Suresh, with his expert knowledge, did the judging in the robotic competition at IEEE SOUTHEAST 2024 conference in Atlanta.
In our regular life, we do some tasks daily. For example, for an organization, someone has to update daily/weekly attendance and task records of each team member, and sometimes it happens that a person forgets to add some task to someone, so there is a risk in tracking each team member’s details manually. Unlike humans, who may skip a process step or not be consistent in processing a transaction, an RPA (Robotic Process Automation) robot performs tasks without bias or any variation. This supports your automation agenda for managing and mitigating risk.
In traditional workflow automation tools, a software developer produces a list of actions to automate a task and interface to the back-end system using internal application programming interfaces (APIs) or dedicated scripting language. In contrast, RPA systems develop the action list by watching the user perform that task in the application’s graphical user interface (GUI) and then perform the automation by repeating those tasks directly in the GUI. This can lower the barrier to the use of automation in products that might not otherwise feature APIs for this purpose.
It is a convenient technology for scheduling and institutionalizing various process-oriented jobs or tasks. It is known as a critical component of digital transformation initiatives. The benefits of implementing Robotic Process Automation are well known. Reduce operational costs, significantly improve processes, reallocate resources to higher value features, improve customer service, improve productivity and quality, etc., the list continues. However, it is also essential for business leaders to understand and analyze its potential risks to optimize their investment in technology. Technical obstacles, security issues, and defective recruitment and implementation processes can reduce profitability and hinder staff efficiency and business operations.
RPA combines APIs and user interface (UI) interactions to integrate and perform repetitive tasks between enterprise and productivity applications. By deploying scripts that emulate human processes, RPA tools complete the autonomous execution of various activities and transactions across unrelated software systems.
This form of automation uses rule-based software to perform business process activities at a high-volume, freeing up human resources to prioritize more complex tasks. RPA enables CIOs and other decision-makers to accelerate their digital transformation efforts and generate a higher return on investment (ROI) from their staff.
In order for RPA tools in the marketplace to remain competitive, they will need to move beyond task automation and expand their offerings to include intelligent automation (IA). This type of automation expands on RPA functionality by incorporating sub-disciplines of artificial intelligence, like machine learning, natural language processing, and computer vision. Intelligent process automation demands more than the simple rule-based systems of RPA. You can think of RPA as “doing” tasks, while AI and ML encompass more of the “thinking” and “learning,” respectively. It trains algorithms using data so that the software can perform tasks in a quicker, more efficient way.
Like any other technology, there are always some risks associated with RPA. Robotic process automation(RPA) is an advanced technology programmed to do precisely what it is told to do. RPA technology differs from artificial intelligence (AI) because the RPA bots can not make judgments or learn from data patterns. They can only mimic human actions. The programming of the RPA bot has to be completed with the exact functionalities the bot must be able to execute. Organizations have unrealistic goals and expectations for RPA implementation. At that point, risks occur. Misusing RPA for duties outside of its capabilities causes risks. RPA is supposed to deliver good quality values. However, RPA can fail to deliver on its promises due to the unrealistic goals set by the organization. With expert knowledge, Suresh also worked as a JUDGE in the IEEE robotic competition at the South Asia 2024 conference.
Author: Suresh Dodda
Suresh’s technical prowess extends to AI/ML, where he has contributed to research papers. His effective management skills have consistently ensured timely project delivery within allocated budgets. His extensive international experience includes working with esteemed clients such as Dubai Telecom in Abu Dhabi, Nokia in Canada, Epson in Japan, Wipro Technologies in India, Mastercard in the USA, National Grid in the USA, Yash Technologies in the USA, and ADP in the USA.
Within core industries such as banking, telecom, retail, utilities, and payroll, Suresh possesses a deep understanding of domain-specific challenges, bolstered by his track record as a technical lead and manager for globally dispersed teams.
Suresh’s professional stature is further underscored by his membership in prestigious organizations like IEEE. His contribution as a journal reviewer for IGI Global and Judge for technology innovation awards like GLOBEE highlight his knowledge and expertise in technology and research.
Published By: Aize Perez
