Cognitive Automation: What is the buzz around this term?

Organizations across the world are sprinting ahead with 'Digital Transformation' as one of the core business strategies, courtesy – COVID -19. As the race to outperform competition heats up among businesses, automation is taking over many processes. However, there are many types of automation technologies present in the market such as Robotic Process Automation (RPA), cognitive automation etc.. This makes it difficult for the organizations to decide which technology to invest in. So, RPA isn't a new term when we talk about automation, but “Cognitive Automation” is the latest buzz word.

What is Cognitive Automation?

Thanks to recent advancements in Artificial Intelligence (AI) and Machine Learning (ML), the typical automation process has evolved from bots handling the rule-based and redundant processes to more cognitive technology.

The fusion of AI/ML technologies and RPA is known as intelligent or cognitive automation. It exists at the crossroads between AI/ML and RPA. Technologies such as AI and ML can arm bots with cognitive capabilities and even decision-making. Simply put, RPA is the doer and cognitive is the decision engine.

How is it different from RPA?

Automation is as old as the industrial revolution. Till early 2000, whenever we used to talk about automation, we thought about industrial processes and machinery. But the last couple of decades saw the birth of the next level of automation in the form of RPA. It helped in automating mundane and clerical processes. RPA is a virtual workforce wherein companies use software programs or bots to reduce human involvement in labour-intensive tasks. It is one of the simplest ways to automate rule-based and repetitive tasks.

However, the challenge with RPA is that it is capable of performing simple rule-based tasks. These include visiting websites, opening applications, copy/pasting data etc. But it is extremely difficult (if not impossible) to implement RPA for complex processes or workflows. Moreover, automation will fail whenever there is any change in the interface, process, or application because RPA bots aren’t capable of responding to changes in the system without human interaction. Bots can’t figure out what to do if you input bad or missing data. Don't confuse RPA with intelligence as most people do (thanks to the use of the term ‘Robotic’!) and which is where cognitive automation steps in!

Cognitive automation is a knowledge-based approach that mimics human thoughts/actions to manage and analyse large volumes of data that requires continual learning capabilities like humans to process. It uses specific AI-techniques that mimic the way humans think to analyse and process complex or unstructured data. The key difference between RPA and cognitive automation is that RPA depends on basic technologies such as screen scraping, macro scripts etc., while cognitive automation relies on more advanced technologies such as Machine Learning (ML), Natural Language Processing (NLP), data mining etc. to make informed business decisions.

Benefits of Cognitive Automation over RPA

• Ability to process unstructured and complex data

• Higher accuracy in image/speech recognition

• Faster and efficient than RPA

• Cuts down data processing time by up to 60%

So, should businesses invest in cognitive automation instead of RPA?

There is no definite answer to that question. Rather, the right answer is not necessarily choosing one or the other as RPA and cognitive automation are two ends of the same continuum. Typically, every organization has a mix of simple and complex tasks. So, ideally, an organization's journey should start with automating simple tasks using standard RPA technique. Then it should gradually progress towards more complex tasks using cognitive automation. We should note that the advantages of RPA override its limitations and it usually provides an immediate ROI. Whereas, cognitive automation may take a while to yield results as it involves learning the human behaviour and language to interpret and automate the data.