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Many enterprises have leveraged Robotic Process Automation (RPA) to automate repetitive, rule-based business processes across various processes. This helps free the human workforce from mundane tasks and focus on higher-level activities that require more strategic work and thinking. However, RPA has certain shortcomings when implemented in more complex automated solutions.
With many technologies evolving and reshaping the end-to-end business processes across industries in recent times, enterprises are looking for ways to be more strategic with automation. It’s time enterprises go beyond RPA and automate more of the end-to-end processes.
This is where the role of Intelligent Automation (IA)comes into play.
Intelligent Automation is increasingly becoming a core part of larger transformation programs. Bringing intelligence to an enterprise’s automation program enables end-to-end automation and opens new possibilities.
Intelligent Automation combines AI and Automation to perfect high-functioning responsibilities that demand reasoning, judgment, decision-making, and analysis. It gives employees more time to invest in value addition tasks like having conversations and making connections with customers. Intelligent Automation, also known as hyper-automation, is increasingly redesigning the functional dynamics of organizational digitization programs.
As enterprises move along the automation maturity curve, many barriers keep them from adopting automation at scale, from failure to identify the right processes to automate to lack of IT readiness. With IA and comprehensive models, it’s not surprising that about 50% of enterprises see the value of end-to-end automation tools to achieve meaningful process optimization, according to a survey report by EdgeVerve and SSON.
Enterprises that remained focused on a single department and deployed tactical automation in finite use cases are now keen to implement IA as a strategic enabler of end-to-end automation.
Here are a few examples:
By applying IA, the automotive industry can forecast and adjust production as per the changes in supply and demand. Intelligent Automation helps customize the workflows to enhance efficacy and minimize the errors in procurement, production, support, and other domains without the intervention of human force.
Drug production needs precise calibration of equipment and measurement of product a tremendous amount of data collection, collation, processing, and analysis. Drug trials are not considered productive without any analysis and tested results. As a manual approach is likely to make calculation blunders, it is advisable to implement Intelligent Automation to boost speed, quality, and production.
The healthcare industry is another example of using Intelligent Automation with natural language processing (NLP) to support a consistent approach for data collection, analysis, diagnosis, and medication. The application of chatbots in remote healthcare appointments necessitates less human intervention and often a shorter time for analysis.
Intelligent Automation in the insurance industry can virtually wipe out the requirement for labor-intensive rate calculations and can streamline the paperwork processing, such as claims and appraisals. Additionally, IA facilitates insurance firms to adhere to compliance regulations effortlessly by guaranteeing that requirements are met.
Employee onboarding and offboarding
Onboarding and offboarding require constant hours of employee labor. While the paperwork, payment processing, training, and obtaining resignation letters are all simple tasks, they can be tiresome and time-consuming. Nevertheless, these processes can be sleeked and completed in a timely manner with Intelligent Automation.
With its ability to automate complex processes, Intelligent Automation solutions will lead businesses toward more adaptive processes that assist enterprises in unearthing bigger ROI. Hence, modern enterprises must exploit this disruptive technology to its fullest potential with an eye on more complex use cases.
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