AI Case Studies and Applications

AI is transforming businesses across industries. In this chapter, we'll showcase real-world examples of companies successfully integrating AI. These success stories demonstrate the tangible benefits of AI adoption, from increased efficiency to improved customer experiences.

Curating Impactful Stories:

• Focused on Diversity: Curate case studies from various sectors like marketing, finance, healthcare, and manufacturing.
• Quantify Success: Highlight measurable outcomes like increased efficiency, cost savings, or improved customer satisfaction.
• Highlight Human-AI Collaboration: Showcase how AI complements human expertise, not replaces it.

30 AI Case Studies and Applications (2024)

AI has a wide range of use cases across industries and business functions. Some examples of AI use cases include:

Google Case Study

Google Photos, a photo sharing and storage service, successfully implemented AI to enhance user experience. The service uses image recognition technology to analyze and categorize photos in the user's gallery, making it easy to search and organize thousands of images.

For instance, users can search for "dogs" and all images with dogs would appear. Google Photos also uses AI to create collages, animations, and movies from users' photos, providing a unique, personalized experience. The implementation of AI in Google Photos is a great example of enhancing a product's functionality and user experience through technology.

Google Flights, a comprehensive tool for booking travel, successfully leveraged AI to provide a user-friendly method of communicating AI-powered price predictions. The goal was to help users make more informed decisions when booking flights.

By using historical flight data, the AI system predicts whether the prices for a selected flight are expected to increase or decrease. This information is displayed in a simple, accessible manner, providing users with a recommendation on whether it's the best time to book.

This use of AI not only enhances the functionality of Google Flights but also significantly improves the user experience by adding a layer of transparency and assistance to the often complex process of booking travel.

Waze, a community-driven GPS navigation application, successfully utilizes AI to optimize routes for users. The application takes into account real-time traffic conditions, reported accidents, and road closures among other factors to ensure that users are directed on the fastest and most efficient path to their destination.

Through AI and machine learning, Waze is able to predict traffic patterns and suggest alternative routes to users, improving their overall driving experience. The application also allows users to report accidents, police traps, or other hazards on the road, providing real-time updates to other users and adding a layer of community-driven data to the AI's decision-making process. This innovative use of AI not only enhances the functionality of Waze but also significantly improves the user experience by providing time-saving alternatives and real-time updates.

KLM Royal Dutch Airlines Case Study

KLM implemented an AI-powered chatbot on its Facebook Messenger platform to provide customers with quick and accurate responses to their queries.

The chatbot, called BlueBot, is designed to handle a range of customer queries, from flight information and baggage allowances to booking confirmations and refunds. Customers can interact with BlueBot through the Facebook Messenger app, and the chatbot uses natural language processing (NLP) technology to understand and respond to customer queries.

Since implementing BlueBot, KLM has seen a significant improvement in customer service efficiency. The airline reports that the chatbot is able to handle around 60% of customer queries without the need for human intervention. This has freed up customer service representatives to focus on more complex queries, improving the overall customer experience.

Coca Cola Case Study

Coca-Cola implemented an AI-powered marketing platform called Albert to help it optimise its digital advertising campaigns.

Albert uses machine learning algorithms to analyse customer data and identify patterns and insights that can be used to optimise digital advertising campaigns. The platform is able to make real-time adjustments to advertising campaigns based on factors like customer behaviour, preferences, and purchasing history.

Since implementing Albert, Coca-Cola has seen significant improvements in its digital advertising campaigns. The platform has helped the company increase its return on investment (ROI) by optimising ad spend and targeting the most profitable customer segments.

UPS Case Study

UPS implemented an AI-powered logistics platform called ORION (On-Road Integrated Optimisation and Navigation) to help it optimise its delivery routes and improve overall efficiency.

ORION uses machine learning algorithms to analyse data from multiple sources, including customer information, traffic patterns, and weather conditions, to generate optimised delivery routes for UPS drivers. The platform is able to make real-time adjustments to delivery routes based on changing conditions, ensuring that packages are delivered in the most efficient way possible.

