Robotic Process Automation & AI: Combating Productivity Challenges

AI (Artificial Intelligence) has always stirred interest in its potential to perform tasks requiring human intelligence more efficiently. According to Forbes, 48% of firms are leveraging the use of machine learning (ML), data analysis, and various other AI tools to enhance productivity. The combined capabilities of Robotic Process Automation (RPA) and AI offer a transformative approach to business process automation, known as Intelligent Automation.

Distinguishing RPA and AI

Robotic Process Automation (RPA) employs software robots to automate repetitive tasks, freeing employees to focus on more stimulating and cognitive work. These robots execute tasks accurately and efficiently based on predefined rules. Conversely, Artificial Intelligence (AI) encompasses cognitive capabilities such as learning, reasoning, and detecting inefficiencies. AI applications, including machine learning (ML) and intelligent document processing (IDP), enable machines to mimic human intelligence and improve over time.

The Synergy of RPA and AI

By implementing AI-powered RPA robots, you have intelligent digital workers that follow contextual rules and learn as they go. While RPA automates structured, rule-based tasks, AI extends these capabilities to handle unstructured data like text, images, and audio. This integration leads to intelligent digital workers who can learn, adapt, and optimize processes continuously, working tirelessly to increase productivity and customer satisfaction​. RPA and AI work together to form the perfect team, known as the digital workforce.

How Do AI and RPA Complement Each Other

Intelligent Automation, the fusion of AI and RPA, represents a comprehensive approach to automation. By incorporating AI technologies such as machine learning, natural language processing, and computer vision into RPA systems, businesses can automate complex processes requiring both repetitive tasks and cognitive functions. This results in greater efficiency, accuracy, and adaptive process optimization​

Utilizing Unstructured Data in an RPA Workflow

A significant portion of organizational data is unstructured. Often this data can run into hundreds of gigabytes and is next to impossible for a human to assess, organize and interpret it in a meaningful manner in a meaningful time frame. This then presents vast opportunities for automation.

Utilizing AI within RPA workflows allows for the extraction and processing of meaningful information from unorganized content, such as documents, emails, and reports. This capability enhances the scope of intelligent automation, enabling tasks like document categorization, content understanding, information extraction, and establishing relationships between data points

AI makes all relevant information instantly meaningful and actionable in RPA. It examines, classifies, and pulls necessary details from unorganized material (such as papers, order forms, statements, emails, survey reports, and so on) before organizing it into clean files for robotic process automation.

What are the Use cases of RPA and AI?

Healthcare Patient Management: In the healthcare sector, AI can assist with patient diagnostic and treatment suggestions, whereas RPA can be used for patient scheduling and record gathering.

Finance Transactions: RPA and AI can work simultaneously to safeguard the security and accuracy of financial transactions by collecting data from several sources and detecting potential fraud by screening for inconsistent activity.

Invoice Processing: AI-powered robots in payment processing systems drastically reduce invoice processing time and improve accuracy.

Conclusion

The integration of AI and RPA signals a future where automation transcends routine tasks to encompass complex decision-making and problem-solving. This not only boosts efficiency and reduces costs but also fosters innovation and strategic growth. AI-enhanced RPA can revolutionize industries such as healthcare, banking, manufacturing, and customer service, offering smarter, more responsive, and adaptable business solutions. As these technologies evolve, they promise to generate novel applications and solutions, driving us towards a more efficient and innovative future.

-By Yash Badiani, Senior Principal Architect at eInfochips (An Arrow Company)