Transparent Peer Review By Scholar9
Revolutionizing Robotic Process Automation (RPA) with Autonomous AI Agents: How GPT and AI Models are Shaping the Next Generation of Process Automation
Abstract
Robotic Process Automation (RPA) powered by Generative Artificial Intelligence (GAI) has emerged as a critical component of the digital ecosystem. The emergence of Chat GPT, a publicly available tool created by Open GAI, and its underlying technology, Generative Pretrained Transformer (GPT), are expected to significantly boost the growth of generative AI in the upcoming years. GAI-enabled RPA mimics human interactions with applications and enables direct access to systems via APIs. When compared to human execution, RPA offers greater benefits including scalability, perpetual lifetime, and 24x7 execution. Process automation is not a new technology, but due to significant advancements in GAI, which RPA utilises, it has emerged as its own solution category. An overall strategy for resolving the matter is to enhance transparency. The study suggests using technology to improve data accessibility and readability while using artificial intelligence. "Transparency technology XBRL (eXtensible Business Reporting Language)" is integrated with this goal in mind. Sunstein (2013) states that XBRL is a component of the regulatory choice architecture used by governments. XBRL has a taxonomy associated with it. The study creates a taxonomy for RPA in order to make the use of artificial intelligence more transparent to the public, while also incorporating ethical considerations. Selected as a business case is the rapidly expanding RPA sector. The paper focusses on improving GAI in a way that is consistent with human values. How may incentives be offered to prevent GAI systems from becoming potential items that raise ethical questions. The paper's key finding is that, while transparency technologies simultaneously offer ways to reduce such dangers, they also highlight moral concerns related to GAI-enabled RPA. This is a human-written paper, devoid of any AI-generated text.
Saurabh Ashwinikumar Dave Reviewer
11 Oct 2024 04:06 PM
Not Approved
Relevance and Originality
The research article addresses a timely and significant issue in the digital ecosystem, focusing on the integration of Robotic Process Automation (RPA) and Generative Artificial Intelligence (GAI). By discussing the implications of tools like ChatGPT, the article showcases originality in its examination of how GAI enhances RPA functionalities. The focus on transparency through technologies like XBRL adds a unique angle that could contribute valuable insights to both academic and practical discussions in automation and AI ethics.
Methodology
The article presents a conceptual framework for understanding the role of GAI in RPA, which is a relevant approach given the evolving technological landscape. However, it would benefit from a more detailed explanation of the methodologies employed in developing the taxonomy for RPA and the criteria used to evaluate transparency. Additionally, including empirical data or case studies to support theoretical claims would enhance the methodological rigor of the study, allowing for a more comprehensive understanding of the practical implications of the proposed framework.
Validity & Reliability
The findings of the research article rely heavily on the theoretical basis established in previous works, particularly concerning XBRL and its application in RPA. While the theoretical insights are sound, the article lacks empirical validation to support its claims, which raises questions about the reliability of the conclusions drawn. To improve this aspect, the article could incorporate quantitative or qualitative data that substantiate the relationship between GAI, RPA, and transparency.
Clarity and Structure
The article is generally well-structured, with a logical flow from the introduction of concepts to the implications of GAI in RPA. However, some sections could benefit from clearer definitions and explanations, particularly regarding technical terms and concepts. The use of headings and subheadings can be enhanced to improve readability and help guide the reader through the various arguments. Overall, a more systematic approach to organizing the content would aid in better comprehension of the key points.
Result Analysis
The analysis presented in the research article highlights the potential of GAI to enhance RPA's transparency and ethical considerations. Nonetheless, the discussion of results lacks depth, particularly in how the proposed taxonomy can be applied in real-world scenarios. The article would be strengthened by a thorough examination of the implications of the findings, including potential challenges and limitations in implementing the suggested strategies in the RPA sector. Providing examples or case studies would also enrich the analysis and offer practical relevance to the theoretical framework proposed.
IJ Publication Publisher
ok sir
Saurabh Ashwinikumar Dave Reviewer