One of the more interesting and vibrant new spaces in enterprise software in the last few years has been the category of Robotic Process Automation or RPA. The premise is simple and compelling: Show a software robot how a repetitive work task is done, and it will literally learn how to do it by watching the employee carry it out manually. The result is a rules-based algorithm that can then be adjusted, configured, optimized, and deployed quickly in the field to have that task carried out by the software robot going forward.
RPA can handle a wide variety of business use cases, from invoice matching to mortgage processing that previously required humans to do most of the work (though they still have to help with edge cases, but usually with greatly reduced workloads.) What's been interesting is that because RPA goes so deeply into exactly what a business does on the ground, inside its processes, RPA is often viewed more as a business tool than an IT tool. Thus IT has been surprisingly slow at times in introducing it to the business side, and businesses, not being in the IT field, are often unaware of the potent capabilities of this generation of much smarter process Automation tools.
This state of affairs -- unclear ownership and tech unfamiliarity -- has led the RPA industry to flourish a bit less than it might otherwise have, yet still capture some of the most impressive growth stats in the enterprise IT industry
RPA also shows some of the best ROI of any new category of business apps. As the industry has matured and evolved, RPA has succeeded in having considerable real-world impact in the enterprise, with a growing wealth of case studies . While there are also some potentially concerning outcomes of adopting RPA, such as displacing workers at scale or the use of the technology by bad actors to engage in unwanted actiities, so far negative outcomes appear rather limited .
The result is that RPA is one of the most practical and impactful applications of artificial intelligence (AI) so far. Yet the realities of making RPA operational quickly became apparent in the initial years, overcoming the overly-simplistic expectations of quick watch-run-deploy-optimize cycles.
End-to-End Intelligent Automation As a Platform
For RPA to sustain and adapt within a business it needs proper support. In other words, adequate design, planning and governance of an overall automation approach, while staying agile enough to quickly change with the business. So too is having a well-defined end-to-end technology framework to automate new processes. This includes integrating a host of related capabilities in an outcome-based sequence: Data capture, ingestion, and cleaning, data enrichment, automation output, quality control, downstream analytics, and exception handling, not to mention back-end features like system integration, autoscaling, capacity management, security, and compliance.
In fact, wouldn't it be nice if most of this was already assembled as a ready-to-go solution around a capable RPA engine so it could be quickly applied to the business? This is, in fact, where a good part of the RPA industry is currently headed, so that businesses don't have to bolt-on the inputs and the outputs to an RPA tool, and then layer on the necessary analytics, operations, management, and governance capabilities.
