In the last few years, AI and automation have perhaps blown more trumpets than any other technology known to us. Robotic Process Automation (RPA) has already automated a huge spectrum of industrial and commercial operations where human actions are needed. On the other hand, Artificial Intelligence (AI) have helped organizations automate repeated mundane tasks by simulating intelligence typical of human mind. The results are increased business efficiency and reduced operating cost.

By implementing AI and RPA together, organizations can reap greater benefits in terms of digitally transformed business solutions and innovative methods to solve repetitive tasks that improve both productivity and customer experience.

Here we will see how RPA and AI work together through practical use cases.

RPA with AI

Adding AI to RPA, results in efficient automation of tasks. With AI, any organizational system gets power to read the existing environment and solve computing problems through good reasoning to attain target business goals. The AI-enabled solution can effectively analyze the outcome of previous actions to adapt and evolve.

Functional working of AI and RPA

While RPA looks after executing repeated actions across industrial enterprise, AI helps identify key performance areas and effective outcomes using cognitive functions as performed by humans. Together, AI and RPA can maximize organizational efficiency, reduce operating cost and help achieve organizational objectives faster.

This is how the combined power of AI and RPA helps various verticals reach unique business goals in less time and efforts.

Enhanced Automation Technique:

AI enables simulation of everyday intelligence of humans by adapting to cognitive functions typical of human mind. From learning and data analysis to self-improvement, AI is the technology that goes beyond the instructions coming from human input and thinks for itself. RPA on the other hand relies rigidly on incoming instructions and human inputs, lacking natural learning and problem solving capabilities. When coupled with intelligent bots, AI and RPA work together to self-learn and implement desired actions independent of static instructions.

Invoice processing:

Since RPA with AI intelligence can process unstructured data, one of the use cases where intelligent RPA works better is invoice processing. Processing invoices for instance is a challenging task where a massive amount of unstructured data from different vendors is tackled and that too, in various formats. General rule-based AI tools with fixed instructions or templates are not the best solution to meet such dynamic requirements of invoice processing.

IPA tools that marry AI with RPA leverage Machine Learning capabilities and OCR (Optical Character Recognition) to identify the context and format of the document being processed. This helps extract and understand the most relevant information for the process.

Chatbots:

Chatbots run by AI technology have turned out to be the smartest solution for modern mobile shopping. Online shopping portals have millions of users relying on chatbots for an excellent shopping experience. Chatbots are good at taking in a set of preferences, unique orders, product choices, and related queries while performing mundane online tasks and sharing relevant data with customers. However, chatbots themselves can’t complete certain tasks on their own.

When RPA is integrated with chatbots, chatbots can supply important customer data to the RPA system to execute actions related to order cancellation, delivery dates, quantities, etc. such quick responses further enhance customer satisfaction and the overall online shopping experience. Automation also brings down the task burden assigned to customer service executives in place.

Payroll automation:

AI stands for automating mundane tasks present in any industry. Payroll management is one such process where monthly repetition is observed. The process is repeated every month and is quite time-consuming as it contains a lot of data entry for HR teams of any organization. Also, the chances of meeting inaccuracies during the manual data entry work are high and result in payment delays.

Intelligent RPA simulates HR intelligence and checks the accurate employee data across timesheets and validates release of payment, minimizing the instances of payment delays or inaccurate payments. If implemented perfectly, it can also automate the process of salary formulation including benefits, deductions, and holidays, thus automating most payroll transactions.

Self-correction:

When integrated with AI, RPA gains cognitive ability to work with processes, where correcting and learning are constant requirements. Through the transfer learning method, the intelligent RPA can capitalize on the knowledge gained from ideal models of running operations and apply it to tackle other similar tasks.

AI allows the RPA to self-learn from modeled tasks and human feedback and see if it can improve process efficiency or user experience by correcting its standards. Integrating such techniques, intelligent RPA can be taught to learn from its actions, interpret possible results and trigger better responses through constant self-correction.

Conclusion

Intelligent RPA combines the best of both: robotic automation and human intelligence. While RPA mimics pre-instructed human actions through the digital systems, AI adds a brain to the machine and develops cognitive ability to learn from model tasks and implement for executing efficient business processes. Together RPA and AI can work on more cognitive tasks and unstructured data to do more than just simple traditional processes. Intelligent RPA thus offers a broad range of use cases and yields better savings, efficient operations, and enhanced customer experience.

Want to boost business productivity and process efficiency with robotic process automation? ZX Digital, the best digital transformation company in Canada, applies RPA-AI technology to enterprise systems to improve productivity, operational efficiency, and reduce costs.

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