Tyler Suss, Product Marketing Director, Kofax
Modern enterprises are seeking to achieve greater levels of operational efficiencies by leveraging robotic process automation (RPA) and process transformation technologies. Despite all the work they are doing to automate and improve business processes, many implementations stall or stop dead in their tracks due to the complexity of the organisation’s data and unstructured documents. 60 percent of business processes contain some sort of unstructured data. Unstructured data can be PDFs, videos, photos, emails, websites, or any other format that’s not easily searchable. This means 60 percent of the time, employees need to intervene to read, classify and take action before the business process can advance further – creating bottlenecks and dramatically slowing workflow.
For instance, 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.
Inability to process unstructured data is the Achilles Heel for many unsuccessful RPA implementations. Organisations that want to automate complex data-driven business processes need to turn to advanced artificial intelligence (AI) technologies to enhance the effectiveness of their RPA investments. Integrating Natural Language Processing (NLP) and Machine Learning (ML) into RPA solutions can help with analyzing, understanding and classifying unstructured data – unlocking the ability to automate a substantial amount of business processes.
Here are four tips for overcoming the challenges of processing unstructured data to make automation dreams a reality:
- Integrate cognitive document automation (including foundational) with the RPA solution to automatically process documents needed for a business process and achieve the most seamless workflows.
- Prioritize flexibility. Document submission should be “smart” enough to allow customers to switch back and forth between channels during the same process.
- Make sure your solution can scale to very large document volumes and distributed work environments.
- Look for global applicability, with support for multiple user interface and OCR languages.