Digital enablers (digitalize processes right from the start)

Digital enablers digitize, improve the quality of and prepare unstructured data for integration with RPA. Generally, the digitalization of a process, such as customer self-service, can take much less time and cost than manual data entry.

Once an organization has digitalized its processes, and incorporated RPA, it will be in prime position for implementation of AI initiatives.

RPA (emulate procedural manual tasks via front-end interaction)

RPA can be likened to a virtual workforce automating highly repetitive tasks, based on defined rules. While RPA creates numerous opportunities for automation, it has key limitations when used in isolation. These limitations mean that, although extremely powerful, RPA can only be used on rule-based processes.

Examples of traditional RPA applications include:

• Data entry across multiple systems
• Applying rule-based data quality checks and transformations
• Running reports for multiple countries and collating into a single summary

AI enablers (use AI techniques to derive structure from unstructured data)

AI refers to the development of computer systems able to perform tasks normally requiring human intelligence where judgement is applied beyond simple decision trees, such as visual perception, chat and messaging dialog, reading emails, speech recognition, decision making and translation between languages.

AI enablers include, Machine Learning (ML), Natural Language Processing (NLP), and Optical Character Recognition (OCR), and can be used to identify patterns and insights to drive decisions and new sources of value.

Human-in-the-loop and process management (passing work from robot to human and back again to optimize use of human skills and experience)

The real power of Intelligent Automation comes in combination not only with RPA and digitization in general, but also with the input of people. Neither human nor machine alone can outperform human and machine working together.

If RPA was the first wave of robotics to transform business, digital enablers, AI and human-in-the-loop processing will be the next. With AI still in its early stages, there are instances where human interaction is critical. RPA+ allows for employee digital portals which facilitate human-in-the-loop work hand-off from RPA to agents.

Examples of Human-in-the-loop include:

• Chatbot conversational interfaces.
• Robot hand over to agent for granting approvals, or highlighting exceptions.

Big AI (identify patterns and insights to drive decisions and new sources of value)

“Big AI” Gives computers the ability to learn and predict from very large data sets. Big AI may use advanced analytics and big data, decision engines, machine learning or deep learning algorithms for certain processes.

Examples include:

• Machine learning to predict equipment failures.
• Expert systems to diagnose health conditions.
• Anomaly detection algorithms to identify cyber-attacks.

RPA+

RPA+ is the combination of RPA and add-on capabilities to significantly increase the scope of automation and improve productivity. Introducing these tools correctly can transform a large proportion of legacy processes.