ai robot touching a human hand

By Shayne Pendergast

The ideas of automation and intelligence aren’t revolutionary. When I was a kid, my father told me about a cartoon he watched where there were autonomous (flying) cars, smart homes, robot helpers, and AI assistants. I remember thinking: “that’s pretty cool.”

That classic, futuristic cartoon, “The Jetsons,” was produced in the early 1960s. They were rocking the tech and stretching the imagination even then.

Fast forward to today, and that once sci-fi coolness is becoming reality—thanks to the evolution of RPA and AI.

Robotic Process Automation (RPA) and Artificial Intelligence (AI) are distinct technologies often paired because of their complementary qualities. RPA automates repetitive and rule-based tasks. AI enables machines to perform cognitive tasks like perception, reasoning, and decision-making. Together, they can increase efficiency, reduce costs, and improve accuracy across all levels of an organization.

Quantifiable outcomes driving increased utilization

The numbers don’t lie. According to researchers at Grand View Research, the global RPA market was worth a mind-blowing $1.1 billion in 2020. It’s projected to continue to grow through 2030 at a compound annual growth rate (CAGR) of 39.9%.

The reason for such increased demand is simple. RPA automates repetitive and time-consuming tasks, making workers more efficient and organizations more profitable.

Here are a few representative examples:

  • Automating invoice processing can decrease processing time up to 80% and improve accuracy up to 90%.
  • Bots can handle common customer service requests (password resets, account inquiries, billing disputes, etc.) and improve support response times by up to 40%.
  • RPA can streamline inventory management processes by automatically generating purchase orders, tracking inventory levels, and updating stock records in real-time—resulting in reduced inventory holding costs of up to 30% and improved order fulfillment accuracy of up to 25%

Similarly, the AI market has a predicted growth rate through 2030 of 37.3%. The increased demand for automation is driving a growing need for intelligent decision-making…across all industries—predictive maintenance in manufacturing, personalized marketing in e-commerce, diagnostics and treatment planning in healthcare, and fraud detection in financial services.

The evolution of RPA and AI is integration

When it comes to the integration of RPA and AI, a report by Gartner predicts that by 2024, 50% of all new RPA implementations will include AI.

It’s not surprising. While automation focuses on mechanizing tasks, AI adds cognitive abilities such as problem-solving, pattern recognition, and decision-making.

For example, an RPA bot can extract data from various sources and input it into a system. However, if the data is unstructured, an AI algorithm can interpret and categorize the data before input. This integration can help improve the accuracy of data input and reduce the risk of errors.

The above is one of endless examples. If correctly deployed, the sky’s the limit for RPA and AI integrations.

The challenge of RPA and AI integration

Understanding the evolution of RPA and AI and how they are distinct is crucial because it highlights the movement of technology from executing instructions to exhibiting intelligent behavior. It also helps with deploying the right tools and mitigating potential risks associated with their implementation.

What are some of these potential risks?

  • Data security and privacy.
  • Bias and fairness.
  • Workforce.

This last point brings up perhaps the most real challenge. To implement and integrate RPA and AI effectively, skilled and trained workers are needed.

Reports conflict on if the demand for AI professionals exceeds supply. It’s either already there or close. Regardless, it’s a talent pipeline in need of development, because like it or not, what was once sci-fi is becoming our reality. If we’re to “go boldly” to a level of awesome we’ve not yet been, we need skilled professionals who can effectively and ethically design and implement the systems to take us there.

Photo of Shayne Pendergast, Senior RPA Developer for Garnet River

Shayne Pendergast is RPA practice lead for Garnet River and  UiPath Certified Advanced Developer. He can be reached at

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