The simplest form of machine intelligence, rules-based automation (RBA) refers to a system that applies human-made rules to store, sort and manipulate data. In doing so, it mimics human intelligence.

The main goal of a rule-based system is to capture the knowledge of a human expert in a specialized domain and embody it within a computer system. To work, rule-based systems need a set of facts or sources of data and a set of rules (“IF x THEN y” instructions) to choose an action. 

For decades, RBA has been widely adopted to automate, simple, rules-based processes such as data entry, invoice processing, accounting reconciliation and so on. For example, a recruiter might use RBA to filter out applicants who have less than 5 years experience. The finance department might use RBA to transfer data from a sales invoice into their financial management system. Sales & marketing teams use RBA to redirect sales leads to appropriate team members based on location. And the list goes on.

Despite its prominence, RBA does have some downsides. It's incredibly rigid and does not respond well to change. For example, if the RBA is tasked with transferring data from one interface to another, if one of those interfaces changes in any way, RBA will likely malfunction. 

In addition, it requires a human programmer to foresee all potential scenarios. For multi-step processes, this can be quite tedious and time consuming.  

Robotic Process Automation

Robotic process automation (RPA) represents the next evolution beyond RBA. Instead of a human having to program rules-based logic into the software ahead of deployment, RPA software “bots” are capable of “observing” human behavior and mimicking it. In other words, it learns the rules on its own. 

As explained on Wikipedia, “In traditional workflow automation tools, a software developer produces a list of actions to automate a task and interface to the back-end system using internal application programming interfaces (APIs) or dedicated scripting language. In contrast, RPA systems develop the action list by watching the user perform that task in the application's graphical user interface (GUI), and then perform the automation by repeating those tasks directly in the GUI. This can lower the barrier to use of automation in products that might not otherwise feature APIs for this purpose.”

However, RPA has some of the same limitations as regular RBA. Even though RPA doesn’t need to be pre-programed, it still only works on routine, rules-based processes. It also tends to be very brittle and does not adapt well to change or anomalies. As a result, RPA software often breaks down and/or has to be re-worked which can lead to increased technical debt. 

Increasingly, organizations are getting around these issues by incorporating machine learning (ML), and artificial intelligence (AI). By incorporating cognitive technologies into these more rigid systems - a process known as Intelligent Automation and Hyperautomation - RPA can become “smarter.”

Workflow automation is another term that is often used in conjunction with both RPA and RBA. While RBA and RBA are concerned with automating the task itself, workflow automation is concerned with the orchestration of tasks.