Understanding the Differences Between Desktop Automation, RPA and RPA by DesignHeading

Originally published on Information-Management.com
Written by Tristan Gitman, Senior Director of RPA

There are four factors that should be considered when evaluating robotic process automation:

  1. Which robotic process automation software should be used to run a digital workforce operation?
  2. Which area inside the organization has or plans to incubate a digital workforce?
  3. How does the RPA software platform enable other technologies?
  4. How do the new robotic workflows offer a differentiated performance advantage?

White-collar workers, whose line of work requires “thinking for a living,” constantly feel inundated by the amount of information which must be dealt with before a decision can be made. When running a search on “automation software” it becomes clear that some of the “thinking for a living” that people do each and every day can now be offloaded to some type of automation utility.

From the end-user’s perspective, finding the right tool for the job is nothing more than software tool selection – find tasks to automate, test a range of the most popular applications, compare the output, negotiate the price, and finally wrap it up then press “Go.”

User driven desktop automation technology quenches a buyer’s thirst for dedicated assistance. McKinsey has provided an interesting statistic based on its assessment of work activities across 800 occupations performed at nearly 2,000 companies. They have concluded that 5 percent of the occupations can be lifted and shifted and fully executed by some automation solution.

Also, hypothetically speaking, it’s possible to automate 1/8th of all activities. This requires breaking up the existing business process workflows before work can be separated into “for people” and “for robots” work queues. Consider an average middle-market company employing 10,000 people. Focusing on labor arbitrage and striving for automation of 1/8th of the work yields a conceptually achievable target estimated at $50 million per year.

Automation ideas typically originate from a person with a specific need and expectation. Consolidating ideas and scaling automation from the bottom up is where companies start to face the reality – it’s difficult to do alone.

Consider a company that has purchased several automation software licenses, trained several people on the software, and compiled a list of automation opportunities. They’re making the most out of their desktop automation technology, but would they be served better by the more strategic, automation by design?

“Automation by design” is the framework which tends to be used in conjunction with an RPA installation in order to adopt an archaic operation into something that can host a business operation, which is co-curated with customers, partners, and suppliers.

Unlike desktop automation, robotic process “automation by design” is not for everybody. This is a more strategic capability that originated at “the top of the house” and not from grassroots ideas that would quench everyone’s automation thirst.

What does this mean? The initial entry point can target a closed block of work. The objective for this work pool is to operate through oversight, rather than hands-on execution. “Closed block” may immediately evoke the thought of an outsourced operation.

Depending on a commercial model there might be sufficient business case for transitioning the block to an RPA platform. Business case alone, however, doesn’t qualify an operation as RPA-ready.

In order to generate the most value from RPA, first focus on the closed operational blocks, and not customer journeys, which cut across a wide range of business units. This allows a kick-start of new robotic capability without impact on the core business.

RPA integrated into a closed block sets the precedent and becomes the foundation for the legacy core operation later.

An example of an alternative approach would be throwing RPA in the midst of the shared services. It is a common practice, which requires major effort. Aligning protocols for security management, software release management, change management and software delivery cycles with the digital workforce operation is a major program.

Initializing robotic process automation inside a closed operational block helps demonstrate its purpose to the wider organization. Having a scalable foundation is the initial success.

Ultimately, it is undeniable that using software in this way can have a number of benefits. That being said, it is important to carry out regular software tests to ensure that everything is working as it should be. Accordingly, you can learn more about the advantages of carrying out software tests such as load and performance testing here: https://www.parasoft.com/products/parasoft-soatest/load-performance-testing/.

Above all, once such capability exits, additional business units can begin to leverage RPA. The rest of the organization can begin leveraging a digital workforce without having to completely reinvent the core business.

It’s recommended that companies start their RPA journeys at department levels, or specific operational closed blocks.

RPA consultants can introduce the technology to the stakeholders in various departments. They in turn volunteer automation ideas. Initially, most people do not understand the distinction between previously used script-like automation and robotic process automation. The differences manifest themselves at scale.

The majority of what is referred to as “tactical” RPA initiatives arising from the grassroots ideas actually impact up to 1 percent of all the work performed inside of a 10,000-person operation. The issue for such low returns is not with the technology. User-triggered desktop automation and robotic process automation by design require two radically different frameworks of automation.

The market is flooded with desktop automation and labels are misconstrued with terms like RDA (robotic desktop automation), RPA (robotic process automation), IBA (intelligent business automation) used interchangeably.

