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Published: 2023-10-15
DOI: https://doi.org/10.56294/dm2023101

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

By
Justiniano Felix Palomino Quispe ,
Justiniano Felix Palomino Quispe

Universidad Cesar Vallejo (UCV), Facultad de Ingeniería, Carrera de Ingeniería Civil. Ciudad de Lima, Perú

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Domingo Zapana Diaz ,
Domingo Zapana Diaz

Universidad Cesar Vallejo (UCV), Facultad de Ingeniería, Carrera de Ingeniería Civil. Ciudad de Lima, Perú

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Leopoldo Choque-Flores ,
Leopoldo Choque-Flores

Universidad Cesar Vallejo (UCV), Facultad de Ingeniería, Carrera de Ingeniería Civil. Ciudad de Lima, Perú

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Alisson Lizbeth Castro León ,
Alisson Lizbeth Castro León

Universidad Cesar Vallejo (UCV), Facultad de Ingeniería, Carrera de Ingeniería Civil. Ciudad de Lima, Perú

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Luis Villar Requis Carbajal ,
Luis Villar Requis Carbajal

Universidad Cesar Vallejo (UCV), Facultad de Ingeniería, Carrera de Ingeniería Civil. Ciudad de Lima, Perú

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Edwin Eduardo Pacherres Serquen ,
Edwin Eduardo Pacherres Serquen

Universidad Tecnológica del Perú (UTP), Facultad de Ingeniería, Carrera de Ingeniería Civil. Ciudad de Lima, Perú

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Arturo García-Huamantumba ,
Arturo García-Huamantumba

Universidad Privada Norbert Wiener (UPNW), Facultad de Ingeniería y Negocios, Carrera de Administración y Negocios Internacionales. Ciudad de Lima, Perú

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Elvira García-Huamantumba ,
Elvira García-Huamantumba

Universidad Privada Norbert Wiener (UPNW), Facultad de Ingeniería y Negocios, Carrera de Administración y Negocios Internacionales. Ciudad de Lima, Perú

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Camilo Fermín García-Huamantumba ,
Camilo Fermín García-Huamantumba

Universidad Privada Norbert Wiener (UPNW), Facultad de Ingeniería y Negocios, Carrera de Administración y Negocios Internacionales. Ciudad de Lima, Perú

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Carlos Enrique Guanilo Paredes ,
Carlos Enrique Guanilo Paredes

Universidad Autónoma del Perú (UA), Facultad de Ciencias de Gestión y Comunicaciones, Carrera de Administración y Empresas. Ciudad de Lima, Perú

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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.

Keywords:

Artificial intelligence,
Automation,
Business Processes,
Operating efficiency,
Quantitative Impact,

How to Cite

1.
Palomino Quispe JF, Zapana Diaz D, Choque-Flores L, Castro León AL, Requis Carbajal LV, Pacherres Serquen EE, García-Huamantumba A, García-Huamantumba E, García-Huamantumba CF, Guanilo Paredes CE. Quantitative Evaluation of the Impact of Artificial Intelligence on the Automation of Processes. Data and Metadata [Internet]. 2023 Oct. 15 [cited 2024 Jun. 30];2:101. Available from: https://dm.saludcyt.ar/index.php/dm/article/view/101

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The article is distributed under the Creative Commons Attribution 4.0 License. Unless otherwise stated, associated published material is distributed under the same licence.

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