How RPA and machine learning work together in the enterprise
RPA and Machine Learning are key drivers of enterprise automation that can help organizations build interconnected and efficient digital workspaces.
Robotic Process Automation (RPA) continues to attract a lot of attention and discussion due to the increasing need for digitizing business processes and connecting applications and disparate data sets for smoother workflows. Automating repetitive tasks to increase efficiency while minimizing human errors is an appealing proposition as businesses focus on digital innovation and monetizing their investments in technology.
Bots, or pre-programmed automated systems, do not tire and can replicate and repeat tasks at any scale or speed, thus aiding humans by freeing them up to focus on higher-value tasks. Intelligent Automation, a combination of Machine Learning (ML) and Artificial Intelligence (AI) with Robotic Process Automation goes beyond simply automating repetitive tasks, but lends an additional layer of human-like perception and predictive capabilities.
The benefits of combining RPA and ML
Automating tasks is a good way to free up employees for more important work and eliminate human error, but by itself, simple automation isn’t enough for scaling organizations. To truly create efficiencies with automation, you need machine learning which can automate more complex tasks, allowing any organization using both technologies to apply automation to its core operational functions.
RPA and ML both provide opportunities for going beyond just automating routine business processes, but also improving the way they are undertaken using data-driven insights and self-learning capabilities of the machine. Since RPA works by collecting and structing data, it can help train machine learning algorithms, and this structured data enables the RPA tool to make autonomous decisions or recommendations which can then be ratified and executed by a human.
RPA and machine learning are technologies that complement and add value to one another. RPA systems are particularly effective in structuring data and digital workflows. With the help of machine learning, RPA systems demonstrate greater capabilities in handling newer and incrementally challenging decisions with absolute precision.
RPA bots with ML can self-learn and improve over time
You may have heard of robotic process automation (RPA) and machine learning (ML), and you might be wondering how the two technologies work together. The answer is that RPA and ML both provide opportunities for automating routine business processes. Because RPA collects data, it can help train machine-learning algorithms. Because an RPA tool has access to structured data, it can make decisions or recommendations based on that data. And because ML can automate more complex tasks, an organization using both technologies will find many new opportunities to apply automation in its day-to-day operations.
You may have heard of robotic process automation (RPA) and machine learning (ML), and you might be wondering how the two technologies work together. The answer is that RPA and ML both provide opportunities for automating routine business processes. Because RPA collects data, it can help train machine-learning algorithms. Because an RPA tool has access to structured data, it can make decisions or recommendations based on that data. And because ML can automate more complex tasks, an organization using both technologies will find many new opportunities to apply automation in its day-to-day operations.
Machine learning and AI are data-driven and rely on large, clean data sets. The power of machine learning comes from algorithms that analyze data, find patterns and make predictions based on those patterns. Machine learning isn’t a replacement for human intelligence; it’s a tool to be used by humans. ML can help identify patterns in the data that more traditional business intelligence tools might miss, but ultimately these tools are meant to augment human capabilities — not replace them. On the other hand, RPA is rule-based and excels at automating structured processes.
RPA can’t use context or interpret unstructured information like video or audio files. In addition, RPA doesn’t have the ability to learn to complete new tasks without being programmed to do so. This predictable nature makes RPA ideal for automating structured processes like accounts payable or claims processing but less effective at handling exceptions or non-standard inputs like handwritten notes or scanned documents.
RPA can’t use context or interpret unstructured information like video or audio files. In addition, RPA doesn’t have the ability to learn to complete new tasks without being programmed to do so. This predictable nature makes RPA ideal for automating structured processes like accounts payable or claims processing but less effective at handling exceptions or non-standard inputs like handwritten notes or scanned documents.
RPA can run workflows even with unstructured data such as text, images, and more when combined with machine learning. Intelligent automation systems with underlying ML programs can also make decisions on a large scale such as credit underwriting, for example, with great speed and accuracy which simple RPA alone cannot. Hence, the two technologies, when combined, can support intelligent end-to-end process automation and scaled up as processes and data sets become more complex.
We help build intelligent automation for your business
RPA and machine learning complement each other nicely: machine learning identifies patterns that can be used to automate processes, while RPA automates those processes themselves. Together, they represent a powerful approach for automating high-volume tasks once reserved for human workers. The use cases of intelligent automation demonstrate just how these technologies drive speed and accuracy with automation that doesn’t impact human jobs but instead helps them perform better while lowering costs through improved productivity.
Despite the complex-sounding explanations of how automation or AI technologies work, their implementation is neither time- nor capital-intensive. The key is the right development and implementation partner who can help you build a strategy and rollout automation systems that are easy to use and do not require long for users to get accustomed to. RPA deployments can be built and scaled up as and when business needs evolve, be it to connect existing SaaS applications and make them cross-talk or building custom systems from the ground up for future-ready digital transformation.
