How Machine Learning Boosts The Power Of RPA
Robotic Process Automation is increasingly handling routine and time-consuming responsibilities across the enterprise and challenging how businesses operate
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Digital bots are gradually becoming an integral part of organizations. They are successfully automating routine and repetitive tasks to increase business productivity, but solitarily, are unable to provide depth or insight into the work they are performing. This is where machine learning steps in and helps breath more life into bot capabilities.
Robotic Process Automation is increasingly handling routine and time-consuming responsibilities across the enterprise and challenging how businesses operate. This form of digital transformation has become highly effective, allowing enterprises to become more competitive, efficient and flexible.
Previously, RPA tools were successful at executing specifically defined tasks, but they could not adjust to changing conditions or learn from experience. Now Machine Learning applies artificial intelligence (AI) capabilities to lend business context to tasks executed by RPA systems, enabling the latter to make better decisions and be more productive overall. For instance, when extracting field values from unstructured data, RPA can extract values based on the rules set. Machine learning “learns” the most common labels for fields, while working with a human trainer to confirm what it’s learning. The result is 10x faster path to automating, because explicit programming isn’t needed to gain quick improvement. By contrast, RPA without this learning requires a human to explicitly program these improvements.
The combination of RPA and machine learning establishes a symbiotic link of continuous improvement between execution and analysis. The transactional data generated by RPA tools also provides a steady stream of analytical fuel to drive AI capabilities forward and enable a deeper level of understanding.
Emerging analytical tools provide increasingly enhanced visibility and transparency into business events and data records. In this context, RPA, AI and analytics are less impactful when used in a sequential manner or as individual components. Rather, the process should be fluid, where RPA deployments generate data to refine AI capabilities on an ongoing basis. Those capabilities can then be applied to conduct ongoing and increasingly targeted, relevant and effective data analysis. The result is a wide range of new possibilities and enhanced business value in terms of cycle time reduction, scalability, innovation and ongoing productivity gains.
RPA at Work
In the banking industry, RPA systems can effectively perform many tasks associated with loan origination or account management. However, RPA typically can’t determine if the person making the inquiry is who they say they are. By analyzing unstructured data (e.g. say, reviewing a scanned passport image and matching it against a customer’s account record), machine learning creates a connection between doing and thinking in an automated environment.
Other applications of RPA and machine learning working in tandem include insurance claims and customer service. For auto insurers, sending claims agents out to review fender benders is expensive and inefficient. Today, many of these providers are exploring the use of computer vision applications with AI capabilities that can assess how an accident happened for fast approval of minor claims. For customer service departments deploying chat agents, sentiment analysis technology can detect anger, dissatisfaction or sarcasm conveyed by customers via text, and then flag at-risk customers and escalate issues to a human for proactive outreach.
The RPA Market Today
While the RPA market today is still relatively small, the innovation from leading RPA vendors to incorporate AI and machine learning technology into these offerings is pushing the industry forward at a rapid rate. In fact, India market is fast identifying the potential of RPA. According to Grand View Research Inc., the RPA industry is projected to reach $ 8.75 billion by 2024 and is gaining in support as users turn away from more traditional business process automation systems that often take hundreds of work hours and millions of dollars to set up. RPA by comparison is an easier and faster system to implement, which can be rolled out more cost-effectively through small, very specific projects and then build up to large scale implementations.
Early adopters from industries such as banking and finance, insurance and healthcare, are seeing the benefits of RPA implementation grow exponentially, from lowering operating costs and error rates, to improving service and compliance, to the ability to scale on-demand, and the application possibilities continue to broaden.
As enterprises begin to further explore and implement RPA technology, we will see bots’ abilities grow beyond automating routine tasks. RPA combined with advances in AI and machine learning is just the stepping off point for enterprises as they move away from legacy processes and closer towards the integration of this new technology that has the potential to disrupt how we think and do work across all industries.
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