15 Use Cases of AI & ML Technology

By Rania Jan 31, 2022, 12:32:30 PM , In Artificial Intelligence
15 Use Cases of AI & ML Technology

Table of Contents

Great customer experiences start with a great add-on experience provided to the customer’s requirement, and that’s why we want to make AI and ML easy to sell to every customer in every requirement coming within IndiaNIC.

By doing so, AI & ML will boost up our service operations, AI & ML can play a key part in driving efficiency across our sales and marketing efforts to enhance customer experience and satisfaction towards their requirement.

AI and ML can be used to power any company’s decision-making process, helping them to make better business predictions. Industries such as healthcare, transportation, banking & finance, retail, Ecommerce, education, manufacturing, etc. can be blessed by the introduction and implementation of AI-ML based solutions.

In this article, we are going to see the 15 used cases of innovative solutions based on Artificial Intelligence and Machine Learning technologies.

Use Cases of Artificial Intelligence & Machine Learning


Application area: Media + Entertainment + Shopping

Example: Using machine learning and artificial intelligence, a recommendation engine is built within a system that suggests/recommends products, services, information to users based on analysis of data which 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 behaviour of the users who are similar to that.


Application area: Search + Mobile + Social

Example: Gathering the images or pictures from users includes 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.


Application area: Automotive + Transportation

Example: 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 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.


Application area: Education

Example: Using data collected from user responses or answers, the system can create or develop a statistical model of how long a person is likely to recall a specific word before needing any type of reminder or refresher. Having that information, the system gets educated when to let the users who might benefit from retaking an old lesson.


Application area: Fashion

Example: 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.


Application area: Healthcare

Example: 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.


Application area: Finance

Example: 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 applicants 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.


Application area: Marketing

Example: Systems can use artificial intelligence and machine learning to provide more effective and meaningful email marketing campaigns by modifying 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.


Application area: Social Media

Example: 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.


Application area: Agriculture

Example: 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.


Application Area: Search

Example: 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 ranks answers to 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.


Application Area: Analytics + Cloud + Consumer Research

Example: Many systems or platforms do use machine learning in order to provide companies with very deeper insight into their own gathered data over a period of time. Machine learning can be used to observe industry trends and at the same time can predict many consumer habits.

13. TRANSPARENCY IN THE CPG(Consumer packaged goods) INDUSTRY

Application Area: Analytics + Retail + Healthcare

Example: System can use machine learning and artificial intelligence to create high-order attributes for retail and consumer packaged goods products. Let’s say a consumer is looking to pick up a few groceries. The system will take machine learning into consideration to provide a personalized view of each food product, which can include ingredients, the supplier, the supply chain history of the supplier, and much more to this, in order to give consumers better insights into their respective purchases.


Application Area: Marketing + Sales + SaaS

Example: 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.


Application Area: Fashion + Ecommerce

Example: 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.

Why IndiaNIC is Your Right AI & ML Application Development Partner?

IndiaNIC is a trusted software development company that has been delivering digital excellence since 1997. Recently recognized amongst the Best Brands of 2021, IndiaNIC has gained a reputation on the world stage and has catered to clients from every corner of the globe.

Team IndiaNIC is the earliest adopter of AI & ML application development services. The in-house AI-ML engineers have proven expertise in Machine Learning (ML), Natural Language Processing (NLP) technologies with tools like TensorFlow, Apache SystemML, Caffe, Apache Mahout, OpenNN, Torch, Neuroph, Mycroft AI, etc. The vision of the team is to deliver next-gen solutions to a wide range of industry domains.

We don’t believe in talking big things at first. Rather we believe in our actions with trust in our successful methodology in developing that great software product. As every software development is having a different approach based on the varying nature of the business requirements, we always start and finish in the following way:

  • Close collaboration with a discovery workshop
  • System analysis & conceptualization
  • Strategic planning
  • Roadmap design and timelines
  • Software development
  • Performance refinements
  • Implementation

The world’s businesses are going smart, is yours? Contact AI & ML experts at IndiaNIC now for end-to-end consultation at no additional cost.