Understanding cognitive automation

Cognitive automation or intelligent process automation (IPA) refers to the use of AI and RPA together. It provides the machine or the robot with the brain (AI) and the limbs (RPA).

Although the general software development life cycle (SDLC) looks the same at a high level for RPA development and cognitive automation development, there are two important differences:

• The role of the RPA developer across the SDLC
• The final output of the RPA and cognitive automation life cycles

Let's now take a look at these differences in detail.

Understanding the expanded roles the RPA developer plays in the cognitive automation life cycle

An RPA developer plays expanded roles in the cognitive automation SDLC. A detailed comparison between a representative RPA SDLC and a representative cognitive automation SDLC is given in the following figure:

Figure 1.4 – Differences in RPA developer roles in the RPA and cognitive automation SDLCs

In the RPA SDLC, an RPA developer is like a traditional developer for any other software package. In this, the typical sequence of the process is as follows:

1. The business analyst collects the end-to-end business requirements of a business workflow detailing inputs, process steps, and output.
2. The RPA developer codes the RPA workflow and tests the code.
3. The business user conducts a user-acceptance test of the RPA robot.
4. Finally, the RPA developer creates a package to deploy to the production environment.
5. Post-production, the administrator manages the operations of the RPA bots.
6. The RPA developer updates the code if the business user suggests enhancements or reports bugs.

The RPA developer plays a heavy role in selected steps of the RPA SDLC (build, deploy, and improve) by converting business requirements into RPA language.

In the cognitive automation SDLC, the RPA developer has a role in almost every step, which is described as follows:

1. The RPA developer collects data-specific requirements to prepare for ML model training/re-training.
2. The RPA developer does not usually build the ML model. Instead, the RPA developer either uses the ML model developed by the data scientist or uses an available OOTB model.
3. The RPA developer prepares the datasets for training and evaluation to train/re-train the ML model according to the specific use cases.
4. When the training result is acceptable, the RPA developer creates the ML package to deploy to the production environment.
5. The ML skills are then available for the RPA developer to plug and play in any RPA workflow.
6. Post-production, the administrator manages the operations of the RPA bots and the ML skills.
7. The RPA developer continues to re-train the model with new data points to improve the model.

In cognitive automation, an RPA developer plays a broader role across the SDLC as a trainer and a data steward.

Understanding the final output of the cognitive automation life cycle and the RPA life cycle

Another important distinction between RPA and cognitive automation is related to the characteristics of the final output produced. RPA configures RPA bots. Cognitive automation develops ML skills that are leveraged by the RPA bot. The following figure illustrates the differences in the expectations of an RPA bot and an ML skill in initial deployment to the stakeholders:

Figure 1.5 – Expectations of an RPA bot and an ML skill in the initial deployment

An RPA robot performs according to a set of rules set out by the RPA developer. The result is black and white. Only the correctly coded robot is deployed into production. The output of the cognitive automation life cycle is a trained ML skill combined with an RPA workflow. The ML skill is trained up to the acceptable threshold of confidence to be deployed into production. In almost all cases, the ML skill is not 100% correct when it is first deployed. The ML skill is expected to improve over time.

Practical tips

Businesses have seen the power and reap the benefits of automation through RPA. However, RPA has its limitations. RPA can only automate rule-based tasks, thus limiting the scope of a process it can automate. In addition, rule-based tasks are usually lower-value work. To move up the value chain, combining AI is essential for businesses to maintain a competitive advantage. Here are some of the key takeaways to bring to your leadership:

• Technology companies have simplified AI technologies to make them accessible for consumption. AI is no longer a tool that only data scientists can leverage.
• The existing RPA team can start incorporating AI without needing heavy investments in springing up a new team.
• There are impactful cognitive automation use cases throughout the organization.
• It is now time to give the machine or the robot a brain.

Now that you have a good understanding of cognitive automation, let's explore the most commonly used OOTB models that you can try as a beginner in AI.