Intelligent Automation Vs RPA - A Detailed Comparison

Companies are therefore always on the lookout for better practices that will help enhance efficiency. Intelligent Automation vs RPA are two business technologies at the cutting edge of revolutionising operations in several industries. RPA, on the other hand, automates strict, linear, repetitive, and deterministic tasks, whereas Intelligent Automation harnesses AI and machine learning (ML) for systems to learn and self-correct. However, which one of them perfect for your organisation? 

Introducing you to Intelligent Automation vs RPA, this comparative statement also determines what type of application is preferable within which context.  Browse through the A to Z of technologies, from basic procedural using automation tools to machine learning (ML) and artificial intelligence (AI) to help prevent inefficiencies and cut costs in half while designing future-oriented solutions. 

Table of Contents 

1) Understanding Intelligent Automation and RPA   

2) What is the Difference Between RPA and Intelligent Automation?  

3) Future Trends in Intelligent Automation 

4) Future Automation in RPA 

5) Can RPA and Intelligent Automation Cco-exist?  

6) Conclusion 

Understanding Intelligent Automation and RPA 

Before jumping into Intelligent Automation vs RPA as a topic, it is pivotal that we understand what these two technologies are and why they are important:
 

What is Intelligent Automation? 

Intelligent Automation (IA) refers to a set of technologies that combine Robotic Process Automation (RPA) with Artificial Intelligence (AI) and Machine Learning (ML) capabilities. This allows systems to learn, adapt and optimise their performance over time, making them more intelligent and better able to handle complex tasks.  

Intelligent Automation can be used in a wide range of applications, from customer service and sales to logistics and supply chain management. It enables businesses to automate more complex tasks, improve accuracy and enhance the customer experience.  

The main benefits of Intelligent Automation are: 

1) Enhanced accuracy and speed 

2) Improved decision-making 

3) Greater flexibility and scalability 

4) Reduced costs and improved efficiency 

5) Better customer experience 

6) Increased competitiveness and agility

What is RPA? 

Robotic Process Automation (RPA), on the other hand, is a type of software that uses bots or digital workers to automate repetitive and rule-based tasks. These tasks can include data entry, form-filling and other tasks requiring little human intervention. 

RPA has been widely adopted by businesses in recent years to increase efficiency, reduce errors, and free up employees to focus on more strategic tasks. In contrast, understanding the differences between AI and RPA technologies can provide insights into how AI can complement RPA by handling more complex, cognitive tasks.

The main benefits of RPA are: 

1) Reduced errors and improved accuracy 

2) Increased productivity and efficiency 

3) Lower costs and improved ROI 

4) Greater scalability and flexibility 

5) Improved compliance and risk management 

Eager to learn more about Robotic Process Automation, refer to our blog on "RPA testing"

What is the Difference Between RPA and Intelligent Automation?
 

Future Trends in Intelligent Automation 

Here, the following are the Future trends in Intelligent Automation:

a) AI-driven Automation: This means that the linkage of AI to automation services will facilitate self-learning, self-organising and self-adjusting capabilities of systems without outside assistance from humans. 

b) Hyper Automation: The integration of RPA, AI and ML will be popularised under the term hyper automation and used as the strategy for automation of end-to-end activities in enterprises. 

c) Low-code/No-code Automation: While the low-code and no-code platforms will enable business users to implement automation workflow applications, low-code and no-code platforms will help you create these applications with simple and no programming skills at all. 

d) Intelligent Document Processing (IDP): Artificial Intelligence (AI) will continue to play a larger role in using specific computer software to identify, categorise, sort, index, and manipulate the information extracted from unstructured documents.  

e) Automation-oriented Workforce: The goal will be to train and develop staff for work hand in hand with intelligent automation tools and cooperation between human and AI.  

f) Cloud-based Automation: Cloud platforms will enable efficient, elastic and affordable automation that is easily adoptable by all organisations. 

g) Powerful Algo and Business Intelligence: Intelligent automation will combine predictive analytical capabilities to support organisations in getting real-time insights and making smart decisions. 

