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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.

Large enterprises are eager to use artificial intelligence software, but many of them aren't aware of the hardware required to execute many AI capabilities. To get a better idea of these hardware considerations, Emerj spoke with Victoria Rege, Director of Alliances & Strategic Partnerships and Graphcore, for Kisaco Research's AI Hardware Summit in Europe, which takes place October 29 - 30 in Munich, Germany.

In banking and finance, chatbots have the potential to improve the customer experience by allowing customers to check their account balances, transfer money, learn about interest rates, change their billing addresses, and more.

This article is based on a presentation given by Emerj CEO Daniel Faggella in Geneva, at the 2019 New Shape Forum: Weapons Governance for the Geneva Disarmament Platform. To learn more about Emerj's AI presentations and speaking, visit our presentations page.

Many large insurers are finding ways to digitize parts of their business process in preparation for future projects involving machine learning. This is especially true in claims processing, which could become faster and less error-prone if claims adjusters did not have to search through large amounts of data or paper documents manually.

What is the state of AI in business today - and what do mid-market business leaders need to know about AI now?