Since implementing ORION, UPS has seen significant improvements in its delivery operations. The platform has helped the company reduce the distance its drivers travel by millions of miles each year, resulting in significant cost savings and environmental benefits.

JPMorgan Chase Case Study

JPMorgan Chase implemented an AI-powered virtual assistant called COiN to help it automate its back-office operations and improve efficiency.

COiN uses machine learning algorithms to analyse large amounts of data from various sources, including invoices, receipts, and other financial documents. The platform is able to automate tasks like data entry, reconciliation, and compliance checks, freeing up human employees to focus on more complex tasks.

Since implementing COiN, JPMorgan Chase has seen significant improvements in its back-office operations. The platform has helped the bank process large volumes of financial documents quickly and accurately, reducing errors and improving compliance with regulatory requirements.

IBM Watson Case Study

IBM Watson Health developed an AI-powered platform called Watson for Oncology, which is designed to help healthcare professionals diagnose and treat cancer.

Watson for Oncology uses natural language processing (NLP) and machine learning algorithms to analyse large amounts of patient data, including medical histories, lab reports, and other diagnostic tests. The platform is able to generate personalised treatment recommendations for individual patients based on their specific medical needs.

Since implementing Watson for Oncology, healthcare professionals have reported significant improvements in the accuracy and speed of cancer diagnosis and treatment. The platform has helped doctors identify previously overlooked treatment options and avoid potential medical errors.

Siemens Case Study

Siemens implemented an AI-powered platform called the Siemens Digital Enterprise Suite to help it optimise its manufacturing operations.

The platform uses machine learning algorithms to analyse large amounts of data from various sources, including sensors, machines, and other manufacturing equipment. The platform is able to generate real-time insights into production processes and identify opportunities for optimisation and improvement.

Since implementing the Siemens Digital Enterprise Suite, the company has reported significant improvements in efficiency and productivity. The platform has helped Siemens optimise its manufacturing processes, reducing downtime, and improving overall equipment effectiveness.

Unilever

Unilever implemented an AI-powered recruitment platform called HireVue to help it streamline its hiring process and improve candidate selection.

HireVue uses machine learning algorithms to analyse video interviews conducted by job candidates. The platform is able to identify patterns in candidate behaviour, such as body language and facial expressions, to generate insights into their suitability for a particular role.

Since implementing HireVue, Unilever has reported significant improvements in the efficiency and effectiveness of its recruitment process. The platform has helped the company identify high-potential candidates more quickly and accurately, reducing the time and cost involved in the hiring process.

Darktrace Case Study

Darktrace has developed an AI-powered cybersecurity platform called the Enterprise Immune System, which is designed to help organisations detect and respond to cyber threats in real-time.

The platform uses machine learning algorithms to analyse large amounts of data from various sources, including network traffic, user behaviour, and other system logs. The platform is able to detect anomalous activity and identify potential threats before they can cause damage to the organisation.

Since implementing the Enterprise Immune System, Darktrace’s customers have reported significant improvements in their ability to detect and respond to cyber threats. The platform has helped organisations identify previously unknown threats and take corrective action to prevent further damage.

Enel Case Study

Enel has implemented an AI-powered energy management platform called Enel X to help it optimise its energy distribution and consumption.

Enel X uses machine learning algorithms to analyse large amounts of data from various sources, including energy production and consumption data, weather patterns, and energy market data. The platform is able to generate real-time insights into energy demand and consumption patterns, helping Enel optimise its energy distribution and consumption in response to changing conditions.

Since implementing Enel X, the company has reported significant improvements in energy efficiency and cost savings. The platform has helped Enel optimise its energy distribution and consumption, reducing waste and improving overall energy efficiency.

Blue River Case Study

Blue River has developed an AI-powered crop management system called See & Spray, which is designed to help farmers optimise their crop yields and reduce the use of herbicides.

