A recent survey by the Institute for Supply Management asked respondents a simple question: “Have you heard of RPA?” More than 60% of the 432 respondents said they had not.

Robotic Process Automation (RPA) is a technology that automates repetitive tasks on a computer. Examples include data entry or moving files from one location to another—processes that are predictable, rule-based, and programmable for a computer to execute.

So why should supply chain professionals care about this? James Fleming, a program manager for certification at the Institute for Supply Management, who spoke at the organization’s annual conference in Houston, explained that it’s about resources. Today, across industries, only about 1.8% of an organization’s workforce is dedicated to supply chain management, and that number is expected to continue decreasing.

“If we think we’re going to get more employees, we won’t,” he said. “If we need more hours of work, they’re not going to give them to us. And I think a lot of people are starting to understand the idea of finding ways to automate transactions using the resources we already have—and to begin applying it strategically.”

Although many respondents had never heard of RPA, most indicated that they are either exploring or already in the process of implementing some form of automation through AI or RPA.

The biggest challenges for organizations adopting automation technology—which often requires significant IT resources—include a lack of technical expertise and limited flexibility of the bots being used. However, the benefits include more efficient workflows, higher productivity, and freeing up time for other tasks.

The first step is identifying the data that needs to be input into the system or the processes that push and pull data from one system to another. This is typically the best starting point for implementing RPA technology.

For organizations using these technologies, the long-term goal is likely to evolve from simple tasks, like basic RPA projects, to AI systems capable of learning independently. Self-learning systems do not operate solely on pre-programmed instructions; instead, AI makes decisions based on historical data and real-time inputs. This represents a significant step forward for organizations looking to implement advanced automation.

While the main goal is often to save time and focus on higher-level work, Fleming suggests that if an organization doesn’t believe it has the data necessary to benefit from these technologies, it may need to rethink its approach.