The benefits of automation in the legal sector
Legal firms have to deal with a large volume of repetitive, routine tasks. They must complete them...
RPA can change the way businesses run in many sectors. It takes over manual labor and allows employees to focus on delivering value. However, strategic business automation often means going one step further. Machine learning coupled with RPA is a future-oriented solution for achieving digital transformation.
Though the majority of companies are still in the early stages of ML maturity, it is incorrect to think there is time to delay your ML efforts. If your company is not currently ML–minded, rest assured your competitors are, and the rate of AI’s development is bound to increase exponentially. Now is the time to future-proof your organization with AI/ML.
As businesses around the world focus on digital transformation, RPA is only one element of the toolset that can bring them closer to their goal. Machine learning, integrated properly with AI and robotic process automation, allows for rules-based automation with the added benefits of smart analytics and prediction. The result? A tireless digital workforce than not only takes over boring, repetitive tasks, allowing human employees to thrive, but also learns and comes up with its own solutions to arising problems.
Depending on the task at hand, businesses choose the type of automation with the best ratio of added value to cost of implementation. ML requires a larger investment than RPA, due to training data, a more robust infrastructure, and development work provided by highly skilled experts. The ROI can be well worth it, however, thanks to deeper insights, better automated decision-making, and the ability to analyze both structured and unstructured information.
Thanks to machine learning, RPA can go beyond processing (gathering, sorting, calculating and reporting) data to begin using it. Automated processes would operate based on the continuous analysis of incoming information, and learn to act smarter over time. This is especially beneficial for businesses dealing with large volumes of unstructured data. ML is capable of gathering insights and improving them over time, while RPA executes them, working in tandem for best results.
Error rate in 59% of content
Accuracy for 75% of content
Cases fully automated
Cases with major costs & time savings
Every organization is plagued by tasks that shouldn’t be falling onto any team member’s...