See & Spray uses computer vision and machine learning algorithms to identify and target individual plants in a crop field. The system is able to differentiate between crops and weeds, and can selectively apply herbicides to the weeds, reducing the amount of herbicide needed and minimising the impact on the crops.

Since implementing See & Spray, farmers using the system have reported significant improvements in crop yields and reductions in herbicide use. The system has helped farmers optimise their crop management, reducing costs and improving overall sustainability.

eBrivia Case Study

eBrevia has developed an AI-powered contract analysis platform, which is designed to help law firms and corporate legal departments automate the contract review process.

The platform uses natural language processing (NLP) and machine learning algorithms to analyse and extract key provisions from contracts, including indemnification clauses, termination provisions, and change of control clauses. The system is able to identify potential issues or inconsistencies within the contract, and can provide recommendations for how to resolve these issues.

Since implementing eBrevia, law firms and corporate legal departments using the platform have reported significant improvements in efficiency and cost savings. The system has helped them to automate the contract review process, reducing the amount of time and resources required to review and analyse contracts.

Lemonade

Lemonade has implemented an AI-powered claims processing platform, which is designed to improve the speed and accuracy of claims processing.

The platform uses natural language processing (NLP) and machine learning algorithms to analyse claims and assess the likelihood of fraud. The system is able to automatically approve certain claims, reducing the need for human intervention, and can identify potential fraud cases for further investigation.

Since implementing the AI-powered claims processing platform, Lemonade has reported significant improvements in claims processing times and cost savings. The platform has helped the company to automate the claims process, reducing the amount of time and resources required to process claims.

Carnegie Learning Case Study

Carnegie Learning, an education technology company, has developed an AI-powered math education platform called Mika, which is designed to provide personalised learning experiences for students.

Mika uses machine learning algorithms to analyse students’ learning patterns and provide personalised feedback and guidance. The platform adapts to each student’s individual needs, providing them with personalised recommendations for further study and practice.

Since implementing Mika, educators and students using the platform have reported significant improvements in student engagement and achievement. The system has helped to improve students’ math skills and confidence, providing them with personalised learning experiences that are tailored to their individual needs.

Netflix Case Study

Netflix, a popular streaming service, has implemented an AI-powered recommendation engine, which is designed to provide personalised content recommendations for users.

The recommendation engine uses machine learning algorithms to analyse users’ viewing histories and preferences, and provide them with personalised content suggestions. The system is able to identify patterns in users’ viewing behaviour and make recommendations based on their interests and preferences.

Since implementing the recommendation engine, Netflix has reported significant improvements in user engagement and retention. The system has helped to improve users’ satisfaction with the service, providing them with personalised content recommendations that are tailored to their individual interests.

Second Spectrum Case Study

Second Spectrum, an analytics company has developed an AI-powered platform, which is designed to provide real-time insights and analysis for basketball games.

The platform uses machine learning algorithms to analyse player movements and interactions, and provide coaches and players with real-time feedback and recommendations. The system is able to identify patterns and trends in player behaviour, and make recommendations for adjustments to gameplay and strategy.

Since implementing the AI-powered platform, Second Spectrum has been able to provide coaches and players with valuable insights and feedback, helping them to improve their performance on the court. The system has helped teams to identify areas for improvement and make strategic adjustments in real-time.

Compass Case Study

Compass, a real estate technology company, has implemented an AI-powered platform, which is designed to provide personalised recommendations for home buyers and sellers.

The platform uses machine learning algorithms to analyse real estate listings and provide personalised recommendations for properties that match a buyer’s preferences. The system is able to identify patterns in buyers’ behaviour and make recommendations based on their interests and preferences.

Since implementing the AI-powered platform, Compass has reported significant improvements in customer engagement and satisfaction. The system has helped to improve buyers’ experiences by providing them with personalised recommendations that are tailored to their individual needs.

Hilton Case Study

One example of AI being used for hospitality is the case of Hilton. The hotel chain has implemented an AI-powered concierge service, which is designed to provide personalised recommendations and assistance for guests.

