Robotic process automation underpins artificial intelligence

June 16, 2016

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How robotic process automation technology can make artificial intelligence even smarter in the enterprise.

Assigning people the tedious task of manually reviewing and manipulating documents is a sure-fire way to miss errors and introduce inaccuracies as blurry-eyed employees move between programs and cut-and-paste data. Although this is standard practice at many companies, it’s a problem that Robotic Process Automation (RPA) technology is now able to solve.

RPA can mimic a human’s ability to gather data and put data into Excel spreadsheets. It can access disparate systems (ERP, sales, tax, etc.), review tens of thousands of documents in an hour or two (instead of several days), do so without error, and put the data in a standardized format.

But RPA’s advantages go far beyond imitating keystrokes and avoiding errors; RPA’s speed, accuracy and formatting abilities make it an essential foundational technology for a more automated, intelligent enterprise. More advanced RPA uses web log data, call log data, audio data, transaction data, etc. to learn the underlying businesses processes, bottlenecks and exceptions. Such machine learning of business processes is often called process mining and is used to automate and optimize existing business processes as well as detect non-compliance.


This business process automation also enables better controls since any divergence from these business processes is easy to track for compliance purposes.

Recently, several companies have identified huge benefits from putting RPA into practice to manage the workforce, quality and assurance, and risk management and compliance. For example, a high growth technology company located in California with $2 billion in revenues recently conducted an RPA Proof of Concept (POC) as part of a finance and accounting process transformation. The POC illustrated the ability to reduce costs by 10-15% (in part by eliminating contract workers and over time), improve controls (thanks to 100% auditability), compress cycle time (saving 5,000 hours) and redeploy talent to focus on value-added activities, such as focusing only on a few “red flagged” documents reviewed by the RPA bots, not the 95% of clean documents.

On an even larger scale, consider the experience of a top 5 U.S. bank. Executives there had aggressively pursued outsourcing and offshoring to lower costs for years but realized that those avenues were tapped out. In their search to implement the next stage of business process improvements, they commissioned an RPA POC around digitizing loan processing functions. The results were dramatic: an opportunity to compress activities by 60%, reduce headcount by 30%, and improve compliance and quality assurance.

RPA is not a brand new technology. It has existed for almost a decade, but until recently, it’s only been able to process very structured data. Within the last few years, however, the technology has improved to read more unstructured data. For example, it can now contextualize a PDF file to dissect and pull data. Now that RPA can better handle unstructured data, RPA adoption is on the upswing.

Looking forward, RPA vendors are planning to embed additional capabilities that will make RPA systems even more adept at handling unstructured data and non-rule-based activities, and to conduct root-cause analysis on transactional data.

What are your plans for RPA? Have you considered using this technology?




Chris Curran

Principal and Chief Technologist, PwC US Tel: +1 (214) 754 5055 Email

Anand Rao

Global Artificial Intelligence Lead, PwC US Tel: +1 (617) 530 4691 Email