AI Articles and Analysis about Business intelligence and analytics

Predictive and prescriptive analytics seem to be the most common types of AI applications at the top medical device companies. These companies, particularly Becton Dickinson, Medtronic, and Fresenius Medical Care, tend to partner with AI firms and vendors to create software that they intend to sell to hospitals most often.

According to McKinsey, global gas demand is expected to reach 4,503 billion cubic meters (bcm) in 2035, showing a more than 1% annual growth from 3,736 bcm in 2017. Asia is expected to be the biggest consumer of the resource at 47%, followed by the rest of the world (24%), the Middle East (16%), and the United States (14%).

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.

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.

Machine learning has far-ranging applications in the finance space broadly from document digitization to document search, chatbots to fraud detection. The insurance space in particular, however, stands to benefit from AI and machine learning applications in a few unique ways. They could help insurance firms with a challenge that’s at the forefront of the insurance world: attracting and meeting the needs of millennial customers.

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.

The finance sector has proven itself an early adopter of AI in comparison to other industries. As such, the applications of artificial intelligence and machine learning in finance are myriad. Traders, wealth managers, insurers, and bankers are likely well aware of this in some form.