Continuing our analysis of the Automation landscape and different automation technologies through the lens of innovation, we will be using a different Innovation framework to analyse expand on the much-debated subject. Feel free to refer to Innovation framework an article published by our head of RPA Deepak Sharma in 2019 that addresses the subject in great detail. We shall continue along a similar line of analysis with a view to aligning the different automation technologies against the four automation categories that are derived based on an organisation’s core automation focus. i.e. task, process, data, or customer, and further explain how Software Robotics (beyond RPA automation) can really be realised as an architectural innovation by combining a few of the key automation technologies together.
Phil Fersht, CEO at HFS Research, who introduced the term RPA in 2012, recently shared this new manifesto for RPA in which, one of the points he makes is to “consider dropping the word Robotics from RPA when all RPA does is to automate tasks”. We too believe, that the term, which has been used to market RPA, creates confusion. In practice, RPA only justifies a third of what is meant by Robotics.
A good reference point is the article Software Robotics = AI + Data Sciences + RPA, which gives mention to the IEEE definition of Robotics, and explains how a Robot must have “Sense, Compute, and Act” as three key components and that RPA in its current UI automation form only equates to the “Act” part of Robotics in the context of Software Robotics.
Gartner, in their very first Magic Quadrant report on RPA, stated that ‘UI automation integration features in RPA do not have the capabilities to adapt as how a human would and thus these automations are not equivalent to be classed as Robots.’ Unless you combine Sensing and Computing capabilities to such scripted automations, they cannot be Software Robots. But, by combining AI and Analytics capabilities, an RPA automation can be transformed into fuller Software Robotics or true Digital Workforce.
Referring back to the automation technologies from An Introduction To The Automation Landscape And 2020 Trends, these can be aligned to the four automation categories. Task, Process, Value and Knowledge-based Automation, as in the below diagram, by having to create this innovation quadrant with the degree of technology innovation on one axis and the degree of process transformation on the other.
RPA as an automation technology fits into the Task automation category, whereas other major automation technologies such as iBPMS and iPaaS fit into the Process and Knowledge-based automation categories, respectively. AI technologies cut across customer-focused and knowledge focused categories, and so do Process Mining & Discovery between Process and Task-based automation categories. Similarly, other automation technologies can be aligned to respective categories, for instance, Low code Apps are becoming a new way to engage with customers, to align with value-based automation, for simple workflows to replace static email and spreadsheets, allowing digital customer interactions via the web or mobile apps. Data Analytics aligns to the Knowledge-based automation category.
We touched upon the concept of Innovation Streams in the previous article mentioning how three levels of innovation can take place in an organisation simultaneously namely- Incremental, Radical and Architectural innovation. Incremental and Radical levels of innovation can be aligned to the two main axis, i.e. Process Innovation and Technology Innovation, and the four quadrants of the above model with task-based automation being aligned with incremental innovation from both process and technology innovation standpoint as an example, and similarly, knowledge-based automation being aligned with radical innovation from the technology innovation point of view.
More importantly, the third level of innovation, i.e. Architectural Innovation occurs when new ways are realised to combine existing technologies and components. Software Robotics is a good example of how a number of these automation technologies can be combined in a specialised way to provide for the “Sense, Compute, and Act” capabilities in a Software Robot. A Software robot that can-
1) Perform real-time personalised interactions with a human through chat or voice channels (Sense),
2) Analyse user inputs to determine optimal processing actions to be performed, handle exceptions, self-correct and maintain the state of interactions (Compute), and
3) Execute automations in real-time on computer applications on the user’s desktop or schedule them to be run unattended on a server (Act)
Additionally, the software robot provides touchpoints to integrate with other automation technologies such as iBPMS to allow execution triggers from within an e2e process orchestration, iPaaS to execute automation steps via API façade’s or as asynchronous events through an ESB, or for triggering interactions with the robot via Physical Devices (Wearables, IoT) or Apps.
Contact us at SQA Consulting, to see how we may assist you in developing the necessary skills needed for implementing RPA projects.
