I’ve noticed recently that Robotic Process Automation, Machine Learning, Natural Language Programming and Artificial Intelligence are terms which are being used inconsistently in conversations around the topic of Intelligent Automation. Whilst it’s correct that they are all within the same field it’s not correct to consider these terms as being interchangeable.
Whilst for the initiated this can help sort out who knows what (and who is simply attempting to use the latest jargon in conversation…) it makes it difficult for those in industry who may be aware they need to initiate some form of Intelligent Automation to their organisation to understand the basic benefits. Actually, there are issues knowing where to start. Sometimes it’s worse than confusion and they decide to ride out the storm in the hope it blows over, as per some form of fad, and they will be ok. Strategically, this isn’t particularly sensible. Let’s discus the first steps on your digital travels.
If these terms are not interchangeable, what do they mean? In relative layman’s terms, here are the generally accepted generic references.
“Preconfigured software instance that uses business rules and predefined activity choreography to complete the autonomous execution of a combination of processes, activities, transactions, and tasks in one or more unrelated software systems to deliver a result or service with human exception management.”
Therefore RPA and Integration:
RPA is highly process-driven — it is all about automating repetitive, rule-based processes that typically require interaction with multiple, disparate IT systems. For RPA implementations, process workshops are required in most cases in order to map out the existing “in situ” process and document clearly.
“The combination of Machine Learning, reasoning, hypothesis generation and analysis, natural language processing and intentional algorithm mutation producing insights and analytics at or above human capability.”
Important to consider for AI (ML) and Integration:
The quality of your data should be controlled to ease deployment yet ML is the previous step that can be used to learn from data samples to use patterns for eventual non-manual intelligent integration.
To put it very simply; RPA is based on “doing” whereas AI and ML are both concerned with “thinking” and “learning”.
There are many common misconceptions:
This last point is an oft uncharted area. Business areas need to be involved from the very start. Here’s why it should come first:
RPA is the first step on the overall AI ladder for an organisation that wants to be efficient and effective. It can allow your employees time to concentrate on your business and not consume precious time with manual tasks.
We understand the process from RPA to full AI. ACS can help you with your business strategy and automation transformation as we are practitioners as well as technologists. To ensure you do what’s best for you or to discuss how Intelligent Automation can work for your business, get in touch with ACS.