In this special guest feature, Eric Bussy, Worldwide Corporate Marketing and Product Management Director at Esker, discusses the importance of machine learning for enabling back office processes? Eric is responsible for the development of strategic products, services and solutions. He joined Esker in 2002 as Director of Marketing Communications, and in 2005, extended his responsibilities to include product management.
Many businesses have yet to understand the full potential of machine learning and cognitive computing for back office processes. The benefits range far beyond simply increased productivity and faster supply chains. Today’s advanced algorithms have the computational power and speed to gather, analyze and manage vast repositories of data. As the Internet of Things (IoT) transitions from theoretical to practical application, information harvested at every touch point in business operations will provide opportunities for insights into areas no traditional analysis has yet explored. The amount of data is too vast for humans to adequately mine and leverage. The key to gaining those insights—and keeping pace with competitors—is machine learning.
Where once we associated automation with standard manufacturing processes, it’s knowledge workers who stand the most to gain from advances in machine learning. From finance to healthcare to retail, automation will soon be critical to document-heavy industries. A recent McKinsey report said predictable manual processes, especially data processing, are among the activities that most lend themselves to automation. The potential to streamline the collection and processing of data is nearly unlimited. And it starts with back office document management.
For the purposes of discussing document processing automation, it’s more appropriate to refer to machine learning as auto-learning. Like almost all emerging tech, it’s cloud technology that enables the practical use of advances in auto-learning. Prior to the cloud, document processing automation took a very basic approach. Systems would build an ever-growing knowledge database around users’ habits. Over time, this rudimentary automation could make changes that the user would have made automatically, similar to autocorrect systems in word processing programs.
But this static data extraction method has limits.
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