Language RPA: The coming of age for language technologies

Although the technologies that make up what we today call RPA are not new, the importance of this business process automation paradigm has been strengthened by a rise of specialized Artificial Intelligence (AI), and its association with the predicted fourth Industrial Revolution. A decade ago RPA was mainly a “screen scraping” process that could automate only very specific tasks. Now RPA encompasses virtually any human process that can be automated through software. For one company, RPA could mean intelligent routing of customer support emails or digitization and storage of physical invoices, for another could mean interfacing legacy software systems or transforming content throughout its lifecycle in the organization.  

As RPA is getting more specialized and more ubiquitous, it tends to absorb business application areas where the automation value was clear only to a certain market segment, but which is now relevant to entire industries. And working with content is one area that RPA is starting to have major impact on, although not by bringing any innovation by itself, but by repurposing and showing new increased value to processes that already have a history in certain areas.

This is exactly the case of Language RPA, which you haven’t heard about until now, because… it simply wasn’t a coined term. If you were working for the localization department for multinational, you might have been familiar with language-specific automation software areas, such as translation management or translation productivity, that automated project management and content workflow transformation and helped content to be translated and delivered faster. If you were working in the data analytics area, performing triage on multi-language content was done using a combination of text analytics and machine translation. But the industry never referred to this type of content-centric (or language-centric) automation as RPA.

Automation in the commercial space has already gained traction with the help of big RPA players, such as UiPath, BluePrims, PegaSystems, NICE or Kofax. The rule of thumb of the all RPA-based digital transformation efforts is to identify non-strategic human activities that can be automated, from sales, R&D, finance to employee management and customer service. The outcome is efficiency, cost saving, process improvement and repurposing employees for more interesting work.

Language RPA, a subset of Content RPA (if you will), is not a recent innovation, but it is just as important as automating the accounts payable activities, claim processing, contact center, HR or Finance and Accounting. With the recent advancements in AI and processing power, content can be created, managed, transformed and distributed for consumption with high levels of automation.

Natural Language Generation (NLG) has proven as an effective approach to create content from existing structured and unstructured content sources that have previously created by humans.

Content can then be translated either completely automated with Neural MT, for certain use cases, or it can be handled by human translation teams in a highly automated manner, by relying on translation management systems that can automate most of the project management tasks. While, at the same time, eliminating most of the redundant human workflow steps and transforming content with the use of file filters, translation memories or machine translation.

And finally, content can be automatically understood with the use of Text Analytics and delivered to its audience in an intelligent manner, in the desired format, language and writing style, in order to have an end-to-end approach to the content lifecycle that involves the highest amount of automation possible.

Language RPA positions existing and new approaches to handling language-specific tasks in the context of the fourth industrial revolution, offering an undeniable new approach to workforce repurposing and full utilization of content in all its forms.