Robotics and Cognitive: How are They Applied in Business Process Automation?

Every time it notices a fault or a chance that an error will occur, it raises an alert. Managing all the warehouses a business operates in its many geographic locations is difficult. Some of the duties involved in managing the warehouses include maintaining a record of all the merchandise available, ensuring all machinery is maintained at all times, resolving issues as they arise, etc. The scope of automation is constantly evolving—and with it, the structures of organizations. In a hospital setting, RPA can count the number of patients in a ward or with a particular diagnosis.

RPA functions similarly to a data operator, working with standardized data. Also, only when the data is in a structured or semi-structured format can it be processed. Any other format, such as unstructured data, necessitates the use of cognitive automation. Cognitive automation also creates relationships and finds similarities between items through association learning. But, there will be many situations in which human decision-making is required. Also, when large amounts of data are there, it can be difficult for the human workforce to make the best decisions.

What are the uses of cognitive automation?

Typically, the Availability to Promise (ATP) process runs an Enterprise Resource Planning (ERP) system when there is a new order. As read above, intelligent automation comprises three cognitive technologies that empower businesses cognitive automation examples with a solution that enables digital transformation and improve customer experience. This data can also be easily analyzed, processed, and structured into useful data for the next step in the business process.

• Although cognitive solutions can unlock data value and provide unique insights, automation makes it possible for CSPs to scale-up and address data diversity, volume and free technical resources to focus on exceptions.
• Like our brains’ neural networks creating pathways as we take in new information, cognitive automation makes connections in patterns and uses that information to make decisions.
• Traditional RPA without IA’s other technologies tends to be limited to automating simple, repetitive processes involving structured data.
• This results in improved efficiency and productivity by reducing the time and effort required for tasks that traditionally rely on human cognitive abilities.
• You can use RPA to perform mundane, repetitive tasks, while cognitive automation simulates the human thought process to discover, learn and make predictions.
• Leverage public records, handwritten customer input and scanned documents to perform required KYC checks.

If you want a system that performs a simple daily task, intelligent RPA is your man with preset rules. However, if you want a complex system that can handle unstructured data and requires accurate decisions, you should use cognitive intelligence. RPA has helped organizations reduce back-office costs and increase productivity by performing daily repetitive tasks with greater precisions. Tasks can be automated with intelligent RPA; cognitive intelligence is needed for tasks that require context, judgment, and an ability to learn.

Customer experience and engagement

An organization invests a lot of time preparing employees to work with the necessary infrastructure. Asurion was able to streamline this process with the aid of ServiceNow‘s solution. The Cognitive Automation system gets to work once a new hire needs to be onboarded. Like our brains’ neural networks creating pathways as we take in new information, cognitive automation makes connections in patterns and uses that information to make decisions.

As a result,  manufacturers can keep track of the health of their equipment in real-time, predict machine failures, set and update maintenance schedules, and alert staff when maintenance is required. SS&C Blue Prism enables business leaders of the future to navigate around the roadblocks of ongoing digital transformation in order to truly reshape and evolve how work gets done – for the better. A well-designed flexible product, plenty of pre-built models, generalized transformations and evaluation processes.

On the other hand, cognitive automation learns the context from the data using patterns. Over time, the system can eliminate the need for human intervention and can function independently, just like a human does. An example of intelligent automation would be using machine learning to analyze historical and real-time workload and compute data. An intelligent automation platform could then manage workloads to optimize runtimes and prevent delays, while provisioning and deprovisioning virtual machines to meet real-time demand. This approach ensures end users’ apprehensions regarding their digital literacy are alleviated, thus facilitating user buy-in.

These are complemented by other technologies such as analytics, process orchestration, BPM, and process mining to support intelligent automation initiatives. Our team of experts comprises software developers, statisticians, data analytics and cognitive computing experts. When it comes to choosing between RPA and cognitive automation, the correct answer isn’t necessarily choosing one or the other. Generally, organizations start with the basic end using RPA to manage volume and work their way up to cognitive and automation to handle both volume and complexity. 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. Make automated decisions about claims based on policy and claim data and notify payment systems.

What are the different types of RPA in terms of cognitive capabilities?

Let us understand what are significant differences between these two, in the next section. Although cognitive solutions can unlock data value and provide unique insights, automation makes it possible for CSPs to scale-up and address data diversity, volume and free technical resources to focus on exceptions. Implementing a full Cognitive Automation solution means building an autonomous or semi-autonomous system, composed of specific building blocks, critical to drive Artificial Intelligence and robotic action. Robotic process automation guarantees an immediate return on investment. Since intelligent RPA performs tasks more accurately than humans and is involved in day-to-day tasks, organizations immediately experience their effect on production.

So now it is clear that there are differences between these two techniques. For example, a financial institution could use automation to analyze customer data and identify trends in spending habits, leading to the development of new financial products and services. Cognitive processes, also called cognitive functions, include basic aspects such as perception and attention, as well as more complex ones, such as thinking. Any activity we do, e.g., reading, washing the dishes or cycling, involves cognitive processing. Let’s see some of the cognitive automation examples for a better understanding. Cognitive automation represents a range of strategies that enhance automation’s ability to gather data, make decisions, and #scale automation.

It is a proven technology used across various industries – be it finance, retail, manufacturing, insurance, telecom, and beyond. It is a software technology that allows anyone to automate digital tasks. These bots can learn, mimic, and then execute business processes based on rules.

These prospective answers could be essential in various fields, particularly life science and healthcare, which desperately need quick, radical innovation. It now has a new set of capabilities above RPA, thanks to the addition of AI and ML. Some of the capabilities of cognitive automation include self-healing and rapid triaging. Cognitive automation can uncover patterns, trends and insights from large datasets that may not be readily apparent to humans. With these, it discovers new opportunities and identifies market trends.