Cognitive Automation: Augmenting Bots with Intelligence

According to IDC, in 2017, the largest area of AI spending was cognitive applications. This includes applications that automate processes that automatically learn, discover, and make recommendations or predictions. Overall, cognitive software platforms will see investments of nearly $2.5 billion this year. Spending on cognitive-related IT and business services will be more than $3.5 billion and will enjoy a five-year CAGR of nearly 70%.

One organization he has been working with predicted nearly 35% of its workforce will retire in the next five years. They are looking at cognitive automation to help address the brain drain that they are experiencing. “With cognitive automation, CIOs can move the needle to high-value, high-frequency automations and have a bigger impact on the bottom line,” said Jon Knisley, principal of automation and process excellence at FortressIQ. These areas include data and systems architecture, infrastructure accessibility and operational connectivity to the business.

How does cognitive automation work?

Accounting departments can also benefit from the use of cognitive automation, said Kapil Kalokhe, senior director of business advisory services at Saggezza, a global IT consultancy. For example, accounts payable teams can automate the invoicing process by programming the software bot to receive invoice information — from an email or PDF file, for example — and enter it into the company’s accounting system. In this example, the software bot mimics the human role of opening the email, extracting the information from the invoice and copying the information into the company’s accounting system.

Exponential Digital Solutions (10xDS) is a new age organization where traditional consulting converges with digital technologies and innovative solutions. We are committed towards partnering with clients to help them realize their most important goals by harnessing a blend of automation, analytics, AI and all that’s “New” in the emerging exponential technologies. 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. 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.

• IA or cognitive automation has a ton of real-world applications across sectors and departments, from automating HR employee onboarding and payroll to financial loan processing and accounts payable.
• This includes applications that automate processes that automatically learn, discover, and make recommendations or predictions.
• It can carry out various tasks, including determining the cause of a problem, resolving it on its own, and learning how to remedy it.

This allows cognitive automation systems to keep learning unsupervised, and constantly adjusting to the new information they are being fed. Difficulty in scaling. While RPA can perform multiple simultaneous operations, it can prove difficult to scale in an enterprise due to regulatory updates or internal changes. You can foun additiona information about ai customer service and artificial intelligence and NLP. According to a Forrester report, 52% of customers claim they struggle with scaling their RPA program. A company must have 100 or more active working robots to qualify as an advanced program, but few RPA initiatives progress beyond the first 10 bots. To learn more about what’s required of business users to set up RPA tools, read on in our blog here.

What are the uses of cognitive automation?

These technologies allow cognitive automation tools to find patterns, discover relationships between a myriad of different data points, make predictions, and enable self-correction. By augmenting RPA solutions with cognitive capabilities, companies can achieve higher accuracy and productivity, maximizing the benefits of RPA. Cognitive process automation starts by processing various types of data, including text, images, and sensor data, using techniques like natural language processing and machine learning. In contrast, cognitive automation or Intelligent Process Automation (IPA) can accommodate both structured and unstructured data to automate more complex processes. One of their biggest challenges is ensuring the batch procedures are processed on time.

Cognitive automation is the structuring of unstructured data, such as reading an email, an invoice or some other unstructured data source, which then enables RPA to complete the transactional aspect of these processes. AIMultiple informs hundreds of thousands of businesses (as per similarWeb) including 60% of Fortune 500 every month. Cem’s work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms cognitive automation definition like Deloitte, HPE, NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and media that referenced AIMultiple. Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised businesses on their enterprise software, automation, cloud, AI / ML and other technology related decisions at McKinsey & Company and Altman Solon for more than a decade.

With AI, organizations can achieve a comprehensive understanding of consumer purchasing habits and find ways to deploy inventory more efficiently and closer to the end customer. When introducing automation into your business processes, consider what your goals are, from improving customer satisfaction to reducing manual labor for your staff. Consider how you want to use this intelligent technology and how it will help you achieve your desired business outcomes. IA is capable of advanced data analytics techniques to process and interpret large volumes of data quickly and accurately.

While automation is old as the industrial revolution, digitization greatly increased activities that could be automated. However, initial tools for automation, which includes scripts, macros and robotic process automation (RPA) bots, focus on automating simple, repetitive processes. However, as those processes are automated with the help of more programming and better RPA tools, processes that require higher level cognitive functions are next in the line for automation.