The AI-powered concierge, called Connie, uses machine learning algorithms to analyse guests’ preferences and provide personalised recommendations for local restaurants, attractions, and events. The system is able to understand natural language queries and provide helpful responses in real-time.

Since implementing Connie, Hilton has reported significant improvements in customer satisfaction and engagement. The system has helped to improve guests’ experiences by providing them with personalised recommendations and assistance, making their stays more enjoyable and memorable.

Amazon Case Study

One example of AI being used for retail is the case of Amazon. The e-commerce giant has implemented an AI-powered recommendation system, which is designed to provide personalised product recommendations for customers.

The recommendation system uses machine learning algorithms to analyse customers’ browsing and purchasing behaviour, and provide personalised product suggestions that are tailored to their interests and preferences. The system is able to identify patterns in customers’ behaviour and make recommendations based on their individual needs.

Since implementing the AI-powered recommendation system, Amazon has reported significant improvements in customer engagement and sales. The system has helped to improve customers’ shopping experiences by providing them with personalised product recommendations that are relevant to their needs and interests.

IRS Case Study

The United States Internal Revenue Service (IRS). The tax agency has implemented an AI-powered platform, which is designed to detect and prevent tax fraud.

The platform uses machine learning algorithms to analyse tax returns and identify potential cases of fraud. The system is able to identify patterns in tax returns and make recommendations for further investigation.

Since implementing the AI-powered platform, the IRS has reported significant improvements in its ability to detect and prevent tax fraud. The system has helped to identify cases of fraud that may have gone undetected using traditional methods, and has helped to reduce the amount of fraudulent refunds paid out each year.

Microsoft Case Study

Microsoft has implemented an AI-powered platform, which is designed to optimise energy consumption in its data centres.

The platform uses machine learning algorithms to analyse data from sensors and other sources, and make real-time recommendations for optimising energy consumption. The system is able to identify patterns in energy usage and make recommendations for reducing waste and increasing efficiency.

Since implementing the AI-powered platform, Microsoft has reported significant reductions in energy consumption and carbon emissions. The system has helped the company to achieve its sustainability goals by reducing its environmental impact and promoting more efficient use of resources.

Airbus Case Study

Airbus, an aircraft manufacturer, has implemented an AI-powered predictive maintenance system, which is designed to identify potential issues with aircraft components before they cause problems.

The system uses machine learning algorithms to analyse data from sensors and other sources, and make predictions about when components may need to be serviced or replaced. The system is able to identify patterns in component behaviour and make recommendations for maintenance based on the data.

Since implementing the AI-powered predictive maintenance system, Airbus has reported significant improvements in aircraft reliability and safety. The system has helped the company to reduce the number of unscheduled maintenance events, and minimise downtime for aircraft.

Komatsu Case Study

Komatsu, a Japanese construction equipment manufacturer, has implemented an AI-powered platform, which is designed to optimise the operation of its construction equipment.

The platform uses machine learning algorithms to analyse data from sensors and other sources, and make real-time recommendations for optimising equipment usage. The system is able to identify patterns in equipment behaviour and make recommendations for reducing waste and increasing efficiency.

Since implementing the AI-powered platform, Komatsu has reported significant improvements in equipment performance and efficiency. The system has helped the company to reduce fuel consumption, minimise downtime, and improve overall productivity.

DHL Case Study

DHL, a global logistics company, implemented an AI-powered platform, which is designed to optimise its logistics operations and improve delivery efficiency.

The platform uses machine learning algorithms to analyse data from sensors and other sources, and make real-time recommendations for optimising delivery routes, vehicle usage, and delivery schedules. The system is able to identify patterns in delivery behaviour and make recommendations for reducing waste and increasing efficiency.

Since implementing the AI-powered platform, DHL has reported significant improvements in delivery efficiency and customer satisfaction. The system has helped the company to reduce delivery times, minimise fuel consumption, and improve overall productivity.

