Automation has become a necessity rather than a luxury over the past 18 months, igniting heated board-level discussions as many organizations grapple with where to begin and which type of automation to invest in.
Two of the most talked about types of automation are robotic process automation (RPA) and cognitive automation. But, what’s the difference between the two? What does each do? How do they work? What benefits do they provide? And which one is right for you?
Let’s take a look at them both to better understand the role RPA and cognitive automation play in helping businesses retain their competitive edge.
Much of the confusion around RPA and cognitive automation stems from the fact that they are closely related and, in many organizations, work hand in hand. Indeed, the two technologies complement each other and are two ends of the same thing: the intelligent automation continuum.
At the starting point of this continuum, RPA performs basic and repetitive rule-based business processes, sprinting through routine tasks that human workers find tedious, such as data entry, claims processing, resume scanning and order processing. RPA most likely also sent the reminder email or text alert you received before your last dental appointment.
Cognitive automation is at the other end of the continuum, blending specific artificial intelligence (AI) techniques to manage and analyze huge volumes of information in a way that mimics human thought and decision making around complex business rules. It builds on the speed, accuracy and consistency of RPA to bring intelligence and continuous learning to information-intensive processes by recognizing patterns, learning from experience and adapting.
Put simply, RPA is the doer, or the hands and legs, and cognitive automation is the thinker and decision maker, or nervous system and brain.
RPA and cognitive automation have distinct business applications. RPA takes advantage of data that is well organized and fits a recognized structure to speed through basic process-orientated tasks. This makes it a good fit for simple back-office processes and transactions that skilled workers find dreary and sometimes get wrong, such as stock reporting, invoice dispatch, credit card reconciliation or refund processes.
In the banking and finance industries, for example, RPA handles many labor-intensive and data-sensitive retail branch activities, underwriting and loan processes, and anti-money laundering and Know Your Customer checks.
Cognitive automation deals with what’s left, namely the mountains of unstructured data organizations handle every day, like email, documents, social media feeds, digital pictures and videos, speech, sensors used to gather climate information, etc., and unstructured content from the web. It learns by finding similarities between different unstructured data and then makes connections by creating tags, annotations and other metadata.
What’s more, add a new data set and cognitive automation creates more connections, allowing it to keep learning and make adjustments without human supervision. All of which makes it ideal for automating nonroutine tasks that require human cognitive capabilities around communication, perception and judgement. It’s cognitive automation, for example, that enables unstructured information from customer interactions to be easily analyzed, processed and structured into data that can be used for predictive analytics.
As RPA is process orientated it relies on basic technologies like macro scripts and workflow automation that require little or no coding. Typically, this also makes it quick and easy to implement and understand.
Cognitive automation, on the other hand, is a data-driven, knowledge-based approach that uses complex and advanced AI technologies like natural language processing, text analytics, data mining, semantic technology, and machine learning. This type of automation can be operational in a few weeks, and is designed to be used directly by business users with no input from data scientists or IT. However, in a nascent project, the technology can take time to train.
RPA and cognitive automation offer different ways to take care of mundane tasks, leaving staff free to focus on what humans do best. RPA’s main advantage is its speed, accuracy and consistency when compared to human workers. It can also be cheap to implement.
Cognitive automation goes one step further, extending workers’ analytical capabilities, which when scaled across an organization fire up big ideas that fuel business growth. Although, return on investment can initially be slow.
As we’ve seen, RPA and cognitive automation are poised to change the world of work as we know it, unlocking new and exciting possibilities around technology working alongside people.
So, which one should you choose?
Clearly, each type of automation is the right solution for the right scenario using the right data – structured or unstructured. But, of course, it’s likely that the best solution may be to use a mix of both. For example, smart bots for digital storefronts often use RPA to collect a customer’s account details, check current stock levels and delivery options, while cognitive automation handles the context of the customer’s conversation and personalizes, or humanizes, the response.
Faced with such choices, organizations typically start with RPA – to solve the problem of too much data – before moving on to cognitive automation to ease the headache of more complex, unstructured data. Either way, get your automation right and you too could be enhancing customer experience and staff productivity while cutting operational costs and risk.
