As businesses are striving hard to outperform each other, automation has been the trump card in the age of digital transformation. With automation technologies promising to take over most of the business processes across industries, organizations have started implementing Robotic Process Automation (RPA) to gain a competitive edge against their counterparts and reduce human effort. But with the introduction of AI-powered automation technology such as Cognitive Automation, organizations are starting to realize that RPA has become more powerful and mature.
In 2017, a report suggested that the highest investment within the artificial intelligence (AI) landscape would be on cognitive applications, forecasting the global corporate investment would reach $46 billion by 2021.
Since the rapid growth of investments in cognitive automation is so enticing, organizations are haunted with the million-dollar question, “Which automation technology would be the perfect match for my company.” Some of the other questions raised by the business leaders are:
- Are we heading the right path by implementing RPA?
- Should we opt for cognitive automation instead?
- If yes, how do we choose between RPA and cognitive automation?
- What are the functions and benefits cognitive automation offers?
Traditional RPA Isn’t Enough
RPA is a prominent technology that mimics redundant manual tasks with more precision and accuracy with the help of software bots. The technology has reaped major benefits to organizations across industries by automating several labor-intensive tasks that don’t require human involvement. It can also exponentially increase the productivity of employees, offering the luxury of being more profitable and innovative. However, as organizations RPA maturity rises, there are going to be a multitude of processes that demand human decision-making, and whenever there is voluminous data involved, organizations must look beyond RPA since the technology is devoid of such characteristics and that the human workforce might find them very challenging.
Automating simple tasks are only a part of the bigger picture. Implementing a technology that can automate unstructured data is the future of a successful automation journey.
Pain points of Conventional RPA
As mentioned earlier, since traditional RPA is only designed to perform simple, redundant human tasks, the technology is not well equipped to handle unstructured data and more complicated processes. Organizations must begin to look beyond the low hanging fruit to improve automation implementations and learn to utilize powerful cognitive technologies.
Document processing is one of the most critical tasks in any organization. Manually analyzing unstructured data such as forms and invoices can prove to be costly due to high chances of human errors, time-consumption, and a major hurdle for automation. Moreover, the luxury of automating these tasks will turn out to have a drastic impact on the company’s efficiency.
Unstructured Data vs Structured Data
Conventional RPA has no troubles in processing structured data since it analyzes data that the data is ordered and labeled in a way that is easily readable by machines. The best example of structured data is the online input forms generated by computers and automated systems.
As for unstructured data, there are many examples such as text, images, PDFs, scanned documents, and natural language input. Since traditional RPA isn’t equipped for analyzing these data, it requires human intervention to convert unstructured data to structured data. Conventional RPA is also not a perfect fit for paper documents with potential inputs such as invoices, customer emails, and verbal language. This leaves a huge dent in a wide range of front-office and back-office tasks. Analyzing unstructured data requires something more than just a basic RPA tool for organizations to reap the highest benefits.
Image recognition essentially means capturing data from handwritten documents, images, and scanned copies. However, the traditional RPA technology is only designed to automate simple, rules-based tasks, companies need a human hand to capture the data from all these documents.
Sales and Supply Chain Forecasts
Predicting the sales and supply chain trends is instrumental for a business to analyze their investments and financial strategies for the future. Although sales and supply chain forecasts involve huge amounts of voluminous data, conventional RPA is not the best fit for the finance leaders to rely on. Organizations need technology that offers decision-making capabilities and human-like thoughts.
Delivering Humanoid Robots
Artificial intelligence is an essential technology where the bots can understand the natural language and act like humans. The beneficial fusion of AI and RPA allows automating tasks of voluminous data and process the unstructured data where conventional RPA can’t.
RPA relies on basic technologies such as screen scraping, macro scripts, and workflow automation, whereas, cognitive automation leverages high-end technologies such as natural language processing (NLP), text analytics, data mining, and machine learning to make the jobs of human workforce easier by making informed business decisions. Cognitive automation, unlike RPA, requires extensive use of programming language to make the best use of machine learning. The technology goes through several human-like conversations to better understand human behavior and decision making.
Benefits of Cognitive Automation:
- 100% accuracy on unstructured data
- >90% accuracy in image/speech recognition
- 24/7 Availability on Prediction/ Fraud Prevention
- Much higher employee productivity
- 50% faster and efficient than RPA
Key Capabilities of Cognitive Automation
Natural Language Processing (NLP): Faster TAT by automating customer service processes through basic language understanding.
Optical Character Recognition (OCR): Helps in automating documents such as images, handwritten forms, and scanned copies. OCR creates a huge impact in industries with higher document-oriented tasks. Invoice processing with RPA and OCR improves TAT and reduces costs.
Machine learning: The machine learning algorithms account for the decision-making capabilities of the technology. Decisions are made through the data patterns generated by the algorithms and learnings from the past data to understand human language.
So, should organizations choose RPA or cognitive automation? The answer is not about choosing one over the other. As RPA and cognitive automation fall under the same automation continuum, our advice would be to start small. Organizations that are still at the beginning of their automation journey can gradually upgrade from RPA and work their way up the ladder to implement cognitive automation.
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