NVIDIA Case Study

One example of AI being used for gaming is the case of NVIDIA, a technology company that specialises in graphics processing units (GPUs) for gaming and other applications. The company has developed an AI-powered platform called NVIDIA DLSS (Deep Learning Super Sampling), which is designed to improve the performance and visual quality of games.

The platform uses deep learning algorithms to analyse graphics data and generate high-quality images in real-time. It is able to identify patterns in graphics data and make predictions about how to improve the image quality and performance.

Since implementing the NVIDIA DLSS platform, game developers have reported significant improvements in game performance and visual quality. The platform has helped to reduce the workload on GPUs, allowing for higher frame rates and smoother gameplay.

Sephora Case Study

Sephora, a cosmetics retailer, has implemented an AI-powered platform called “Virtual Artist”, which is designed to enhance the customer experience and increase sales.

The platform uses augmented reality and machine learning algorithms to help customers try on different makeup products virtually. Customers can use the Sephora app to scan their face and then apply different makeup products to see how they would look in real life. The platform also uses machine learning to recommend personalised product recommendations based on the customer’s skin tone and preferences.

Since implementing the Virtual Artist platform, Sephora has reported significant improvements in customer engagement and sales. The platform has helped the company to increase customer satisfaction and reduce product returns, as customers can now try on makeup virtually before making a purchase.

Hootsuite Case Study

Hootsuite, a social media management platform, has implemented an AI-powered feature called “AdEspresso by Hootsuite”, which is designed to help businesses optimise their social media advertising campaigns.

The platform uses machine learning algorithms to analyse data from various sources, including social media ad performance and audience behaviour. It is able to identify patterns in audience behaviour and make recommendations for optimising ad spend, ad targeting, and messaging.

Since implementing AdEspresso by Hootsuite, businesses have reported significant improvements in their social media advertising performance. The platform has helped businesses to increase their return on ad spend, improve targeting accuracy, and reduce the time required to launch campaigns.

United Nations World Food Programme Case Study

The United Nations World Food Programme (WFP) has implemented an AI-powered platform called “Building Blocks”, which is designed to improve the efficiency and effectiveness of its aid distribution efforts.

The platform uses machine learning algorithms to analyse data from various sources, including satellite imagery, weather patterns, and social media. It is able to identify areas of need, predict potential crises, and optimise aid delivery routes.

Since implementing Building Blocks, the WFP has reported significant improvements in its aid distribution efforts. The platform has helped the organisation to increase the speed and accuracy of aid delivery, reduce waste and inefficiencies, and reach more people in need.

Tesla Case Study

Tesla, a company that produces electric cars, has implemented an AI-powered platform called “Autopilot”, which is designed to enhance the safety and performance of its vehicles.

The platform uses machine learning algorithms to analyse data from various sensors, including cameras and radars, to detect obstacles and other vehicles on the road. It is able to make real-time decisions about braking, steering, and acceleration to avoid collisions and improve driving performance.

Since implementing Autopilot, Tesla has reported significant improvements in vehicle safety and performance. The platform has helped the company to reduce the number of accidents and increase the efficiency of its vehicles.

The Next Rembrandt Case Study

The Next Rembrandt project, a collaboration between ING Bank and J. Walter Thompson Amsterdam, used machine learning algorithms to create a new “Rembrandt” painting, designed to look and feel like one of the master’s original works.

The project started by analysing data from Rembrandt’s paintings, including brushstrokes, composition, and colour. The machine learning algorithms then used this data to create a new painting in the style of Rembrandt, which was produced using a 3D printer.

The result was a highly detailed painting, complete with brushstrokes and intricate details, that looked and felt like an original Rembrandt painting. While the painting was not created by Rembrandt himself, it demonstrated the potential for AI to create art in the style of famous artists.

These are just some examples of the many use cases for AI in business. As AI technology continues to develop, new use cases will continue to emerge, creating new opportunities for businesses to improve their operations and drive innovation.