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SAP Intelligent RPA: Why Care?

Robotic Process Automation for the Intelligent Enterprise

You’ve heard it before: Robotic Process Automation (RPA) automates business process steps to accelerate time-to-value, reduce human error, and more! These assertions are believable, because companies have successfully implemented the technology and tapped into its value.

So…what more is there to know? Well, plenty.

A Simple Concept Achieves Outcomes at Scale

Conceptually, RPA has roots in screen scraping. From our perspective today, this starting point may seem technologically rudimentary. However, it’s important to bear in mind the simplicity of an RPA bot to understand the magnitude of outcomes that it helps achieve.

You see, RPA mimics what a human user does interacting with applications on the front-end. Similar to a person, a bot relies upon defined business rules to complete tasks in a process. For example, an operational purchaser updates Purchase Orders with confirmation details from incoming emails with clicks, typing, and toggling among applications and screens. When an RPA bot is applied, it automates the clicks, typing, and toggling needed to complete the task.

The purchaser is relieved of a boring task that drains time, energy, and motivation. The purchasing department benefits from efficiencies gained at scale (including fewer errors). And finally, attention may be directed further towards strategic tasks requiring human judgment and mutual collaboration – otherwise known as the interesting parts of people’s jobs.

One Part of the Whole: RPA and End-to-End Business Processes

Now that we know a bit more about what RPA does, let’s take a look at what is possible when RPA is combined with other intelligent or AI technologies.

RPA with Situation Handling together can help people exercise judgment faster. To put it simply, RPA reduces errors via task automation, and Situation Handling recognizes and proposes solutions for anomalies. The relevant information is gathered about a specific business issue. These details, along with resolution options, are presented to the specific user. Usually, with a single click, the user decides on a resolution and it is automatically implemented.

RPA with Machine Learning together can further collaboration, as Machine Learning offers predictive capabilities. A Machine Learning algorithm draws from historical data in order to offer insights on likelihood.

Let’s extend the previous example:

  • We know how RPA automates updating Purchase Orders with confirmation details from emails
  • In this case, Situation Handling notifies about missing Purchase Order confirmations and proposes options to avoid late delivery. Situation Handling can also alert to a quantity deficit and propose alternate suppliers
  • On top of this, Machine Learning predicts a shipment’s delivery date

As a result, the purchaser quickly and preemptively addresses an issue that could have a domino effect of implications (production delays and re-scheduling) on an end-to-end business process.

A Baseline Intelligent Technology

We’ve taken a look at RPA from several angles: the standalone task, the outcomes it helps achieve scaling completion of a task, and the benefits of combining it with other innovations such as Situation Handling and Machine Learning. Let’s keep in mind that RPA is a baseline innovation for the Intelligent Enterprise; it all starts with one dull task that needs to be done.

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  • Thanks for putting this together, very informative and structured.

    A couple of comments:

    1. Can you provide links to a set of resources for IRPA.
    2. What distinguishes intelligent process automation.
    3. What are its implications on customer satisfaction, loyalty, and experience.
    4. Since there are multitude of decisions and choices that enterprises need to make along the way, it poses a huge challenge to automation. How would this be addressed.

     

    Thanks,

    Osman.