial Intelligence & Machine Learning

Are you looking to build the next-gen business?  That takes more than basic development. It is a solution that works in present and projects the future known as AI-ML: artificial intelligence and machine learning and it is already the backbone for several successful businesses.

The human race is in the middle of an extraordinary revolution led by Artificial Intelligence and Machine Learning powered solutions and innovations. These technologies have been to the core and impacting almost all walks of life.

Let us have a look at some of the best-used cases of AI & ML applications into various systems and industries that are catered to.

12 Used Cases of AI & ML Impacting Multiple Industries


Industry: Media, Entertainment, Shopping

Using AI & ML, a recommendation engine is built within a system that suggests/recommends products, services, information to users based on analysis of data that has been done by the AI and ML. The recommendation can be derived from a variety of factors such as the history/past data of the user and the behavior of the users who are similar to that.

We can log on to a site and it recommends products and services to me based on my taste and previous browsing history. It is that simple!

You may have experienced it too. Not sure? Take a look at the following examples:

  • Ecommerce platforms like Amazon and Flipkart
  • Book sites like Goodreads
  • OTT platforms like Prime and Netflix
  • Online database like IMDb
  • Hospitality sites like MakeMyTrip, Booking.com, etc.
  • On-demand food delivery apps like Zomato, Uber Eats, etc.


Industry: Marketing + Sales + SaaS

A system can develop sales, marketing, and service software that allows systems/businesses to get insights into their customers and their future opportunities. The system of a company can take consideration/use of machine learning in many different ways. Machine learning gives the content marketers a good look through into what the search engines resemble their content with and uses it to assign predictive lead scores to let the sales teams which customers are most ready to purchase their products.


Industry: Fashion + Ecommerce

An Ecommerce or a fashion-related system can consider or use machine learning in order to help the consumers in order to get right-sized clothes and brands to gain helpful insights about their customers. The system can measure a customer’s body inches and uses machine learning to make suggestions or recommendations for the best-fit styles for them. On the back-end, machine learning analyzes all the available data points to provide the clothing businesses lookups into everything from popular styles to average customer measurements.


Industry: Search + Mobile + Social

Gathering the images or pictures from users include pictures of the business they’re reviewing or the service they’re getting in return. AI and ML together can sort out tens of millions of pictures. When a user surfs the internet and looks up a popular restaurant on any social application, pictures/images are sorted into groups such as menus, food, inside, outside, and so on. That makes it simpler for people to search and get the  relevant pictures rather than looking through all of them.


Industry: Automotive + Transportation

Machine learning and Artificial intelligence can also help in building cars that can drive themselves without having a human driver/interface. In order to get this done any system would require artificial intelligence to be taken under consideration. Along with AI, the system will also use machine learning to view/see their surroundings and nearby things and make sense of them and predict how others behave or react. With so many moving elements or variables on the road, an advanced machine learning system is very important in order to have a working model ready.


Industry: Fashion 

Fashion retailers take the help of machine learning in order to determine Customer Lifetime Value. This metric provides an estimate of the net profit a business can get from a specific customer over a period of time. Customer Lifetime Value depicts which customers are likely to go on or continue purchasing the items/products from the system. Once this is analyzed or determined then, the system can have a priority of the high-CLTV users/customers and now can convince them to spare or spend more the next time around. As the retailers can end up losing money on low-CLTV (with things like free shipments or ignored marketing promos), this process of the model will ensure that the system is turning a profit.


Industry: Healthcare

This case can surely help caregivers of the patients in order to predict which patients may get sick so they can intervene earlier for the same in order to save money and potentially the patient’s life. And this can be done using machine learning by analyzing databases of patient data or their information available such as electronic medical records, financial data, and respective patient claims.


Industry: Finance

Normally and traditionally the credit card companies will evaluate the eligibility through an individual’s FICO score and their credit history. But this can be surely an issue or a big-time problem for those users who have no credit history. To bring this to a solution, a system which is can be developed toward any user or any new credit card applicant holder calculates creditworthiness by using a machine learning algorithm that can take into consideration many other points or factors  like the user’s current financial health and habits.


Industry: Marketing

Systems can use artificial intelligence and machine learning to provide more effective and meaningful email marketing campaigns by modified and personalizing the textual or any type of content, as well as adjusting their scheduling, to have a meaningful impact on each recipient who receives the emails.


Industry: Social Networking

Using machine learning to prioritize posts/tweets that are most relevant to each user. Using that model, post/tweets are now ranked with a relevance score (based on what each user engages with most, popular accounts, etc.), then placed atop your feed so you’re more likely to see them.


Industry: Agriculture

Over here the technology uses computer vision and machine learning to get to know the plants in fields. This is especially useful for finding and spotting the  unwanted plants whom we call weed among acres of crops on the field. Systems developed using this technology can also have specific target plants and can spray them with fertilizer or pesticides. This process will be more helpful to farmers rather than spraying the entire farm on which the spray is not required.


Industry: Digital Search Engines

The search queries are broadly divided into mainly two categories now:

  • Voice-based queries
  • Text-based queries

The search engine takes the help of machine learning in a few ways, but the most outstanding is to evaluate which questions and answers are relevant to a user’s search query. When the search engine ranking answers a specific question, the company’s machine learning takes into account carefulness, truthfulness, reusability, and many other qualities in order to always give the “best” response to any and all questions of the users.

Interested and Wish to Know More?

Get a free consultation from our AI & ML engineers if you are looking for something similar.

You can also hire AI & ML engineers from IndiaNIC who are skilled in NumPy, PHP, Python, CNTK, Spacy, TensorFlow, Spark, IBM, Watson, Amazon Lex, and other powerful technologies to do the rest and build high-value technical solutions for you.

We are living in a golden age of artificial intelligence and machine learning. You must have also experienced or visualized the limitless possibilities of this wonderful field. We would like to know about them too. Feel free to share them!!

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