4 Ways Robotic Process Automation (RPA) Differs from Intelligent Automation (IA)

Under the thumb of the pandemic, organizations accelerated automation initiatives, creating a strong demand for robotic process automation (RPA) software and services. Adoption of RPA is accelerating, with the market expected to hit $22 billion over the next two years, , Forrester indicates. But it’s not just spending on RPA that is forecasted to grow. 2023 is billed as the year of automation with investments in Intelligent automation solutions fuelling enterprise-wide efficiency and supporting decision-making through data. 

How does robotic process automation differ from intelligent automation? To answer this question, let us first understand the meaning of RPA and IA.

Learn More:  What is Robotic Process Automation?

What is Robotic Process Automation?

Robotic process automation or RPA is defined as the use of software or bots, to perform business processes such as invoice approvals, data transfers in customer relationship management, etc. RPA helps automate mundane, repetitive tasks, thus helping users focus more on higher-value activities. 

However, RPA only works well with rules-based systems and doesn’t perform well with exceptions. That is why scaling RPA often requires complementary technologies like task mining

To address these challenges, several organizations have moved into the next phase of RPA, known as intelligent automation. Bots continue to play a critical role, but sophisticated tools like artificial intelligence, process maps, dashboards, etc., provide greater control, resilience, and business alignment.  

What is Intelligent Automation?

Intelligent automation refers to the use of advanced technologies like artificial intelligence, machine learning, and robotics process automation to automate tasks that were traditionally performed by humans. This approach to automation enables businesses to not only save time and reduce costs, but also improve the accuracy, speed, and quality of their processes. Intelligent automation or IA creates a scalable automation solution. IA doesn’t just apply to repetitive manual tasks. 

Some organizations use IA to make their RPA bots a little more intelligent – for example, adding optical character recognition (OCR) to a data extraction bot so it can read screenshots as well. For others, it is part of an extensive, organization-wide automation program that aims to unlock efficiencies, reduce effort, and save costs wherever possible.

Learn More: What is Intelligent Process Automation (IPA)?

4 Ways Robotic Process Automation Differs from Intelligent Automation

RPA differs from Intelligent Automation in these four key aspects:

1. RPA does not simulate human intelligence; IA does

An essential characteristic of RPA is that it works best for rules-based systems. Software bots do not simulate human thinking – they only mimic human actions, such as repetitive, rules-based processes involving copying or updating the same data in multiple places by clicking on a system, thus building more efficient automated workflows

In contrast, intelligent automation uses cognitive capabilities such as AI to scale process automation and build more flexible business functions.  

2. RPA is part of Intelligent Automation

RPA is narrower in scope. On the other hand, Intelligent Automation leverages Artificial Intelligence technologies, and cognitive automation extends the applications beyond RPA. Intelligent Automation uses structured and semi-structured data inputs and can “learn” to improve itself.

3. Unlike IA, RPA cannot work in an unstructured data environment

RPA works with structured data inputs with data fields in purpose-built digital forms. Intelligent automation, however, works with both unstructured and semi-structured data inputs.

For instance, OCR identifies relevant information in various contexts, from PDFs to screenshots. NLP can convert vast volumes of unstructured data like social media chatter into actionable information. As a result, Intelligent Automation solutions can continue working even if it does not have a structured data set to feed on. In this scenario, RPA would grind to a halt and surface an error. 

Learn More: Intelligent Process Automation vs. RPA: 4 Key Differences 

4. Intelligent Automation offers more value versus RPA

While RPA is less costly upfront, it involves a higher cost of ownership due to the need for maintaining scripts and scalability issues. Second, RPA has no machine-learning capabilities and cannot learn from exceptions or errors.

In comparison, intelligent automation takes longer to start generating ROI. But it is much more sustainable and provides you with economies of scale. Using ML, it can also glean insights from past exceptions, events, and errors and learn.

As automation technologies mature, IA is clearly poised to be at the forefront of enterprise adoption. 

Learn More: What is Process Automation?

Experience the Scout work graph advantage

Today, over 60% of the teams’ workday is spent on unstructured interactions in documents, emails, communications, custom applications, and websites – outside of ERP, CRM, and other systems of record. This massive unstructured and undocumented interaction dataset between people and software is untapped and contains a goldmine of insights that could give a significant competitive edge to enterprises. Traditional solutions for identifying and building automation pipelines have significant privacy, accuracy, and scalability issues and fail to capture work done outside the systems of record, such as ERPs and CRMs.  

Through Soroco ScoutTM, powered by the world’s first work graph platform, organizations can unlock this new data source to discover processes where inefficiencies occur and are prime candidates for RPA and automation. 

Request a demo of Soroco ScoutTM today!    


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