While automation has been around since the 19th century, the technology’s goals have evolved significantly.

As a recent eWeek opinion piece by Accenture analysts puts it, automation in the 19th century was all about efficiency and empowering individual workers to be more productive. One hundred years later, 20th-century “titans of industry” invested in automation to increase value through more tightly controlled quality and supply chain optimization. 

As industrial automation grew into intelligent automation, the goals changed once again. Today, companies are turning to intelligent automation to deliver greater business value and innovation. Whether it’s improving customer experiences, uncovering new areas for competitive advantage, or making more strategic decisions, the goal of automation is ongoing digital transformation and top-line growth. 

To help organizations better understand these and other opportunities, the Accenture analysts outline five levels of intelligent automation:

1. Tools 

As they put it, automation tools are often the easiest part of intelligent automation. Companies leverage them to address specific issues and tasks that have historically been handled by people. As organizations see how technology can accomplish these with more efficiency and fewer errors, they realize the promise of automation.

2. Process

At this level of automation maturity, the focus is on eliminating unnecessary or redundant steps from entire processes. The Lean set of principles is often used to reduce time on non-value-added activities and ensure companies deliver products and services correctly the first time.

3. Robotic Process Automation (RPA) 

The third phase of intelligent automation is using RPA to automate repetitive, rules-based processes. At this level, companies should also establish the infrastructure and learning processes that enable RPA projects to learn and improve so that the technology can ultimately scale beyond individual projects.

4. Data

This is an essential element of automation maturity, as a strong focus on data lays the foundation for AI-driven intelligent automation. To do this, companies must reengineer their data supply chains and processes and address transparency, trust, and accessibility concerns.

5. Intelligence

Once a company has mastered managing data as a corporate asset and automating critical tasks and processes, it’s time to implement intelligent automation at scale across departments, teams, and processes. This ushers in new opportunities to augment individual processes, transform customer and employee experiences, and increase the bottom line.

For more on this and how to take your organization to the next intelligent automation level, take a look at the eWeek piece in its entirety.