How various industries are using AI/ML for Predictive Analysis?

April 5, 2021 0 Comments

Predictive Analytics uses raw data, structures it with algorithms and brings accurate forecasts for better decision making. It can iron out the time-eating constraints and can deliver fruitful statistics according to the industry-specific requirement. Although almost all sectors have adopted its assistance, there are certain industries where it gets higher oomph in easing tasks.

Manufacturing Industry:

Artificial Intelligence, Machine Learning and Predictive Analytics have a crucial role to play in connecting the loose ends that often lead to unproductive gaps. Equipment health, inventory, safety, operations, workforce and everything else that can collect data, can benefit from predictive analytics. Even better when the statistics are real-time. Whether it is manufacturing a retail product, or an industrial tool, algorithms can make the most of the big data to ease both manufacturing and sales of the output.

Medicine and Pharma:

From initiation to trials, each product that a pharmaceutical company develops has to undergo rigorous procedures. Out of all of this, the hardest part is to determine patient clustering that will respond best to their developed formula. Patients willing to enroll for the trial have different genetic makeup, demographic history and allergies. AI can sort out and structure this available information and create patient clusters optimum for a specific trial. Aside from assisting in trials, AI generated reports are significantly helpful in anticipating market demands for a particular drug in territorial segments. Thus, medicine and pharmaceutical companies can eliminate the value gap by modelling strategies around the AI generated predictive analysis.


From recording patient’s data to evaluating follow-ups, healthcare sector has to tread on ropes in the entire diagnostic and recovery procedure. Algorithms can segregate the information arising from patient’s health record, lab tests, documents and radiographs. It can then create an insight according to the grouping. The medical practitioners can use this insight to drive closer to the possibilities. AI generated predictive analytics is even more helpful for the healthcare organisation, as it can forecast patient revisits, automatically segregate appointments and make some administrative decisions that would have otherwise required unnecessary communication time.


When designed properly, predictive analytics is extremely handy to fix the most difficult jigsaw of the agriculture industry. Which crop to grow, when to grow, are there any possible environmental disturbances, how many fields are ready for sowing/irrigating/harvesting, how well is the genetic modification keeping up with the practical use, how much and which fertilizers will be used, etc. are the vaguest questions. From the broadest spectrum in speculating demands for the perishables, chemicals and tools to the narrowest creek in field management, predictive analytics can be most accurate. Agriculture is indeed one of those industries where timing is everything. Dairy and Animal Husbandry: Similar to agriculture, data driven organised statistics can help farms improve the quality of milk, eggs, etc. along with increasing yields, provide constant monitoring through sensor tags, automatically schedule inseminates, track animal movement, report unusual events and do a lot more.

Food Processing:

Companies putting food on plate largely utilize AI and analytics in supply chain management. These companies can enhance nutrition and quality in their products, smoother the packaging and logistics hassles, customise products to user demands, set adequate prices, and eliminate food-borne diseases with the help of AI.