Some verticals where Machine Learning is disruptive

Health Science

Rising costs and other challenges, such as the increase of the average population age apply a mounting pressure on the actual health care systems. By introducing Artificial Intelligence, which will help assist Health Care Professionals, we see a better and safer environment, which will lead to a better life. Below are a few examples.

  • Automated Differential Diagnostics
  • Automated Drug Discovery
  • Hospital Flux Optimization

Manufacturing

The combination of Machine Learning, advanced robots, additive manufacturing, and the internet of things (IoT) will usher in the fourth industrial revolution. Initiatives to apply AI technologies such as deep learning and computer vision are underway. Here are a few concrete examples of use-cases:

  • Automated Quality Control
  • Predictive Maintenance on Production Machines
  • Products Generative Design

Energy

The Energy sector is at a cross-road. The emergence of renewable energy sources, the electrification of transports and the de-nuclearisation pose great challenges to the industry. Leveraging the enormous amount of data, Machine Learning is helping the shift by:

  • Forecasting Production
  • Detection of Faulty Entities in Electrical Network
  • Predictive Maintenance of Production Assets

Marketing

Machine Learning makes it possible to process, infer, and analyze data on a massive scale. Data becomes insight into general and personal customers’ behavior, allowing businesses to act proactively on cases such as:

  • Customer Segmentation
  • Advertisement Optimization
  • Customer Churn Prediction

Finance

As a heavy-numbers and data-driven industry, the financial sector has been one of the first movers in this area. There are a plethora of applications, here is a short selection of some of them:

  • Fraud Detection
  • Portfolio Optimization
  • Automated Risk Assessment

Logistics

AI has the potential to augment current logistics activities from end to end significantly. AI-Powered warehouse logistics will reduce cost and create new value. A few examples include:

  • Inventory Needs Forecasting
  • Supply Chain Synchronization
  • Automated Supplier Relationship Management

Agriculture

Being a somewhat manual industry and science, agriculture can benefit a lot from the wealth of data it is generating. Leveraging this data, AI can help agriculture and ultimately help feed the increasing population. Examples are:

  • Production Forecasting
  • Plants and Animals Disease Detection
  • Automated Crop Monitoring & Optimization

Sports

Fair games, non-disputable decisions, and in-depth statistics are what makes games enjoyable for sports lovers. By using the latest advances in Artificial Intelligence and Computer Vision, it is possible to provide advanced statistics about the matches and make sure that everyone is having a great time. Some examples are:

  • Automated Highlights Generation
  • Performance Analytics
  • Game Statistics Generation

Retail

Innovative retailers have already taken steps to create an AI-enabled future. AI will impact virtually every aspect of retail operating models: Customer engagement, Merchandising, and Back-Office automation. Specific applications include:

  • Customer Behavior Analytics
  • Shelves & Ads Placement Optimization
  • Sales & Revenue Forecasting

Automotive

The automotive industry is one of the most high-tech industries in the world. With the emergence of connected vehicles, they are generating data at a faster pace than ever before as well as making it easily accessible through centralized cloud services. In the this industry, machine learning (ML) is most often associated with product innovations, some examples are:

  • Predictive Maintenance and Services
  • Autonomous and Assisted Driving
  • Car Personalization for Drivers
And many more!