Artificial intelligence has the potential to completely transform our world and touch every industry and business. AI is currently used in many industries to change how businesses understand and interact with their customers, and to automate and improve former manual business processes. Ever wondered how AI is practically applied? We’ve explained some of the most interesting use cases below.

AI for Customers

AI can allow businesses to better understand who their customers are, predict what customers want

and provide customers with more personalized interactions.


Facebook has an AI engine named FBLearner Flow which provides personalized news feeds and homepages for each user with information it believes they will find interesting. Machine learning algorithms also filter out content users may find offensive or has been identified as fake. Facebook uses neural networks in its DeepFace technology after users upload a photograph to suggest who it thinks is in the picture by parsing data points to measure facial characteristics. The facial recognition algorithms have a reported success rate of 97.35% when used with publicly available test datasets.

American Express/ Mastercard

American Express and Mastercard have built AI systems that are able to read data from credit card transactions around the world in real time. Due to the large volume of transactions that occur every day, American Express leverages distributed storage infrastructure. Mastercard leverages AI to help identify a false decline, which is when a legitimate credit card transaction is declined due to being flagged as potentially fraudulent. Mastercard applies machine learning to the decision scoring system run when a merchant passes the card details to Mastercard’s systems for verification. It works by understanding how a card is used over time, allowing the algorithms to learn what is within normal boundaries of behavior. The Mastercard AI system is 3x more successful in detecting fraud and has a 20x reduction in false positives. Both companies make use of a combination of supervised and unsupervised learning techniques to raise flags when potential fraudulent transactions occur to alert their customers.


Amazon uses deep learning to spot patterns and relationships in Amazon’s transactional and customer behavior data. The deep learning algorithms are used in Amazon’s prediction tools and appear on the website by presenting a user with a personalized shopping experience, such as the “frequently bought together” recommendations. Amazon also uses neural networks in its personal home assistant device Alexa. Neural networks are used to detect when the user is awake and when to start listening for and interpreting a command. As voice commands are processed, it becomes increasingly efficient at understanding nuanced communications.

AI for Services and Products

AI can allow businesses to create more intelligent services and products to offer to their customers.


The streaming music service Spotify creates Discover Weekly playlists, which give users a playlist of new music they will likely enjoy. The playlist is generated by using predictive technology powered by machine learning. Audio analysis is utilized to break down tracks into parts such as the pattern of lyrics, tempo, beat, pitch of the notes, types of instruments and sounds used and natural language processing is also used to create recommendations. A calculation of the probability that a certain user will like a particular track is made based on the user’s listening preferences.


Netflix uses AI to predict which movies and TV shows in its catalog its users are most likely to want to watch. These recommendations pop up immediately after a movie or show completes and when Netflix first loads. Netflix uses deep learning for its recommendation engine to break down content into tags of individual elements and measures how content that fits these tags match with users viewing preferences. Netflix also uses AI to optimize video and audio encoding and uses compression algorithms to minimize the size of files that need to be streamed over the network. In addition, Netflix uses machine learning to better understand what type of content to produce for its original movies and TV shows by learning characteristics that make content successful.


Tesla is using AI to create self-driving cars. Tesla cars are equipped with cameras scanning the road, atmospheric sensors for monitoring weather conditions and steering wheel sensors to understand how drivers use their hands. This data is processed using machine learning algorithms to allow the car to understand what it is seeing and how to react to a situation. Tesla currently uses an autonomous driving system known as Autopilot that allows the car to match speeds to traffic conditions, change lanes, self-park and be summoned from a parking spot.

AI to Automate Processes

AI can improve and help automate business processes.


The global multimedia publishing business Elsevier has an advanced clinical decision support platform named ClinicalPath, which uses natural language processing and machine learning to suggest the optimal treatment pathway for patients. It can correlate data from anonymized patient records and medical insurance claims along with Elsevier’s large archive of published research in its medical journals. It looks at prior cases where patients reported similar symptoms and analyzes outcomes and then suggests treatment for cancer patients. ClinicalPath has a more than 80% on-pathway decision rate across oncology pathways.


Domino’s is using AI to ensure more consistent quality pizzas. The system named Pizza Checker photographs every pizza that leaves the oven and uses machine learning algorithms to inspect for quality before it is delivered. The pizza is checked against the customer’s order to ensure it is the correct order, the toppings are distributed evenly and the crust has been baked at the right temperature. Pizzas are remade before delivered if they do not pass the algorithmic inspection.

Understanding practical examples of how AI is being used in the world will may inspire you to explore the applications and implications of AI for your business. If you’re ready to explore the potential of AI to solve your organization’s needs, Ellumen’s AI experts are here to help. Stayed tuned for forthcoming articles in Ellumen’s blog series exploring topics related to AI innovation, and listen to the AI Innovation Space podcast on Spotify.

Meet Ellumen’s AI Experts

Are you looking to utilize AI for your next technology or integrate AI into your current systems? Feel free to reach out to us.

Iyanuoluwa Odebode, Ph.D. is Ellumen’s artificial intelligence/machine learning expert, supporting the Innovation Lab project by identifying and researching new & emerging technology. He also serves as an adjunct professor in cybersecurity at University of Maryland Baltimore County. Prior to his work at Ellumen, Iyanuoluwa built an algorithm for repurposing old drugs for new use using the DReiM methodology. He has been published in ResearchGate and IEEE for his research on machine learning. Iyanuoluwa completed his master’s in bioinformatics at Morgan State University and his Ph.D. in information systems (machine learning/AI) at University of Maryland Baltimore County.

Todd R. McCollough is a Software Engineer for Ellumen. He works primarily with the CVIX/VIX Services which support image viewers and applications to provide the ability for users to query, retrieve and manipulate VA and DoD medical images and image artifacts. Todd is a co-inventor on several issued U.S. patents and is passionate about discovering novel ways to image patients and improve patient care. Todd received his bachelor’s and master’s degrees in biomedical engineering from Northwestern University.