With the explosion of data and emerging automation technologies, organizations are looking at how they can optimize business processes to achieve greater operational efficiencies. They’re likely to find the engine runs smoothly until unstructured data enters the mix. At that point, the process stalls – or even stops dead in its tracks. This is a problem for companies that want to take full advantage of what robotic process automation (RPA) has to offer, including greater efficiency and a lower total cost of ownership (TCO) for their automation initiatives.
Documents and other unstructured data, such as PDFs, videos, photos, emails, and websites, make full end-to-end automation of business operations difficult because they require a human to analyze, understand, and make a decision based on the information contained within each. This creates bottlenecks and dramatically slows workflow – quite the opposite of what organizations want to achieve with their automation initiatives.
This situation is far from uncommon, making it a significant threat to companies’ automation ambitions. As much as 60% of business processes contain some unstructured data. That means 60% of the time; robots have to stop their work until a human intervenes.
For example, in the claims processing world, nearly every aspect of the process remains paper-based. People mail or email physical or scanned documents to a system, where humans must then review and classify them by hand. For those with full automation dreams, this is a nightmare.
It also might explain why, despite two decades of business process management (BPM) implementations, full process automation is still the exception. According to AIIM’s 2019 Emerging Technologies Market Report, “two-thirds of organizations say that specific core back-end processes are less than 50 percent automated.”
And while some industries are using RPA for records management, customer correspondence, check processing, and other paper-intensive processes, fewer than one in five organizations have fully automated their core back-end processes, AIIM found.
In the meantime, the problem caused by unstructured data is only going to get worse. Half of the respondents to the AIIM survey say 70% of the data in their organizations is unstructured. At the same time, organizations are anticipating massive data growth. According to the survey, 35% expect the amount of data to increase fivefold over the next two years. It’s no wonder that 70% of organizations surveyed by AIIM say unstructured information is the “Achilles’ Heel” for many RPA implementations.
To achieve mature levels of automation, businesses will need to combine RPA with artificial intelligence – a core capability of an Intelligent Automation platform. With advanced cognitive capture and entity extraction, analyzing and interpreting unstructured data becomes a reality. Intelligent automation enables organizations to digitally transform knowledge-based business processes, turning their nightmares into sweet dreams.
An Intelligent Automation platform can manage document separation, classification, and routing, increasing the speed of processing and accuracy, while reducing the need for human involvement. Thus, routine tasks that previously derailed a robot are handled more efficiently.
Consider what happens when a customer trying to open an account via the bank’s mobile application uploads a photo of their driver’s license. The image must be read and the data extracted. Or how RPA alone handles a patient email that includes important details about a recent claim. In both cases, the RPA bot can’t process this sophisticated data. A human must step in to read, understand, and make a decision.
But an Intelligent Automation platform does that and more. Using cognitive document automation (CDA), the platform captures, reads, and understands the information. Because CDA can read data in a variety of formats, it can transform the driver’s license and the email into usable information. Post using machine learning and natural language processing, the Intelligent Automation platform then interprets the data and determines what happens next.
An Intelligent Automation platform handles this job more effectively and at a lower cost than a “bolt-on” solution. This enables companies to create greater efficiencies, lower TCO, and fully automate their business operations end-to-end.
For organizations that are struggling to achieve more mature levels of automation due to data limitations creating bottlenecks and slowdowns, a key consideration should be implementing a solution that integrates RPA with artificial intelligence. Rather than endure the nightmare, organizations can advance automaton initiatives from repetitive transactional use cases to more complex knowledge-based business processes – enhancing customer experiences and operational excellence. With the combination of AI and intelligent automation technologies, your teams can begin working like tomorrow -today.