RPA is packed with features which can be applied on small tasks (i.e. scripts). However, RPA has been designed to scale up to an enterprise-size platform, to seamlessly execute hundreds and even thousands of parallel work activities. Organizations can observe the difference when the automation pool becomes sufficiently large.

Click here to view the original publication.

Recent Posts

Executive Perspective: Why Securing Your Data is the Key to Winning with AI

As CXOs, we’re all focused on leveraging AI to drive efficiency, innovation, and competitive advantage. The conversation often starts with infrastructure (GPUs, LLMs, and copilots), but let’s be clear: AI doesn’t run on GPUs. It runs on data. And if our data isn’t secure, nothing else matters. Unsecured and unclassified data undermines trust, exposes us to risk, and jeopardizes the very AI strategies we’re betting our futures on. It’s not about chasing the next shiny tool; it’s about building a foundation that allows AI to scale safely, responsibly, and securely.

Quantifying the Value of Data in Financial Services

In the financial services sector, data is a critical asset that drives profitability, risk management, regulatory compliance, and competitive edge. However, measuring its value remains challenging for many CFOs across sectors of the financial services industry regardless of organizational size or country of operations. CFOs rely on accurate data for forecasting, budgeting, and strategic planning. Quality data leads to better decision-making, optimized capital allocation, and swift responses to market changes. It is also vital for risk management, regulatory compliance (e.g., BCBS 239, Basel III, AML/KYC), and avoiding fines and reputational damage. “Fit for Business Use” data also supports customer retention, personalized services, and improved revenue stability. Data-savvy CFOs leverage insights for long-term growth.

AI Starts with Data: Go From Hype to Results

AI continues to dominate the conversation in business. From executive meetings to strategic roadmaps, AI is no longer just a trend but a real driver of transformation. The challenge is that while nearly every organization is talking about AI, very few are prepared to use it in a way that delivers measurable outcomes and lasting impact. The difference between hype and outcomes almost always comes down to two things: the quality of your data and your organization’s readiness to execute.

Exciting Updates from Informatica World: Paradigm Embraces the Future of Agentic AI

The digital landscape is evolving rapidly, and staying ahead means embracing the latest innovations in data management and artificial intelligence. At this year’s Informatica World, Paradigm is thrilled to share the groundbreaking advancements unveiled by Informatica, centered around their latest agentic AI solutions on the Intelligent Data Management Cloud (IDMC) platform.

Modernizing PowerCenter: The IDMC Way – Better, Faster, Cheaper

For many organizations, Informatica PowerCenter has been the workhorse of their data integration for years, even decades, reliably driving ETL processes and populating data warehouses that feed BI reports. However, this longevity often leads to a complex environment that can hinder agility and innovation.

Boost Growth with Data-Driven Hiring for Boutique Consultancies

Consistency is key to a boutique consultancy. Delivering quality services day in and day out, even as client demand fluctuates, relies heavily on having the right talent at the right time. Perhaps one of the largest operational challenges for small and mid-sized consulting firms, though, is matching recruitment cycles with cyclical demand. Without scalable, data-driven talent practices, consultancies can suffer from misaligned capacity, lost revenue streams, and stalled growth.

Strategies for a Successful Journey in Building the Dream Team

In the whirlwind world of project management, the success of a project often hinges on the strength and consistency of the team behind it. Imagine embarking on a journey where building a high-performing project team is not just about assembling a group of skilled individuals; it’s about fostering collaboration, trust, and a shared sense of purpose. Based on my personal experiences, let me take you through this journey with some strategies I use to help you build and lead a high-performing project team.

The Ultimate Guide to AI-Enhanced APIM Analytics for Enterprise Success

Enterprises increasingly rely on Application Programming Interface (API) Management (APIM) to streamline their operations, enhance customer experiences, and drive innovation. Azure API Management is a comprehensive solution enabling organizations to manage, secure, and optimize their APIs efficiently. However, beyond API management, APIM analytics – particularly when integrated with AI and data analytics – empowers senior executives with critical insights for real-time decision-making.

Why PMOs that Leverage Power BI are More Successful

Project Management Offices (PMOs) are increasingly turning to Microsoft Power BI as a game-changing tool to enhance their project management processes. By leveraging data visualization and analytics, PMOs can make informed decisions, streamline reporting, and improve overall project outcomes.