Progressive is one of the largest auto insurers in the US. The company has been experimenting with AI since the middle of the 2010s, with customer-facing applications that update insurance premiums based on driving habits and answer questions in a chat window. In this article, we discuss both of these AI use-cases. More specifically:
The AI conversation has made it into the C-suite at large banks. Leaders from Citi to JP Morgan are considering how to respond to their competitors' press releases and looking to craft winning AI strategies and adopt low-hanging fruit AI applications in their business.
The artificial intelligence space is increasingly competitive with new AI companies and products being developed every day. Every industry is getting more and more crowded with products from startups and from established companies.
The retail industry collects massive amounts of data every day, and this makes its key processes ripe for automation with machine learning. Along with the manufacturing sector, the retail industry likely stands to benefit the most from one particular AI technique in the next few years: machine vision, also known as computer vision.
In the 1990's, Ben Horrowitz described a product manager as follows:
"A good product manager is the CEO of a product."
That definition isn't always a perfect fit, but it can be a good way of summarizing the responsibilities of a product manager; they are wholly in charge of bringing an in-house product from inception to generating an ROI.
Applying AI to the real world is much more difficult than applying it in digital ecosystems; this is what makes robotics use-cases in business so much more difficult than applications such as AI-enabled fraud detection.
Many business leaders make the mistake of believing that AI and machine learning are like regular IT, but this could not be further from the truth. In large part, this is because, unlike simple software solutions for discreet business problems, it can be very difficult to measure the ROI of machine learning.