How to Execute AI Data Entry
Artificial Intelligence is often seen as a buzzword or a generality, making hard to conceptualize — but when it comes to something specific like AI data entry, what does it actually look like in practice?
The pairing of two technologies — OCR and RPA — help create a true AI data entry system.
Optical Character Recognition (OCR) is a technology that can ‘read’ images and turn them into characters, text, and numbers. For example, if an employee receives a PDF file in an email, OCR can pull all of the text and data from that file; this process would normally require a person to read and manually record the data. The use cases for this kind of technology are numerous, and many organizations already have OCR in place for tasks like invoice processing and expense reporting. Once OCR is put to work, however, some questions remain: where does all of the data go once it’s captured, and how does it get there?
That’s where Robotic Process Automation (RPA) comes in.
Moving Data with Digital Workers
Once data has been extracted from a file, many organizations still rely on manual processes to get that data into the correct systems. In a hospital, for example, OCR could read a form filled out by a patient, but an employee would still have to manually enter the data that OCR pulled from the form into a patient management system. With one of RPA’s intelligent digital workers, the burdensome task of manual data entry can be completely automated, allowing employees to spend more time on more important work, eliminating data errors, and creating a true AI data entry system.
The ‘intelligence’ behind these digital workers shouldn’t be understated – they are capable of anything from performing web searches to logging into systems and collecting specific files. When paired with OCR, there are almost boundless potential uses in industries like insurance, human resources, financial services, healthcare, public sector and more.
An AI Data Entry System
OCR and RPA each stand on their own as efficiency-increasing software solutions, but when combined they become even better. Typically, OCR solutions work best when used on highly structured documents, because it’s easy for them to recognize the data amongst the layout of the document. When combined with RPA, however, OCR can analyze and capture data from even highly unstructured documents from a variety of sources. What’s more, RPA’s digital workers will continue to learn from each document analyzed and only improve at collecting the data from a page.
An AI data entry system using RPA and OCR as complimentary solutions unlocks even more potential from both platforms than they have on their own. By implementing these solutions into business processes, a company can streamline manual tasks and free up its workforce for value-added work, all while eliminating errors and inefficiencies in its most important data. Look for a partner with experience and expertise in both OCR and RPA as you consider taking your organization to a new level of efficiency!
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