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.
Hemant Singh Sengar Reviewer
11 Oct 2024 05:42 PM
Approved
Relevance and Originality
The article addresses a highly relevant and timely issue within the digital landscape—how GAI is transforming Robotic Process Automation (RPA). The integration of tools like Chat GPT and advancements in GAI represents a significant shift in how organizations automate processes and improve operational efficiency. The originality of the paper is evident in its exploration of transparency in AI systems, particularly through the lens of XBRL technology. This unique angle provides a fresh perspective on addressing the ethical considerations associated with the deployment of RPA in various sectors.
Methodology
The methodology appears to involve a comprehensive review of existing literature and a case study of the RPA sector. However, details regarding how the literature was selected and analyzed would strengthen the methodology section. Including specific criteria for literature inclusion, such as date range, publication types, and the frameworks used for analysis, would provide greater transparency. Additionally, elaborating on how the taxonomy for RPA was developed and its implications for transparency in GAI usage would enhance the overall rigor of the methodology.
Validity & Reliability
The validity of the claims made in the article is supported by the integration of existing literature and established concepts like XBRL. However, to enhance reliability, the paper could benefit from empirical data or case studies showcasing real-world applications of GAI in RPA and the associated ethical considerations. Addressing potential biases in the literature or contrasting views on GAI’s role in RPA would also enrich the discussion and provide a more balanced perspective.
Clarity and Structure
The article is generally well-structured, presenting a coherent flow from the introduction of GAI-enabled RPA to the discussion of transparency and ethical considerations. To improve clarity, the use of subheadings to delineate different sections—such as "Introduction to GAI-Enabled RPA," "Transparency Technologies," and "Ethical Implications"—would enhance readability. Additionally, bullet points or tables to summarize key findings or recommendations could provide quick reference points for readers.
Result Analysis
The analysis of GAI's impact on RPA and the proposed focus on transparency is a significant strength of the paper. Highlighting the benefits of RPA, such as scalability and continuous operation, provides a strong rationale for its adoption. The discussion around creating a taxonomy for RPA and the integration of ethical considerations adds depth to the analysis. However, to strengthen the results, the article could provide specific examples of how transparency technologies like XBRL can be applied in the RPA sector. Furthermore, discussing potential incentives for organizations to adopt transparent practices in GAI-enabled RPA would offer practical recommendations for stakeholders.
IJ Publication Publisher
thankyou sir
Hemant Singh Sengar Reviewer