The path to end-to-end intelligent automation
Companies are always looking to optimize their automation processes by making them smarter and more efficient. While one size does not fit all, enterprises need to look at customizable and scalable solutions for their automation journey. End-to-end automation can automate almost every part of the production, reducing human errors and speeding up the processes.
Intelligent Automation (IA) combines Robotic Process Automation (RPA) with artificial intelligence (AI) to accomplish rapid end-to-end business automation and empower owners to boost their digitalization. IA supports all processes of the automation cycle and develops them in line with new digital requirements without pre-set instructions and structured data inputs.
It also allows businesses to reimagine their possibilities in terms of workflows, by optimizing the relation between robots, humans and work processes. Assigning mundane and repetitive tasks to robots liberates human workers and lets them use their unique human abilities. Here is how end-to-end intelligent automation is structured.
Robotic Process Automation
The starting point to achieve intelligent end-to-end automation of your workflow is RPA. Its main purpose is to reduce human input in computer applications to allow workers to focus on more valuable tasks for the company, such as improving customer interaction or developing business strategies. It is not meant to replicate human-like intelligence, but to mimic rudimentary human tasks.
RPA is a software program that runs on a worker’s computer or mobile device and generates a sequence of commands that will then be executed by bots under defined sets of business rules. These bots can work 24 hours without compromising on accuracy and efficiency, which is important for reducing the risk of errors that humans are prone to.
The software can be used to automate workflow, infrastructure and back-office processes which are labor intensive. This tool interacts with the existing IT infrastructure and does not require a complex system integration. This provides a quick and cost-effective solution for companies to start their digitalization process. Furthermore, RPA is scalable, meaning that when a process changes, the software can be adapted by changing a few lines of software code.
While RPA works by mimicking human activities, artificial intelligence simulates human behavior to make smart decisions and respond to more complex tasks. AI-powered tools automatically observe the work activities people are doing, identify optimal workflows, and propose an automation path. For example, AI-powered platforms can extract useful information from unstructured data in the form of chat conversation, audio and video, which is crucial to making end-to-end automation possible.
On top of that, integrating machine learning can contribute to deductive analytics and predictive decisions that increasingly approximate the outcomes that can be expected from humans. Machines learn from past experiences using historical data and different algorithms that will inform future decisions.
By combining RPA with cognitive technologies such as machine learning and AI, companies can automate higher-order tasks that in the past required the judgment capabilities of humans. RPA complements AI by generating useful insights that are then used to handle complex cases. This type of intelligent automation will not only help businesses optimize processes and manage their workflow more efficiently, but it will also help them stay competitive in an ever-changing digitalized environment that requires the most innovative solutions.
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