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AI and Machine Learning in the Order-to-Cash Cycle

How companies can harness artificial intelligence to reduce errors, increase customer service, and improve efficiency from order to fulfillment to payment reconciliation.

By Eric Bussy | Updated on October 23, 2018

It’s difficult to overstate the volume of buzz around artificial intelligence (AI). These technologies are finding their way into everything from the smallest consumer devices to the largest supercomputers—essentially into every corner of enterprise software and the farthest reaches of the cloud. And so journalists, analysts, and corporate decision-makers are all spending a lot of time writing and reading about AI, innovating around the concept of artificial intelligence, and integrating it into products.

The hype isn’t necessarily overblown. These technologies and their various components—like deep learning and machine learning algorithms and robotic process automation (RPA)—hold promise to accelerate processes, reduce errors, improve efficiency, and lower costs, particularly for areas of the business that are highly automated and data-rich. That would include the complex order-to-cash cycle, a vital part of any company’s operations. It’s where businesses interact with their customers, orders are made and fulfilled, bills are sent, disputes are resolved, and payments are received and processed.

A company’s reputation may rise or fall based on how it manages its order-to-cash cycle. Over recent decades, many companies have worked hard to improve operations in this area. Some have leveraged customer order management software to make them more efficient. AI and machine learning further extend the benefits of automation. The key to maximizing the benefits is to leverage the right AI solutions, in the right areas of the order-to-cash process.

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