With three years of experience in the IT industry, I’ve been on a continuous journey of professional growth and skill development. My expertise lies in Power Apps and Automate, where I’ve had the privilege of contributing to multiple successful projects. Look at the robotic arms in assembly lines, such as automotive industry.
RPA is a technology that uses software robots to mimic repetitive human tasks with great precision and accuracy. RPA is also ideal for processes that do not need human intervention or decision-making. It’s as simple as pressing the record, play, and stop buttons and dragging and dropping files around.
In a Gartner survey, 81% of marketers agreed their companies compete entirely based on customer experience. Cognitive automation can help organizations to provide faster and more efficient customer service, reducing wait times and improving overall satisfaction. Additionally, by leveraging cognitive automation examples machine learning and natural language processing, organizations can provide personalized and tailored customer experiences, improving engagement and loyalty. This can translate into new revenue opportunities through repeat business and positive word-of-mouth recommendations.
The organization can use chatbots to carry out procedures like policy renewal, customer query ticket administration, resolving general customer inquiries at scale, etc. 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. AI and ML are fast-growing advanced technologies that, when augmented with automation, can take RPA to the next level.
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. Cognitive automation is the current focus for most RPA companies’ product teams. So, with the advances in AI, robotic-automation-industry vendors start utilizing artificial intelligence technologies to boost RPA bots with the cognitive capabilities.
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. Cognitive automation involves incorporating an additional layer of AI and ML.
Splunk has helped Bookmyshow with a cognitive automation solution to help them improve their customer interactions. Digitate’s ignio, a cognitive automation solution helps handle the small niggles in the system to ensure that everything keeps working. In case of failures in any section, the cognitive automation solution checks and resolves the issue.
This frees up medical staff to focus on patient care, leading to better health outcomes for patients. Through cognitive automation, it is possible to automate most of the essential routine steps involved in claims processing. These tools can port over your customer data from claims forms that have already been filled into your customer database. It can also scan, digitize, and port over customer data sourced from printed claim forms which would traditionally be read and interpreted by a real person.
CIOs are now relying on cognitive automation and RPA to improve business processes more than ever before. These tasks can range from answering complex customer queries to extracting pertinent information from document scans. Some examples of mature cognitive automation use cases include intelligent document processing and intelligent virtual agents. The biggest challenge is that cognitive automation requires customization and integration work specific to each enterprise. This is less of an issue when cognitive automation services are only used for straightforward tasks like using OCR and machine vision to automatically interpret an invoice’s text and structure.
If not, it instantly brings it to a person’s attention for prompt resolution. Let’s see some of the cognitive automation examples for better understanding. Cognitive automation represents a range of strategies that enhance automation’s ability to gather data, make decisions, and scale automation. It also suggests how AI and automation capabilities may be packaged for best practices documentation, reuse, or inclusion in an app store for AI services. It represents a spectrum of approaches that improve how automation can capture data, automate decision-making and scale automation. It also suggests a way of packaging AI and automation capabilities for capturing best practices, facilitating reuse or as part of an AI service app store.