Quantitative Evaluation of the Impact of Artificial Intelligence on the Automation of Processes

Justiniano Felix Palomino Quispe, Camilo Fermín García Huamantumba, Elvira García Huamantumba, Arturo García Huamantumba, Edwin Eduardo Pacherres Serquen, Luis Villar Requis Carbajal, Alisson Lizbeth Castro León, Leopoldo Choque Flores, Domingo Zapana Diaz and Carlos Enrique Guanilo Paredes

Data and Metadata, 2023, vol. 2, 101

Abstract: Introduction: in the current era, Artificial Intelligence (AI) has profoundly transformed the operation and management of business processes, being essential for competitiveness. This article focuses on quantitatively evaluating the impact of AI on the automation of business processes, seeking to support decision making. Objective: this study aims to carry out a quantitative evaluation of the impact of AI on business processes. Robust methods are used to measure and analyze key variables related to AI adoption. Methods: the methodology combines secondary data and company surveys. Public business databases are accessed and financial data is collected, in addition to analyzing Key Performance Indicators (KPI). A random selection of companies is made for the surveys, a structured questionnaire is used and the data is subjected to rigorous statistical analysis. Result: quantitative results show significant impact of AI on business processes. The average reduction in operating costs reaches 26 %, the improvement in the quality of products and services is 30 %, and an average increase of 20 % in profit margins is observed. Possible moderators that influence these results are identified. Conclusion: this quantitative study supports the strategic importance of AI in business, demonstrating substantial improvements in efficiency, quality and decision making. Despite its limitations, it offers a solid framework for decision-making and future research in the field of AI and business automation

Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:dbk:datame:v:2:y:2023:i::p:101:id:1056294dm2023101

DOI: 10.56294/dm2023101

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