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With great leaps in digital innovation, the emerging technologies have revolutionized the world and continue making a big impact in all spheres of life including businesses across all industries. Consumers and enterprises across the globe have a lot of potentials to look forward to, as frontier technologies like AI, ML, RPA, IoT and Big Data, are finally available to mass markets.
But the terms AI, machine learning, and robotic process automation are often used interchangeably when there are key differences between each type of technology. With the overuse of these buzzwords, it is critical to understand what these terms really mean. This section provides a brief sketch of these trending technologies.
Robotic Process Automation or RPA is an application of technology aimed at automating business processes. Using RPA tools, a company can configure software, or a “robot,” to capture and interpret applications for processing a transaction, manipulating data, triggering responses and communicating with other digital systems, according to the Institute for Robotic Process Automation and Artificial Intelligence. RPA scenarios can stretch across a variety of tasks from automating workflows, managing infrastructure to labor-intensive back-office processes. The main goal of the robotic process automation process is to replace repetitively, time-consuming, labor-intensive clerical tasks performed by humans, with a virtual workforce.
Artificial Intelligence is a way of making a computer, a computer-controlled robot, or a software think intelligently, in a similar manner the intelligent humans think. It is an umbrella term for a machine’s ability to imitate a human’s way of sensing things, making deductions and communicating. An example of this is machine vision, which allows for real-time traffic counting from the feed of a traffic camera, the anticipation of congestion, and alerts concerning potential emergencies.
Machine Learning is an idea to learn from examples and experience, without being explicitly programmed. Instead of writing code, data is fed into a generic algorithm, and it builds logic based on the data given. It is a method of data analysis that automates statistics and analytical model building. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention.
While RPA involves processing rule-based, repetitive tasks in high volumes, it needs to be explicitly trained for new tasks and can only take inputs in predefined formats. Businesses leverage AI to perform complicated tasks that may have unformatted and unstructured inputs. As AI evolves with performance, it drives to find better solutions. ML, on the other hand, is used to identify and analyze trends to predict outcomes with self-learning capability.
With the extensive hype surrounding these new world technologies, readers would expect high adoption rates. As per a survey report by Statista, 84% of enterprises believe investing in AI will lead to greater competitive advantages. However, the reality is far different. The next section explores this paradox.
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