Why RPA Needs Cognitive Document Automation, According to HfS
This has been a core pain point for RPA customers in every industry, particularly as they expand their digital workforces to meet increasing business demands.
Fortunately, cognitive document automation (CDA) addresses this issue by leveraging artificial intelligence (AI) algorithms that help computers understand, decide, learn and predict to better automate business processes that require capture capabilities. When customers combine RPA and CDA, they are empowered to manage their data-centric and document-centric tasks and solve their most pressing business problems.
CDA involves 3 stages: acquire, understand and integrate. Together, RPA and CDA acquire documents and electronic data from numerous sources; extract, aggregate and transform the data into business-consumable content; and deliver this data to various systems and processes that need it.
And although optical character recognition (OCR) is a core component of document capture, next-generation cognitive capture is more. This has become more relevant—and important—as RPA has evolved from simply automating repetitive tasks and augmenting the work of employees to a richer set of capabilities that address broader business requirements.
This is why the emergence of CDA was such a breath of fresh air—it automates the processing of unstructured data contained in documents and emails, information that was difficult, if not impossible, to access before. CDA is the intelligent “head work” of understanding what the document or email is about, what information it contains and what to do with it. And it’s the combination of RPA and CDA that enables the processing of any data for any business process.
While most C-suite leadership agrees and is pleased with return on investment, and time- and cost-savings found with RPA, there’s still some challenges to overcome. According to a recent report, while there is broad adoption of RPA, 27% of enterprises still rely on humans to review all documents and emails, and only 13% have fully automated unstructured text interpretation.
While we are on the subject of RPA: typically, this is the next automation layer in building an Intelligent Automation solution, wherein organizations are empowered to build and manage a digital workforce by seamlessly integrating RPA and CDA. But end-to-end Intelligent Automation that supports enterprise digital transformation encompasses much more.
Additional key pillars for Intelligent Automation
1. Process Orchestration
Another consideration is process orchestration, or pushing the process forward to achieve a desired result. We see this most often in various onboarding/new account opening use cases.
2. Mobility and Engagement
From gathering signatures or communicating with customers, creating those satisfying business moments within a mobile experience is the key to engagement.
3. Advanced Analytics
Peter Drucker once said, “If you are not measuring it, how can you manage it?” Analytics provides those actionable, up-to-the-moment insights to measure what is working, what is not and how to fix broken processes.
There’s not a single point solution available that can enable organizations to just flip a switch and automate across the enterprise. That’s why Intelligent Automation is so important. It’s the custom “mix” of technologies, designed to address the specific and individual challenges for each and every organization. It’s about addressing people, processes and technology.
Download this recent article from HfS for a closer look at how the recent Kofax acquisitions of NDI and TIS bridge the gap between RPA and Intelligent Document Management – helping empower customers to innovate and scale automation like never before.