ROBOTIC PROCESS AUTOMATION
Robotic & Process Automation
In this step, we review proposed processes for automation and add qualified processes to backlog. We identify data needed for matrix and assign data collection
SCORING AND PRIORITIZATION
In this step, we score and prioritize processes leveraging matrix data and kick off preliminary process flow design discussions.
In this step, we Draft RPA automated functional design for selected sub-process, Identify opportunities to optimize before automation (time-boxed) and Discuss automation progression/increments for end-to-end processes.
RPA and intelligent automation
In order for RPA tools in the marketplace to remain competitive, they will need to move beyond task automation and expand their offerings to include intelligent automation (IA). This type of automation expands on RPA functionality by incorporating sub-disciplines of artificial intelligence, like machine learning, natural language processing, and computer vision.
Intelligent process automation demands more than the simple rule-based systems of RPA. You can think of RPA as “doing” tasks, while AI and ML encompass more of the “thinking” and “learning,” respectively. It trains algorithms using data so that the software can perform tasks in a quicker, more efficient way. As artificial intelligence becomes more commonplace within RPA tools, it will become increasingly difficult to differentiate between these two categories.
RPA and artificial intelligence
Robotic process automation is often mistaken for artificial intelligence (AI), but the two are distinctly different. AI combines cognitive automation, machine learning (ML), natural language processing (NLP), reasoning, hypothesis generation and analysis.
The critical difference is that RPA is process-driven, whereas AI is data-driven. RPA bots can only follow the processes defined by an end user, while AI bots use machine learning to recognize patterns in data, in particular unstructured data, and learn over time. Put differently, AI is intended to simulate human intelligence, while RPA is solely for replicating human-directed tasks. While the use of artificial intelligence and RPA tools minimize the need for human intervention, the way in which they automate processes is different.
That said, RPA and AI also complement each other well. AI can help RPA automate tasks more fully and handle more complex use cases. RPA also enables AI insights to be actioned on more quickly instead of waiting on manual implementations.
RPA and hyperautomation
Hyperautomation is the concept of automating everything in an organization that can be automated. Organizations that adopt hyperautomation aim to streamline processes, and they leverage technologies such, as artificial intelligence (AI) and robotic process automation (RPA), to operate certain workflows without human intervention.
We are ready to assess your challenge and help you with it. Schedule a call to start.