AI Articles and Analysis about Process automation

Oil and gas companies face many of the same challenges as large banks and established insurance firms when it comes to searching through their backlogs of documents. They want to use the data stored within these documents to make decisions on where to drill and determine whether or not they’re in compliance with laws and regulations. 

Much of the artificial intelligence and automation discussion going on in the enterprise recently involves something that, in most cases, actually isn’t artificial intelligence at all: robotic process automation, or RPA.

Our sector-wide research suggests that natural language processing (NLP) is one of the more common AI approaches in banking AI use-cases today. Sentiment analysis is a capability of NLP which involves the determining whether a segment of open-ended natural language text (which can be transcribed from audio) is positive, negative, or neutral towards the topic being discussed.

Life sciences companies are likely to begin experimenting further with AI in their workflows in the coming years, but they face challenges in AI adoption due to strict regulations. Machine learning has a "black box" problem, meaning that it's in many cases impossible to know how a machine learning algorithm comes to its conclusions.

Customer data is essential for insurance firms to stay competitive in the coming decade. Insurance companies at present have backlogs of data on past and existing customers in the form of policy agreements, applications, and claims forms. They’ve also collected millions of images showing car damage, property damage, and personal injuries.

There are many possibilities for automation in the healthcare industry outside of AI. Robotic process automation (RPA) technology can serve healthcare companies with various use cases involving data transfer and clinical documentation. Moving important information from the business’ frontend to their deeper business processes is among the most common use cases for RPA in healthcare, and many other solutions emerge from this idea. A similar phrase and field to RPA is called White Collar automation and readers can find a full interview on white collar automation in healthcare today here. 

AI software for corporate banks is not too different from those for retail banks, although their data requirements and intentions for the software will differ. AI vendors currently selling to banks typically have clients covering all types of banking, but few specify any of their solutions to be for corporate banking specifically. Instead, they market themselves across the entire industry and give corporate banking details where appropriate.

There's an entire artificial intelligence ecosystem for enterprise search. Most of this is in a purely digital world. Most vendors help with a layer of AI-enabled search that understands terms or phrases and is able to return the results or answers to questions that someone types in. But the problem is compounded when it comes to searching the physical world.