Artificial Intelligence isn’t a thing of science fiction anymore. It is in every industry, seeking to revolutionize things and bring out top-level efficiency at everything- from process to manufacturing.
Not long ago, AI wasn’t considered at the level of human consciousness. But with time, and extensive research, trial and error of AI models, and testing of AI at larger stages have brought about an era where AI is everywhere.
You might also be an AI enthusiast or looking to build your AI model. Well, once you’ve decided what AI model, you’ll build to address a problem, the only thing left for you to do is engage yourself with Java developers. You’d need to leverage the best Java development services around you to build the best quality AI programs and models!
With all the buzz around AI, it is easy to get lost in chaos. And for this exact reason, we’ve compiled top 10 detailed artificial intelligence case studies in the year 2024 to get an insight into how exactly AI is helping people map out new courses in business, innovation, and healthcare, all in real time.
![10 Detailed AI Case Studies [2024]](https://bosctechlabs.com/wp-content/uploads/2024/07/10-Detailed-AI-Case-Studies-2024-.png)
Challenge: Proteins have unique and complex 3D structures made of amino acid sequences that aren’t easy to determine. There are about 200 million proteins that exist in the world. It is quite crucial to understand protein structures to understand and decode things like diseases and patterns on a molecular level- but biological structural complexity is a hurdle.
AI Solution: AlphaFold, developed by Google DeepMind, is trained on the vast datasets of already-known protein structures. It works by assessing the distances and angles between amino acids to predict protein folding patterns; thus, reducing the time factor required concerning computational biology.
Impact:
Challenge: Managing a global supply chain is no simple feat. From predicting product demands, managing the inventory, and streamlining shipping and delivery, to managing blocks in the supply chain. The main challenge for Amazon was to meet customer demands with reduced prices while managing its massive inventory.
Solution: Amazon is currently employing AI algorithms for predictive inventory management. It works by assessing changes in the market, buying trends, and more to predict product demand. The best part about this AI is that it can adjust to real-time market changes.
Impact:
Challenge: In the Healthcare Industry, one of the biggest challenges is the vast amount of patient data, reports, registrations, appointments, diagnoses, etc. Analyzing and organizing this data will be useful to predict disease progression as well as personalize treatment plans.
Solution: IBM Watson uses cognitive computing to analyze large volumes of medical records, patient data, research papers, and more. By using NLP to process medical jargon and present useful structured data to help healthcare professionals in treating patients.
Impact:
Challenges: The fashion industry is quite fast-paced, and trends change within weeks! At such a pace, it becomes quite important to manage inventory, keep track of trends, and more. The challenge is to deliver on changing trends and avoid overstocking.
Solution: Zara now uses AI algorithms to analyze their sales data, current fashion trends, sift through customer preferences, and more. This helps in understanding customer preferences and stocking popular items quickly, as well as reducing unsold products at outlets.
Impact:
Challenge: While we may not realize it, the streaming industry has become far more vicious. Personalized entertainment is perhaps one of the best ways to retain existing customers. Here’s where AI comes in.
Solution: Netflix employs AI to drive recommendations suited to ratings, individual viewing habits, and preferences. This tactic helps in keeping users engaged for a longer period, thus enhancing the overall viewing experience.
Impact:
Challenge: Legal document analysis is a time-consuming and exhaustive process and may also be prone to errors. JP Morgan, with its extensive list of clientele, couldn’t afford to waste time over this. So, they chose the easier and possibly more efficient route- AI.
Solution: COIN, short for Contract Intelligence, is an AI employed currently by JP Morgan that analyzes and interprets legal data accurately and efficiently. It uses NLP to extract relevant information from documents, significantly cutting down on the time required for document review.
Impact:
Challenges: Disabled people often face challenges in using technology. Microsoft resorted to creating AI tools to address this issue- particularly for people with visual, hearing, or cognitive disabilities.
Solution: Seeing AI, by Microsoft, helps visually impaired people to realize their surroundings by resorting to vividly describing them. The range of tools that Microsoft created for this issue included voice recognition applications, visual assistance, cognitive support, and more.
Impact:
Challenges: Traffic congestion in many urban cities has spiked after the pandemic. And while this may seem miniscule, it has many environmental effects as well as leads to movement issues in the city. Efficient traffic management is always the need of the hour.
Solution: City Brain, developed by AliBaba, leverages AI to assess real-time traffic data from GPS systems, traffic cameras, and sensors. This data is then processed and used to predict traffic patterns and optimize the timing of traffic lights, thus reducing congestion.
Impact:
Challenges: Recruiting suitable patients for clinical trials is quite a slow and wearisome process, and this slow approach often negatively affects medical research. The process involves going through humongous amounts of patient data and assessing their history to determine if they can be recruited for clinical trials.
If AI is leveraged for the same, it can help go through the vast amounts of patient data to identify the eligible candidates for the same in a time-efficient manner.
Solution: Deep 6 AI can sift through extensive medical data to locate potential trial participants depending on the given criteria. The AI can go through doctor’s notes, and vast amounts of structured and unstructured data, to find prospective matches for clinical trials, thus revolutionizing the world of healthcare clinical trials.
Impact:
Challenges: The gaming industry is rapidly evolving, but so are the desires of the people who wish for more realism and performance games. Gaming graphics enhancement is challenging, but who is better than AI to give gamers what they want?
Solutions: NVIDIA’s graphic processing technologies, driven by AI (like Deep Learning Super Sampling (DLSS)) can provide extremely detailed and highly realistic gaming graphics. These technologies use AI to render graphics much more efficiently and do not compromise the games’ visual appeal either.
As of today, they’ve set out to create new standards in gaming graphics, keeping the spirit of gamers alive and raging.
Impact:
The above case studies are barely a glimpse into the vast world of how AI can engage and revolutionize various industries. The key lies in identifying a conflict, a challenge, or a problem. The solution usually lies in tackling the said challenge from different perspectives, and figuring out how AI can make it more efficient, swifter, or cheaper.
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