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Automation Journey

Chapter 6

RPA vs Intelligent Automation: What’s the Difference?

The automation market has evolved quickly over the past few years. In 2016, robotic process automation (RPA) was still a relatively new technology that few companies had running in production, let alone at any scale, within their organizations.

Over the years, however, RPA has evolved to solve more challenging enterprise demands beyond simple task automation. This made way for cognitive functioning intelligent automation (IA), which combines the decision-making capability of artificial intelligence (AI) with machine learning (ML), natural language processing (NLP) and other advanced technology to drive productivity and scale enterprise-wide.

In 2021, however, with thousands of organizations adopting some form of RPA, there’s even more confusion about the difference between intelligent automation and traditional RPA.

Rpa vs hyperautomation

How Does Robotic Process Automation Differ from Intelligent Automation?

RPA is the foundational layer upon which intelligent automation is built. RPA uses bots to interact with applications, just like a person would, and requires defined rules to function. In other words, RPA only automates a task once it’s programmed to do so.

Meanwhile, intelligent automation can learn how to automate a task through cognitive decision-making capabilities. So, if we’re comparing IQs, intelligent automation’s got RPA beat. But RPA isn’t being left behind—IA is taking RPA under its wing. That’s why intelligent automation is considered the evolution of enterprise automation – because it uses artificial intelligence and RPA to totally transform operations.

Intelligent automation links artificial intelligence with the interactive capabilities of RPA. AI is the ‘thinking’, RPA is the ‘doing’ and intelligent automation connects the two.

Traditional RPA: A Powerful Automation Platform

In its inception, robotic process automation (RPA) redefined how we viewed work. By integrating software robots into the business workflow, RPA enabled people to streamline their efforts, managing data across various systems using rules and strict governance to ensure compliance. RPA also helped free employees from manual processes and time-consuming, repetitive tasks so they could focus on higher-value business goals.

Traditional RPA automation platforms can automate processes based on structured data and well-defined rules. Organizations across industries can realize significant gains in efficiency, data quality, employee experience and customer experience using traditional RPA. In fact, many organizations who’ve implemented traditional RPA have experienced cost savings and ROI of 30%-200%.

Examples of RPA across industries:

  • Invoice processing
  • Fraud detection
  • Quality control management
  • Dispatch and reporting
  • Shipment scheduling and tracking
  • Automating data entry and mapping
  • Collecting statistical data
  • Appointment scheduling

But even with these amazing benefits and cost reductions, there are limitations to traditional RPA. For example, the world we live in is not always made up of structured data. Most organizations receive vendor invoices on paper or in other non-standardized formats.

Converting data from these unstructured documents traditionally means that people must read the information and re-type the data into a structured format that can be used with data processing systems. In addition to the unstructured data, the rules to judge specific actions are dependent on many factors which may not be aligned with easily definable rules. With high volumes, this process is anything but efficient.

That’s where traditional RPA falls short of enterprise-grade intelligent process automation.

Intelligent Automation: Making RPA Smarter

Building on the strengths of RPA, intelligent automation brings together complementary technologies to augment the capabilities that traditional RPA doesn't include for an end-to-end digital transformation. Broadly, these technologies fall into the following categories:

  • Knowledge and insight - The ability to scan data sets and knowledge bases to extract data and compile it into customized formats
  • Visual perception - The ability to read, understand and contextualize visual information digitally
  • Learning - The ability to adapt and evolve processing patterns and contextual meaning from datasets
  • Problem-solving - The ability to solve logic, business and system problems without human intervention
  • Collaboration - The ability to enact seamless communication and collaboration between people, processes and technology
  • Planning and sequencing - The ability to discover and utilize opportunities, such as repetitive tasks and bottlenecks, and to optimize complex workflows and workloads for better business outcomes

Complementary technologies

Advances in automation technology have opened the door for exciting business transformation opportunities.


Technologies like AI and ML can be applied in nearly every category, where complementary technologies like optical character recognition (OCR) and intelligent document processing (IDP) would apply to the visual perception category.

