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Machine Learning

RPA provides a quick method of automation; however, it has its own limitations. While RPA works well in situations where processes and decision-making are clearly defined, it is difficult for RPA alone to be smart. In any RPA-based automation, it is important to split the larger processes into 2 categories: Silent Automation and Human Intervention

There are many instances where it is not practical to automate a big process fully. Most processes require a human component to make decisions and continue to the next level. In such cases where a vast amount of knowledge cannot be effectively defined into algorithms, it is not conducive to do RPA and human intervention is necessary. That’s when Machine Learning (ML) comes into play to solve the “knowledge problem.”

Machine Learning (ML) has matured to a degree where it can be applied to solve real-life problems. ML essentially works on the principle of encapsulating large amounts of data (or knowledge) into a form of mathematical model that can be then utilized to help solve complex problems through automation.

The Machine Learning Process

Machine Learning works as follows: Employees work in a particular department, or do certain tasks, for many years on end and apply the knowledge gained to decide what actions to take and eventually that creates a process. ML is applied to a multitude of problems where there is access to large volumes of historical data which can be used to predict or define decision-making in certain areas. Unlike developing an algorithm, ML is based on building a repository or knowledge base. Think introducing a SMART AGENT into the RPA process. The Digital Worker (BOTS) becomes “smarter” and more efficient by gathering additional information to make more accurate decisions.

Many RPA platforms, like Blue Prism, UiPath, and Automation Anywhere have developed robust ecosystems and are implementing their own powerful Machine Learning algorithms. With the combination of RPA and ML, BOTS are now capable of capturing previously unidentifiable data like signatures, images (in under 100 milliseconds), complex application automation, and image recognition optimization.

RPA Technology Partnerships

ML uses Artificial Intelligence (AI) that enables systems to learn from data without being explicitly programmed. ML is based on the premise that technology can process data and learn from the data, to help make better decisions based on new information without constant supervision of programmers.

For example, Netflix and Spotify use ML to store what movies or music a subscriber is watching or listening to in a repository and will be able to create a pattern from that data, improving accuracy as it learns and offers movies/music specifically tailored to a user’s liking. Spotify’s ML algorithm uses your data (i.e., the songs you listen to) to create a weekly two-hour long playlist of music that it thinks the subscriber will enjoy.

Netflix, YouTube, Amazon, and many other services use ML in a similar fashion by employing your viewing/browsing history to recommend content or products that they think you will watch/buy. It may seem as if companies like these know you as well or even better than you know yourself! That is because the more you use these, or any other, services, the more they learn about your viewing/purchasing pattern, resulting in recommendations that are more and more accurate. This is machine learning in action.

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