Driven by strategic digital transformation efforts worldwide, Robotic Process Automation (RPA) is today the fastest-growing segment of the global enterprise software market.

By automating mundane, repetitive, time-consuming and error-prone manual tasks, RPA reduces costs and operational risks, enhances commercial outcomes, improves customer experience and provides valuable insights into company data, all while remaining highly scalable, flexible and predictable.

RPA is disrupting back-end operations as we know them, liberating the enterprise workforce and enabling it to focus on higher-value tasks.

But RPA has yet to become a fully mature sector and does not come without challenges.

Unstructured data and (process) customization as defining aspects of today's businesses are making enterprise processes difficult to automate end-to-end. Enter AI.

To meet a broad range of client needs, especially for enterprise workflows with high-volume, multi-decision processing requirements, RPA software vendors, value-added re-sellers and system integrators are looking to underpin AI and data management capabilities to deliver hyperautomation - integrated, intelligent automation.

If data is the new oil, running on low-quality fuel will not get you far

On the one hand, data sources have multiplied: emails, social media, mobile photos, location pins and time stamps, chat bots, live videos and what not, they all contain crucial data that is convenient to send, but not so convenient to processes, back-end systems having little control at input level. 

On the other hand, traditional optical character recognition (OCR) technologies fail to deliver acceptable results automatically, leaving much of the exception handling and validation to be done manually. With today's smartphones being the new scanners, the pass rates and accuracy offered by conventional capture are incompatible with the idea of end-to-end automation, let alone with that of hyperautomation.

Lastly, unstructured data is an impediment for the end-to-end automation of mission-critical, high-value, complex processes which typically integrate multiple external applications or include content- and context-dependent decision-making. With poor data creating downstream friction in workflows, automation is only leveraged at task level, in a fragmented manner. 

end-to-end processing

END-to-end processing

It's not fiction, it's (data) science.

It is difficult to extend RPA projects to upstream and downstream applications without a coherent data strategy ensuring high quality throughput at enterprise level. For many use cases, straight-through processing is therefore still a challenge. The docBrain platform includes a separate data entry interface allowing human-in-the-loop as well as quality assurance (QA) monitoring to analyze the training results and maintain performance over time. Moreover, docBrain is in itself a conduit and centralized hub for extensions and add-ins from other technology solutions.

rpa project scoping

Project scoping

Fail to plan and you plan to fail.

Was each owner, object, task, activity, decision and rule applied throughout the entire process, from the first point of entry to the final output? Is the process "robotic" enough to start with? Does scaling up mean simply finding more tasks to automate? There can be RPA projects that fail or at least result in a gap between the initial business requirements and the delivered outcomes. ROI can be difficult to prove and the case for scaling up difficult to make. docBrain accelerates time-to-deploy and ROI realization for the most complex processes.

The docBrain difference

Cross the ROI gap by ensuring a constant flow of high quality data all throughout the workflows stages and ensure scaling up in further projects with more complex workflows & high data volumes