AI Sector Overviews Articles and Reports

The military has always been at the forefront of advanced technology. Some of the most important applications we use every day, such as the Internet, were developed by or for military use. That said, the military is adopting predictive analytics at what seems to be a slower pace than industry, although there are likely applications for the technology that they choose not to publicize. 

Although one might assume healthcare companies are investing the most amount of money into AI for R&D efforts, such as clinical trials, drug discovery, and surgical robotics, the top healthcare companies don't seem to be publicizing their efforts if they are.

Applications for artificial intelligence technology within the financial industry are most often focused on document search and fraud detection. This is especially true for large credit card companies such as Visa and Mastercard, who themselves provide technology to smaller businesses and card issuers.

The US Department of Defense's DARPA has a plan to invest as much as $2 billion in artificial intelligence research and development in the next 5 years. This is on top of the $2 billion the federal government has already spent on AI-based technology R&D.

Various use cases and applications for AI and machine learning in the healthcare industry are proposed more frequently now than ever. Healthcare leaders may find it difficult to keep up with where AI is being applied in their sector.

Banks and financial institutions are particularly opaque when it comes to how they implement and leverage AI for their business. Mastercard is a key example of this because they use most of their AI applications internally and have only recently begun to make their technology more transparent to the greater financial industry.

Northrop Grumman Corporation is a U.S. global technology company that provides autonomous, aerospace, cybersecurity, and defense solutions to military and commercial organizations.

We've spoken to many leaders in healthcare and pharma over the last half a decade, and when it comes to AI, the most pressing challenge that healthcare and pharma leaders report is that they're unsure of how to streamline and structure their data in a way that lets them build machine learning models. Healthcare companies are stuck in the data consolidation phase of their potential AI initiatives while vendor after vendor is trying to sell them on a new application that the company might not even be close to ready for.