OCR and IDP eliminate repetitive administrative tasks from your team by accurately validating, extracting and digitizing data. This is where unstructured or complex documents like invoices or purchase orders can be read, allowing the data to then be used by RPA.


Business process management (BPM) is a structured way of improving complex business processes by analyzing, monitoring and automating them.

BPM programs such as the SS&C | Blue Prism® Chorus user interface makes it easier to rapidly build business process automations and optimize workflows to accelerate straight-through processing.

Intelligent Automation

IA can engage components from one or more of the categories to create complex and interesting solutions to real business issues, from automating time-consuming mundane tasks to entire business processes. This capability not only opens new ways to interact with customers, vendors and employees, but can also spawn new products and services for the organization.

While IA can integrate with these complementary technologies to achieve amazing outcomes, the technology is incapable of considering the need to manage the platform at an enterprise level.

Let’s look at what’s necessary to upgrade from IA to true enterprise-grade intelligent automation technology.

Creating an Intelligent Digital Workforce

If your organization wants a lasting, adaptable IA solution, then you need a robust and intelligent digital workforce. That means you need a digital workforce that collaborates with your people, complies with industry standards and governance, and improves workflow efficiency.

A digital workforce is there to remove tedious tasks from your people so they can focus on higher-value projects, like improving customer satisfaction by providing faster response times.

Digital workers, when deployed correctly, can reduce time to market and deliver a higher return on investment (ROI) for your organization.

Enterprise intelligent automation: Smart RPA under governance

While adding technical solutions to interpret complex documents and unstructured data makes it possible to automate data through traditional RPA methods, it’s obvious that technology alone isn’t enough.

Stacking one technology on top of another can create a house of cards instead of a structure to withstand a hurricane. You need a unified workforce.

To reach enterprise automation, your whole stack of cognitive automation technologies must adhere to a strong governance structure to ensure that any automations built on that stack can endure the harsh extremes of corporate use, including:

  • Security
  • Data management
  • Integration
  • Access control
  • Flexibility under stress
  • Scalability
  • Support
  • Policy management

You need to consider expanding the scope of automation in your organization if you want to produce better business value.

If one of the technologies used to build your intelligent automation solution doesn’t conform to the enterprise-grade IT requirements, the entire solution is at risk. Each component – like a brick in a wall – must hold up under the pressures of the entire enterprise.

SS&C | Blue Prism® Enterprise is our solution to unifying your workforce and maintaining governance by cohesively bringing together your people and your digital workers. With top-tier security standards and development practices, our Enterprise low-code automation software logs every action taken within your system to provide irrefutable audit trails.

Building a robust intelligent automation solution may require additional effort, but it will produce a solution the entire organization can rely on and scale across departments.

Putting the Intelligence into Your Intelligent Automation Strategy

When RPA just isn’t enough, intelligent automation takes the stage.

RPA on its own is made for mimicking human actions, such as back-office tasks. Meanwhile, intelligent automation uses AI, ML, RPA and NLP to automate complex business processes.

IA is a dynamic and flexible solution when you have processes that frequently change, allowing for human-in-the-loop (HITL) collaboration to make decisions and get work done. But you need to make sure your automation is working in conjunction. And for that to work, you need to unify your digital workforce.

Collaborative automation technologies = A unified digital workforce = enterprise-grade intelligent automation.

Basically what we’re saying is, when it’s scaled effectively across your organization, intelligent automation can bring your workforce together, spike revenue and blow away your competitors.


About the Author

Alexis Veenendaal

Alexis Veenendaal is an Associate Content Writer and Editor at SS&C Blue Prism. She’ll tell you all the cool tips and tricks for implementing intelligent automation into your workplace. She has lived and worked internationally as a professional writer and designer for nearly a decade after graduating from the University of Lethbridge for English Literature. Her personal pursuits include authoring books and digital cartography.

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Next: Chapter 7

Will RPA Technology Take Human Jobs?

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