What is Cognitive Automation? How It Can Transform Your Business AI-Powered Automation
Let’s take a look at how cognitive automation has helped businesses in the past and present. As cognitive technologies slowly mature, more and more data gets added to the system and it will help make more and more connections. Now the time is right for businesses to look at combining RPA with cognitive technologies to stay ahead of the competition. RPA uses technologies like screen scraping, workflow automation whereas Cognitive automation relies on technologies like OCR, ML and NLP. RPA provides immediate Return on Investment (ROI) whereas Cognitive automation takes more time for realization.
We won’t go much deeper into the technicalities of Machine Learning here but if you are new to the subject and want to dive into the matter, have a look at our beginner’s guide to how machines learn. Watch the case study video to learn about automation and the future of work at Pearson. “Cognitive automation multiplies the value delivered by traditional automation, with little additional, and perhaps in some cases, a lower, cost,” said Jerry Cuomo, IBM fellow, vice president and CTO at IBM Automation. “This is especially important now in the wake of the COVID-19 pandemic,” Kohli said. Not all companies are downsizing; some companies, such as Walmart, CVS and Dollar General, are hiring to fill the demands of the new normal.”
End-to-end customer service (Religare)
By augmenting human cognitive capabilities with AI-powered analysis and recommendations, cognitive automation drives more informed and data-driven decisions. Its systems can analyze large datasets, extract relevant insights and provide decision support. It mimics human behavior and intelligence to facilitate decision-making, combining the cognitive ‘thinking’ aspects of artificial intelligence (AI) with the ‘doing’ task functions of robotic process automation (RPA). Cognitive automation leverages different algorithms and technology approaches such as natural language processing, text analytics and data mining, semantic technology and machine learning.
When implemented strategically, intelligent automation (IA) can transform entire operations across your enterprise through workflow automation; but if done with a shaky foundation, your IA won’t have a stable launchpad to skyrocket to success. Sentiment analysis or ‘opinion mining’ is a technique used in cognitive automation to determine the sentiment expressed in input sources such as textual data. NLP and ML algorithms classify the conveyed emotions, attitudes or opinions, determining whether the tone of the message is positive, negative or neutral. Additionally, large RPA providers have built marketplaces so developers can submit their cognitive solutions which can easily be plugged into RPA bots. You can check our article where we discuss the differences between RPA and intelligent / cognitive automation. However, it is likely to take longer to implement these solutions as your company would need to find a capable cognitive solution provider on top of the RPA provider.
Cognitive RPA solutions by RPA ecosystem
Check out our RPA guide or our guide on RPA vendor comparison for more info. You can also learn about other innovations in RPA such as no code RPA from our future of RPA article. Semi-structured information such as invoices and unstructured data such as customer interactions can be analyzed, processed, and classified into useful data fields for the next steps of automation. In the real working world, more than 60% of data is either semi-structured or unstructured. The integration of different AI features with RPA helps organizations extend automation to more processes.
Image recognition refers to technologies that identify places, logos, people, objects, buildings, and several other variables in images. Facial recognition is used by security forces to counter crime and terrorism. Text recognition (OCR) transforms characters from printed /written or scanned documents into an electronic form to be further processed by computers or other software programs. Job application tracking system uses OCR to search through resumes for key words.
These areas include data and systems architecture, infrastructure accessibility and operational connectivity to the business. It imitates the capability of decision-making and functioning of humans. This assists in resolving more difficult issues and gaining valuable insights from complicated data. Deliveries that are delayed are the worst thing that can happen to a logistics operations unit. The parcel sorting system and automated warehouses present the most serious difficulty. They make it possible to carry out a significant amount of shipping daily.
Every time it notices a fault or a chance that an error will occur, it raises an alert. We serve over 2 million of the world’s top employee experience professionals. Join us today — unlock member benefits and accelerate your career, all for free. This renders the business by necessity subservient to and reliant on IT. However, he points out that there is a silver lining in that most of the research respondents expect their challenges to be resolved within the next three to five years. Organizations are understanding the tremendous value of automation and expect to see significant growth and investment in these initiatives moving forward.
One of the foremost challenges before cognitive automation adoption is organizations need to build a culture that encourages the human workforce to accept, adapt, and work alongside the digital workforce. According to IDC, AI use cases that will see the most investment this year are automated customer service agents, sales process recommendation and automation and automated threat intelligence and prevention systems. Task mining and process mining analyze your current business processes to determine which are the best automation candidates. They can also identify bottlenecks and inefficiencies in your processes so you can make improvements before implementing further technology. Cognitive automation has the potential to completely reorient the work environment by elevating efficiency and empowering organizations and their people to make data-driven decisions quickly and accurately. There was a time when the word ‘cognition’ was synonymous with ‘human’.
With the automation of repetitive tasks through IA, businesses can reduce their costs as well as establish more consistency within their workflows. The COVID-19 pandemic has only expedited digital transformation efforts, fueling more investment within infrastructure to support automation. Individuals focused on low-level work will be reallocated to implement and scale these solutions as well as other higher-level tasks.
Cognitive automation is a blending of machine intelligence with automation processes on all levels of corporate performance.
Knowing the differences can be quite important for CIOs, Taulli said. Traditional RPA usually has challenges with scaling and can break down under certain circumstances, such as when processes change. However, cognitive automation can be more flexible and adaptable, thus leading to more automation. RPA has been around for over 20 years and the technology is generally based on use cases where data is structured, such as entering repetitive information into an ERP when processing invoices. While they are both important technologies, there are some fundamental differences in how they work, what they can do and how CIOs need to plan for their implementation within their organization. The way RPA processes data differs significantly from cognitive automation in several important ways.
Cognitive automation involves incorporating an additional layer of AI and ML. TalkTalk received a solution from Splunk that enables the cognitive solution to manage the entire backend, giving customers access to an immediate resolution to their issues. Identifying and disclosing any network difficulties has helped TalkTalk enhance its network. As a result, they have greatly decreased the frequency of major incidents and increased uptime. The cognitive solution can tackle it independently if it’s a software problem.
Use case 5: Intelligent document processing
AI can help RPA automate tasks more fully and handle more complex use cases. RPA also enables AI insights to be actioned on more quickly instead of waiting on manual implementations. The concept alone is good to know but as in many cases, the proof is in the pudding.
There are a lot of use cases for artificial intelligence in everyday life—the effects of artificial intelligence in business increase day by day. With the help of AI and ML, it may analyze the problems at hand, identify their underlying causes, and then provide a comprehensive solution. RPA operates most of the time using a straightforward “if-then” logic since there is no coding involved. If any are found, it simply adds the issue to the queue for human resolution.
It deals with both structured and unstructured data including text heavy reports. These are the solutions that get consultants and executives most excited. Vendors claim that 70-80% of corporate knowledge tasks can be automated with increased cognitive capabilities. To deal with unstructured data, cognitive bots need to be capable of machine learning and natural language processing.
- You can think of RPA as “doing” tasks, while AI and ML encompass more of the “thinking” and “learning,” respectively.
- “Ultimately, cognitive automation will morph into more automated decisioning as the technology is proven and tested,” Knisley said.
- By using cognitive automation to improve customer service, businesses can increase customer satisfaction and loyalty.
Traditionally cognitive capabilities were the realm of data analytics and digitization. Robotic Process Automation (RPA) works best if you have a structured process, involves a large volume of data and is rule based. If this process involves complex, unstructured data that requires human intervention then Cognitive automation is the answer. Since cognitive automation can analyze complex data from various sources, it helps optimize processes. RPA imitates manual effort through keystrokes, such as data entry, based on the rules it’s assigned.
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