Process automation is critical to any organization's growth in today's rapidly evolving world of business. To achieve their goals, many companies are turning to emerging technology for help, most notably artificial intelligence (AI), machine learning and robotics.
There’s been a lot of hype surrounding both in the past, but recent advances in tech are helping leading businesses find exciting ways to bring their value to process automation.
Here are a few examples:
Error Determination. All processes have potential for errors. Historically, businesses have had few options on how to improve service levels: training, checking every item in a process multiple times, or performing random sampling of items flowing through a process. Now, however, we have an improved way to answer the fundamental question: “Can we only check work that has errors?” With AI and machine learning, we can create analytical models based off key elements of a process that can determine the probability an item has an error, then route it through a quality process without impacting other work.
Optical and Intelligent Character Recognition. Straight-through-processing (STP) is the goal of most organizations. Let the process move the item along, update systems, then move the item to a human for processing only if there is information presented requiring human decisioning. Often times, it’s the beginning of a process that can be most challenging. For example, in highly-regulated fields like financial services and healthcare, where regulation hasn’t advanced to allow end-to-end digital processing, there is still a great amount of incoming paper and yes, faxes. Traditional optical character recognition (OCR) and intelligent character recognition (ICR) solutions haven’t made any real progress over the years. Traditionally, we got 90 percent character recognition and 40 to 60 percent word recognition, if we were lucky. Enter new AI and machine learning models. Add large amounts of data used to “train” these models, and we can now recognize words and sentences that may be difficult even for human workers to read.
Digital Worker Oversight. A 24/7 processing environment would be ideal, right? Digital workers (robotics, integration or similar technologies) are now entering processes with that very intention. But the challenge is how do we manage digital workers? They’re only as good as the training they’ve been provided or the integration patterns that are applied. If the work doesn’t match the training or the pattern, the digital worker’s “decision” could potentially cause a large amount of rework if it’s caught; or worst case, you may not know if the work was done incorrectly until a client informs you of the error days, weeks or months later. Digital workers also require oversight, reporting and management—maybe not as much as human workers, but those requirements are critical to delivering the automation and ultimately, the success expected.
Bringing value to process automation isn’t just hope anymore for artificial intelligence and machine learning. Learn more about the impact of robotics process automation in our case study, “RPA solution for mutual fund firm cuts data transformation time over 85%.”
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