What are the Differences Between the Two Technologies?

Robotic Process Automation (RPA) and Intelligent Automation (IA) are two of the most popular approaches to business process automation today. As more and more businesses look to automate repetitive tasks in their day-to-day operations, the differences between RPA and IA have become increasingly important to understand. This article explores the key differences between these two technologies to help you determine which one is right for your business needs.

What is Robotic Process Automation (RPA)?

Definition of RPA

RPA is a form of automation that uses software robots or ‘bots’ to automate simple, rule-based tasks that are repetitive and time-consuming. These software bots can be programmed to carry out tasks such as data entry, invoice processing, and customer service inquiries, among others.

How RPA works

RPA uses structured data and follows pre-defined rules to execute tasks. It typically involves the use of software bots that are designed to mimic human actions within a digital process automation workflow.

Applications of RPA

RPA has applications in virtually every industry. It is commonly used in finance and accounting, human resources, procurement, supply chain, manufacturing, and customer service. RPA helps businesses automate repetitive tasks, freeing time for employees to focus on more complex and creative tasks that require human intelligence and intervention.

What is Intelligent Automation (IA)?

Definition of IA

Intelligent Automation, sometimes referred to as Intelligent Process Automation (IPA), is a more advanced form of Automation that combines Robotic Process Automation (RPA) with Artificial Intelligence (AI),  Machine Learning (ML), and Intelligent Document Processing (IDP). IA uses bots that are capable of learning and adapting to unstructured and unpredictable data sources, making it more effective at automating complex processes.

How IA works

IA leverages computer vision, advanced algorithms. and natural language processing to understand unstructured data sources. It can make intelligent decisions based on data inputs and automate business processes that are too complex for traditional RPA alone.

Applications of IA

IA has applications in a wide range of industries, including professional services, healthcare, finance, and customer service. IA can be used to remove “human in the loop” steps in rule-based RPA workflows or to fully automate complex processes such as fraud detection, claims processing, and risk assessment.

What are the Differences Between RPA and IA?

Level of Intelligence

RPA is a rule-based technology that follows pre-defined instructions to carry out tasks, while IA is an extension of RPA that uses AI and ML to learn from data inputs and make intelligent decisions. IA bots can adapt to new and unstructured data sources, making them more effective at automating complex business processes.

Business Processes

RPA is best suited for automating simple, repetitive tasks such as data entry and invoice processing. IA, on the other hand, is better suited for automating complex business processes that involve unstructured data sources.

Types of Data

RPA works best with structured data sources, while IA can process both structured and unstructured data sources. IA bots are better equipped to handle complex data sources such as voice and image data.

RPA vs. IA: Which One to Choose?

Factors to Consider

When choosing between RPA and IA, businesses must consider factors such as the complexity of their business processes, the types of data sources they need to process, and the level of intelligence they require from their automation technology. Intelligent Automation solutions (due to their additional toolsets and complexity) typically require more significant investments than stand-alone RPA automation, but IA-based automations usually drive higher Returns on Investment (due to additional labor savings).

Use Cases for RPA and IA

Use RPA when you are automating simple, rule-based tasks such as data entry and invoice processing. IA, on the other hand, is better suited for automating complex business processes that involve unstructured data sources and require intelligent decision-making capabilities; examples include social media feedback monitoring, document analysis, and automated customer support.

Future of Automation

The future of automation will likely involve a combination of RPA and IA technologies. While RPA will continue to play a role in automating repetitive, rule-based tasks, IA is expected to become increasingly important as its capabilities continue to expand and businesses seek to automate more complex business processes.

How RPA and IA Can Improve Business Productivity

Reduction in Time-Consuming Tasks

RPA and IA can help businesses reduce the time employees spend on mundane, repetitive, time-consuming tasks. This, in turn, allows employees to focus on more complex and creative higher-value tasks that require human intelligence and intervention.

Cost Saving

Automation can help businesses to reduce costs by improving efficiency and reducing the need for labor-intensive manual processes. By automating simple tasks, businesses can free up employees to work on more strategic activities that can drive revenue and growth. This can ultimately lead to cost savings for the business.

Improved Customer Experience

Automation can help businesses to improve the customer experience by reducing errors and speeding up response times. By automating customer service inquiries, businesses can provide faster and more accurate responses to customers, improving customer satisfaction and loyalty.  By freeing up internal resources from manual, mundane, and repetitive tasks, Automation unlocks additional time for your customer-facing teams to focus on providing intentional and proactive customer service.

Improved Accuracy

By leveraging automation, organizations can achieve higher accuracy and reliability in their processes, as robots follow predefined rules and execute tasks without human error or fatigue, minimizing the chances of mistakes or inconsistencies.  This improved accuracy enhances the quality of work output and reduces the need for manual error correction, saving employees time and effort.

Enhanced Job Satisfaction

Automation relieves employees from monotonous and repetitive tasks, empowers them to engage in meaningful and challenging work, leads to higher job satisfaction, and enables them to allocate their time and skills towards their highest and best use, maximizing their morale and contributions to the organization.

Conclusion

Understanding the differences between RPA and IA is essential for effective process automation. RPA is ideal for repetitive, rule-based tasks, while IA combines RPA with AI, ML, and IDP for complex processes involving unstructured data. Consider factors like process complexity and data types when choosing between the two. Regardless of your path (IA or RPA), implementing automation improves productivity, reduces costs, enhances customer experience, ensures accuracy, and boosts job satisfaction. Automation is a clear differentiator that streamlines operations, optimizes resources, and drives success in today’s competitive business landscape.