You can also check our article on intelligent automation in finance and accounting for more examples. Cognitive automation leverages different algorithms and technology approaches such as natural language processing, text analytics and data mining, semantic technology and machine learning. Through cognitive automation, enterprise-wide decision-making processes are digitized, augmented, and automated. Once a cognitive automation platform understands how to operate the enterprise’s processes autonomously, it can also offer real-time insights and recommendations on actions to take to improve performance and outcomes.

Regulatory compliance and risk management

Cognitive automation allows building chatbots that can make changes in other systems with ease. Levity is a tool that allows you to train AI models on images, documents, and text data. You can rebuild manual workflows and connect everything to your existing systems without writing a single line of code.‍If you liked this blog post, you’ll love Levity. The concept alone is good to know but as in many cases, the proof is in the pudding.

Customers submit claims using various templates, can make mistakes, and attach unstructured data in the form of images and videos. Cognitive automation can optimize the majority of FNOL-related tasks, making a prime use case for RPA in insurance. Ability to analyze large datasets quickly, cognitive automation provides valuable insights, empowering businesses to make data-driven decisions. This leads to better strategic planning, reduced risks, and improved outcomes. Businesses are increasingly adopting cognitive automation as the next level in process automation.

• To learn more about what’s required of business users to set up RPA tools, read on in our blog here.
• But at the end of the day, both are considered complementary rather than competitive approaches to addressing different aspects of automation.
• AI models require extensive training in order to produce an algorithm that is highly optimized to perform one task.
• Automation will expose skills gaps within the workforce and employees will need to adapt to their continuously changing work environments.
• AI is still at its infancy, it learns by example, most technologies like NLP, OCR or ML has not yet been perfected or matured, this leaves room for error and require close attention.
• Currently there is some confusion about what RPA is and how it differs from cognitive automation.

Intelligent/cognitive automation tools allow RPA tools to handle unstructured information and make decisions based on complex, unstructured input. Cognitive automation (also called smart or intelligent automation) is an emerging field that augments RPA tools with artificial intelligence (AI) capabilities like optical character recognition (OCR) or natural language processing (NLP). It deals with both structured and unstructured data including text heavy reports. These are the solutions that get consultants and executives most excited.

In the incoming decade, a significant portion of enterprise success will be largely attributed to the maturity of automation initiatives. Thinking about cognitive automation as a business enabler rather than a technology investment and applying a holistic approach with clearly defined goals and vision are fundamental prerequisites for cognitive automation implementation success. Itransition offers full-cycle AI development to craft custom process automation, cognitive assistants, personalization and predictive analytics solutions.

Cognitive RPA solutions by RPA ecosystem

Traditional RPA is mainly limited to automating processes (which may or may not involve structured data) that need swift, repetitive actions without much contextual analysis or dealing with contingencies. In other words, the automation of business processes provided by them is mainly limited to finishing tasks within a rigid rule set. That’s why some people refer to RPA as “click bots”, although most applications nowadays go far beyond that. RPA imitates manual effort through keystrokes, such as data entry, based on the rules it’s assigned. But combined with cognitive automation, RPA has the potential to automate entire end-to-end processes and aid in decision-making from both structured and unstructured data. Additionally, while robotic process automation provides effective solutions for simpler automations, it is limited on its own to meet the needs of today’s fast-paced world.

Cognitive automation will enable them to get more time savings and cost efficiencies from automation. “Ultimately, cognitive automation will morph into more automated decisioning as the technology is proven and tested,” Knisley said. He observed that traditional automation has a limited scope of the types of tasks that it can automate.

Through this data analysis, cognitive automation facilitates more informed and intelligent decision-making, leading to improved strategic choices and outcomes. It streamlines operations, reduces manual effort, and accelerates task completion, thus boosting overall efficiency. CIOs are now relying on cognitive automation and RPA to improve business processes more than ever before.

cognitive automation use cases in the enterprise

With light-speed jumps in ML/AI technologies every few months, it’s quite a challenge keeping up with the tongue-twisting terminologies itself aside from understanding the depth of technologies. To make matters worse, often these technologies are buried in larger software suites, even though all or nothing may not be the most practical answer for some businesses. There are a lot of use cases for artificial intelligence in everyday life—the effects of artificial intelligence in business increase day by day.

