• Sreenath Gurrapu

As a result of developments in Artificial Intelligence (AI) and its related domains, automation of knowledge and service labor is an important contemporary technical development. To characterize this phenomenon, we use the phrase Intelligent Automation. This advancement offers organizations a new strategic chance to boost their company worth. However, because academic research contributions that look at these processes are dispersed throughout a wide array of scholarly fields, there isn't much agreement on the most important findings and their ramifications. The intellectual condition and advancement of Intelligent Automation technologies in the knowledge and service sectors are thoroughly characterized in the first interdisciplinary literature review that we have conducted. We offer three important contributions in light of this review. First, we define Intelligent Automation and the technology that support it. Second, we present an intelligent automation model based on business value for knowledge and service labor and list twelve research gaps that prevent a thorough understanding of the process of realizing business value. We then offer a research agenda to fill these deficiencies. In the vast subject of artificial intelligence, it is one of the most current trends. Robotic process automation (RPA), low-code platforms, machine learning, and other cutting-edge methodologies and technologies are all part of IA. Important ideas in the book: Intelligent Automation (IA) – what is it? Why has IA's use been growing so quickly? What advantages does it provide to society, businesses, employees, and customers? How have top firms been able to fully utilize IA at scale and produce enormous efficiency improvements between 20 and 60%? What this book will teach you: Learn the insights from more than 100 successful (and unsuccessful) IA transformations. Take use of the greatest collection of 500+ IAs available to the public. Academics and industrial practitioners are now pursuing robust and adaptive decision making (DM) in real-life engineering applications and automated business workflows and processes to accommodate context awareness, adaptation to environment, and customization due to recent advancements in robotic process automation (RPA) and artificial intelligence (AI). With regard to the consideration of decision options, the developing research via RPA, AI, and soft computing offers advanced decision analysis methods, data-driven DM, and scenario analysis, which has advantages in many engineering applications. Achieving previously unheard-of levels of operational effectiveness, decision quality, and system dependability is possible with the new intelligent automation (IA), which combines RPA, AI, and soft computing. While AI has the cognitive abilities to simulate human behavior and process unstructured data via machine learning, natural language processing, and image processing, RPA enables an intelligent agent to eliminate operational errors and mimic manual routine decisions, including rule-based, well-structured, and repetitive decisions involving enormous amounts of data. When context-aware data, ambiguity, and consumer preferences are present in complex decision settings, new opportunities for automated DM procedures, problem detection, knowledge elicitation, and solutions arise.

Intelligent automation, Robotics process automation, OpenAI, ChatGPT, Artificial Intelligence

"Intelligent Automation – A Technical Research Around OpenAI, ChatGPT, Gen AI, and Vast Areas", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 8, page no.h68-h105, August-2023, Available :http://www.jetir.org/papers/JETIR2308710.pdf

"Intelligent Automation – A Technical Research Around OpenAI, ChatGPT, Gen AI, and Vast Areas", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 8, page no. pph68-h105, August-2023, Available at : http://www.jetir.org/papers/JETIR2308710.pdf