RPA developers have long been limited in the processes where they can achieve end-to-end automations. Fortunately, NLP has matured as a technology and now provides devs with an accurate and reliable solution to extend automation into even the most opaque manual processes.
To ensure customers receive the best experiences and highest returns, end-to-end process automation has become the ultimate goal of RPA. Yet is this even possible for the majority of processes business users deal with on a daily basis?
A business process is usually made up of both structured and unstructured workflows. While RPA has paid dividends for the former, RPA devs still struggle to make a dent in the hundreds of manual unstructured processes that comprise a business.
The last barrier to end-to-end process automation
Access to structured data frequently blocks good processes from being automated. Email is a significant culprit, but is one of many problematic data sources; ticketing systems, chats, CRM note-entries all block RPA devs from delivering successful end-to-end automations.
The problem is unstructured data. These manual, communications-based tasks aren’t based on the structured data flows we usually rely on for automation.
An RPA bot can’t parse an email between two colleagues, even when supported by added tools like IDP. Even seemingly simple tasks, like reading an email or asking a customer for more information, require an understanding of freeform natural language which, until recent years, only humans have been able to provide.
What’s more, without real-time structured data, quantifying and measuring the success of automation projects becomes a static and labour intensive task.
As a result, RPA devs have been forced to focus on the easier and lower value automations that are available in their pipelines. This is instead of the high-value processes revolving around natural language, which can provide significantly more time back to the business and improved CX.
The unfortunate outcome is unfulfilled potentiation; where automation programmes are only half-complete and only generate a fraction of the savings possible. It’s the unstructured processes where most delays happen, and where most time and resources are wasted. Email alone costs businesses $5,000 to £10,000 per employee each year depending on the size of the company.
Huge automation opportunities are available in manual communications-based processes, but RPA devs have so far lacked the tools needed to realise them.
How NLP enables RPA
Fortunately, natural language processing (NLP) has finally matured as a technology.
It used to be the case that NLP models could prove unreliable, that their performance was hard to measure, and their value difficult to prove. But recent advances - including the use of transformers, the development of low-code and Active Learning methodologies - have made it much faster to train, deploy and create value from NLP.
NLP is now sophisticated enough to accurately process and understand human language in real time. This creates some very useful applications for automation teams:
Process discovery and definition
Traditionally, processes have been discovered by interviewing process SMEs, which has produced good results in the past. Yet, all too often important information - like additional processes - is forgotten or isn’t communicated properly, leading to flawed automations that fail to make an impact.
The value of NLP is that it gives RPA devs another avenue to go down for that insight. Clustering and unsupervised learning techniques have made finding new issues and opportunities in unstructured data much faster. When deployed across a business’s comms channels, an NLP solution can reveal the common issues and manual processes otherwise hidden or forgotten.
Automation, analytics and reporting
Manual comms tasks, like email processing, have always lacked the structured data needed for automation. This is probably the most important application of NLP - It reliably converts unstructured comms data into structured data that’s machine-readable and ready for automation. RPA devs now have the structured data they need to build software bots for even the most complex comms processes.
For the first time, RPA devs are able to automate business processes from end-to-end. They’re no longer limited to the structured workflows that comprise the tail-end of a process. Where data is sparse, RPA devs can now create their own using NLP.
Crucially, this also means that developers have the data they need to analyse automation performance, measure system exceptions, and prove the value their automations are driving for the business.
Conversational Data Intelligence
NLP has become a critical tool and enabler of RPA. It’s created an entirely new category of structured data that devs can analyse and use for automation. For the first time, comms-based manual processes can be automated, creating new opportunities and extending the scope and value of automation projects as a whole.
However, NLP solutions are still difficult to build and train up from scratch. Few RPA devs will have the time or resources to develop proprietary NLP models specialised to their clients’ needs.
That’s why the best way to deploy NLP for RPA is through an integrated platform that’s specially built for communications data. One that combines machine learning and NLP to build and train models rapidly, and which delivers real-time visibility into all enterprise comms channels. That's why organisations need Conversational Data Intelligence.
Begin your free trial of the Re:infer Conversational Data Intelligence Platform to see how this can be achieved.