Newer technologies live side-by-side with the end users or intelligent agents observing data streams — seeking opportunities for automation and surfacing those to domain experts. RPA tools without cognitive capabilities are relatively dumb and simple; should be used for simple, repetitive business processes. Automated processes can only function effectively as long as the decisions follow an “if/then” logic without needing any human judgment in between.

Cognitive automation is responsible for monitoring users’ daily workflows. It identifies processes that would be perfect candidates for automation then deploys the automation on its own, Saxena explained. Cognitive automation is more expensive and may take longer to implement than traditional RPA tools in specific scenarios. AI models require extensive training in order to produce an algorithm that is highly optimized to perform one task. KlearStack is an AI-based platform that achieves intelligent data extraction from unstructured documents.

Faster processes and shorter customer wait times—that’s the brilliance of AI-powered automation. Another important use case is attended automation bots that have the intelligence to guide agents in real time. RPA uses technologies like screen scraping, workflow automation whereas Cognitive automation relies on technologies like OCR, ML and NLP.

“A human traditionally had to make the decision or execute the request, but now the software is mimicking the human decision-making activity,” Knisley said. “Cognitive automation, however, unlocks many of these constraints by being able to more fully automate and integrate across an entire value chain, and in doing so broaden the value realization that can be achieved,” Matcher said. It gives businesses a competitive advantage by enhancing their operations in numerous areas.

The next step is, therefore, to determine the ideal cognitive automation approach and thoroughly evaluate the chosen solution. Cognitive automation has proven to be effective in addressing those key challenges by supporting companies in optimizing their day-to-day activities as well as their entire business. New insights could be revealed thanks to cognitive computing’s capacity to take in various data properties and grasp, analyze, and learn from them. These prospective answers could be essential in various fields, particularly life science and healthcare, which desperately need quick, radical innovation. 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.

This approach led to 98.5% accuracy in product categorization and reduced manual efforts by 80%. Seetharamiah added that the real choice is between deterministic and cognitive. “Go for cognitive automation, if a given task needs to make decisions that require learning and data analytics, for example, the next best action in the case of the customer service agent,” he told Spiceworks. Whether it be RPA or cognitive automation, several experts reassure that every industry stands to gain from automation. According to Saxena, the goal is to automate tedious manual tasks, increase productivity, and free employees to focus on more meaningful, strategic work. “RPA and cognitive automation help organizations across industries to drive agility, reduce complexity everywhere, and accelerate value of technology investments across their business,” he added.

“Cognitive RPA is adept at handling exceptions without human intervention,” said Jon Knisley, principal, automation and process excellence at FortressIQ, a task mining tools provider. 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. And if you are planning to invest in an off-the-shelf RPA solution, scroll through our data-driven list of RPA tools and other automation solutions. Additionally, large RPA providers have built marketplaces so developers can submit their cognitive solutions which can easily be plugged into RPA bots. These are just two examples where cognitive automation brings huge benefits.

Cognitive automation enhances the customer experience by providing accurate responses, round-the-clock support, and personalized interactions. This results in increased customer satisfaction, loyalty, and a positive brand image, ultimately leading to business growth and a competitive advantage in the market. It uses AI algorithms to make intelligent decisions based on the processed data, enabling it to categorize information, make predictions, and take actions as needed. Consider you’re a customer looking for assistance with a product issue on a company’s website.

But at the end of the day, both are considered complementary rather than competitive approaches to addressing different aspects of automation. While chatbots are gaining popularity, their impact is limited by how deeply integrated they are into your company’s systems. For example, if they are not integrated into the legacy billing system, a customer will not be able to change her billing period through the chatbot.

In this case, cognitive automation takes this process a step further, relieving humans from analyzing this type of data. Similar to the aforementioned AML transaction monitoring, ML-powered bots can judge situations based on the context and real-time analysis of external sources like mass media. Since cognitive automation can analyze complex data from various sources, it helps optimize processes.

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. Intelligent automation streamlines processes that were otherwise composed of manual tasks or based on legacy systems, which can be resource-intensive, costly and prone to human error. The applications of IA span across industries, providing efficiencies in different areas of the business.