AI Sector Overviews Articles and Reports
Artificial intelligence “sector overview” reports are designed to help business leaders explore the possibilities and important AI trends across industries. Search our sector overview reports below:
Modern AI and machine learning software require large sets of data in order to train its algorithms to make judgments, make predictions, and take actions. Data is a critical part of bringing artificial intelligence to life in different industry sectors. The applications we’ve highlighted below involve organizing historical and real-time data from existing businesses in the retail sector from which data scientists can build machine learning models.
It should come as no surprise that AI has found its way into radiology in a similar fashion to most other medical fields. Many AI vendors selling into the radiology field are just beginning to gain regulatory approval. We researched the use of AI in radiology to better understand where AI comes into play in the industry and to answer the following questions:
Some financial institutions have begun investing in departments that focus on artificial intelligence and machine learning applications that could determine their customer's sentiments towards market developments. These applications fall under the category of sentiment analysis. We have previously covered some of the top the machine learning applications in finance. In this report, we focus on AI-based sentiment analysis applications for the finance sector.
McKinsey reported that most oil and gas operators have not maximized the production potential of their assets. A typical offshore platform, according to the 2017 report, runs at about 77% of its maximum production potential. Industry-wide, the shortfall comes to about 10 million barrels per day, or $200 billion in annual revenue.
Our AI in Banking Vendor Landscape and Capability Map report details the state of various AI approaches and capabilities within specific banking functions, measuring them on their level of funding, evidence of ROI and adoption at large banks, and more. In this article, we discuss how and where banks are using natural language processing (NLP), one such AI approach—the technical description of the machine learning model behind an AI product.
Natural language processing, (NLP) is one AI technique that's finding its way into a variety of verticals, but the finance industry is among the most interested in the business applications of NLP. In fact, according to our AI Opportunity Landscape research in banking, approximately 39% of the AI vendors in the banking industry offer solutions that involve NLP.
Allied Market Research estimated the value of the global autonomous vehicle (AV) industry to reach $54.23 billion in 2019, increasing to $556.67 billion by 2026 at an annual growth rate of 39.47% during that period. It follows that AI would find its way into the autonomous vehicle world. We detailed our own timeline for self-driving cars, pooling quotes and insights from executives at the top 11 global automakers.
AutonomousNEXT released a report on the opportunity that AI might create in the banking and financial services industry. The report estimated that by 2030, the potential cost savings by applying AI in banking, investment management, and insurance were $490 billion in front office operations, $350 billion in middle office, $200 billion in the back office operations.