Future Automation in RPA 

Here following are the pointers for Future Automation in RPA:
 

a) Cognitive RPA: Cognitive RPA will replace traditional RPA as cognitive RPA is expected to handle more decision-making capabilities such as NLP and machine learning (ML).  

b) End-to-End Process Automation: New RPA applications will no longer be defined with the scope of automating simple tasks; future applications will be of end-to-end automation of business processes, enhancing the efficiency factor across the enterprise.  

c) AI and RPA Integration: These bots will use AI for better forms of automation, such as intelligent decision-making, dealing with exceptions and mitigating errors in managing unstructured data. 

d) Improved Interactivity of Humans with the Bot: It will be the future that fully autonomous or semi-autonomous bots will be assisting humans when it comes to performing tasks and while doing so, they will always remain within supervision and control. 

e) Scalability and Flexibility: As a result of these considerations, cloud-based RPA solutions will enable organisations to scale the automation of workloads and satisfy new or altered requirements.  

f) Security: This was measured by an increase in compliance and Governance. This was measured by an increase in propriety. Security mechanisms to shield data and the overall compliance of the functioning of RPA will be integrated in the platforms. 

g) RPA-as-a-Service: Solutions of RPA under the subscription model will become more popular among businesses since it reduces infrastructure expenses and accelerates the process of deployment.

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Can RPA and Intelligent Automation Co-Exist?

Intelligent Automation and RPA can co-exist in a variety of ways, and they can even complement each other in many scenarios. Here are a few examples:   
 

Intelligent Automation and RPA can co-exist in a variety of ways, and they can even complement each other in many scenarios. Here are a few examples: 

1) Combining RPA and AI: RPA can be used to automate repetitive, rule-based tasks, while AI can be used to add intelligence to the process. For example, an RPA Bot can extract data from a document, while an AI-powered algorithm can analyse the data to make decisions or predictions.  

2) Task Handling: RPA can automate simple, routine and repetitive tasks, while human employees can focus on complex tasks that require creativity and decision-making and involve human interaction.

3) Implement Intelligent Automation to optimise RPA processes: Intelligent Automation can be used to improve the performance and efficiency of RPA processes. For example, Machine Learning algorithms can identify patterns and optimise RPA workflows. 

4) Using Intelligent Automation to Provide Context and Decision-Making Capabilities to RPA: Intelligent Automation can provide additional context and decision-making capabilities to RPA Bot. For example, Natural Language Processing (NLP) can make RPA Bots understand unstructured data or language and make decisions based on the executed data.

5) Combining Intelligent Automation and RPA to Create End-to-End Process Automation: Intelligent Automation and RPA can automate end-to-end business processes. For example, an RPA Bot could extract data from a document. An AI algorithm can analyse the data; another RPA Bot could use the analysis results to automate a subsequent task.   

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Some use cases where Intelligent Automation and RPA have been used together are: 

a) Financial Services: One is RPA that processes work such as entry of data (e.g., loan applications), and the other is Intelligent Automation which incorporates decision-making (e.g., creditworthiness).  

b) Healthcare: RPA completes administrative work such as scheduling appointments, while IA analysis severity of disease based on symptoms. 

c) Manufacturing: RPA is simple activities such as data input or verifying data; Intelligent Automation comes in after the data has been gathered in order to derive meaning from the data and enhance production. 

d) Retail: RPA controls stock and prompt, whereas Intelligent Automation forecasts need and arranges inventory. 

e) Employee Onboarding: RPA makes forms easier to fill in, while on the other hand Intelligent Automation is a full-cycle solution to onboarding that entails IT build-up for new recruits and compliance. 

f) Customer Relationship Management (CRM): While RPA is responsible for dealing with template responses, Intelligent Automation deals with customer trends and even analyses them for further useful information.

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Conclusion 

Intelligent Automation vs RPA? Wise will vary based on the features you are seeking to create in the company. RPA is best used when you want efficient low-skill level process execution, while Intelligent Automation applies AI to decision making and flexibility.  Knowledge about these strengths would help businesses determine whether it is useful to apply RPA to relentlessly capture easy wins or to apply intelligent automation that would result in end-to-end intelligent automation. 

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