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https://doi.org/10.37815/rte.v36ne1.1190

<= span lang=3DES>Artículos originales

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El impacto de la Inteligencia Artificial en la Gestión de Proyectos The Impact of Artificial Intelligence on Project Management

José Antonio Carrillo Zenteno¹ ^{https://orcid.org/0000-0002-4159-0882}, Aida Diana Ormaza = Vintimilla2 https://orcid.org= /0009-0006-9876-8887, Julio Jhovany S= antacruz Espinoza1 https://orc= id.org/0000-0001-7543-<= /o:p>

0919

^{1Universidad=
Católica de Cuenca, Cañar, Ecuador}

jacarrilloz@ucacue.edu.ec, jsantacruze@ucacue.edu.ec

^{2Unidad Educativa Luis Cordero, Azogues,
Ecuador}

aida.ormaza@educacion= .gob.ec

Esta o= bra está bajo una licencia internacional Creative Commons Atribución-= NoComercial 4.0

Enviado: 2024/06/30 Aceptado: 2024= /10/03 Publicado: 2024/10/15

Resumen

En la última década, la inteligencia artificial (IA) ha emerg= ido como una tecnología trans= formadora, especialmente en la gestión de proyectos. Este estudio analiza su impacto en América Lati= na, con un enfoque en Ecuador. Se observa un interés significativo en la adopción de IA en Ecuador, influenciado por políticas favorables, condiciones económicas= y avances tecnológicos. La mayoría de los encuestados son docentes, investigadores científicos y directores de departamento, lo que subraya la relevancia de la IA en los ámbitos educativo y científico (Fernández & Fernández, 2019; Hassan, <= span class=3DSpellE>Khairudin & Nasir, 20= 19).

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Las gran= des organizaciones, con más= de 200 empleados, están m= ejor posicionadas para adoptar l= a IA debido a sus mayores recursos financieros y técnicos (Chui, Henke, &a= mp; Miremadi= , 2020). Sin embargo, persisten barrer= as significativas, como limitaciones tecnológicas, restricciones presupuestarias y la falta de apoyo de la dirección, que dificultan su implementación (Smith & Lazarus, 2021).

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A pesar<= span style=3D'letter-spacing:-.75pt'> de estas barreras, la mayoría de los encuestados anticipa un aumento significativo en la adopción de IA en los próximos cinco años, aunque aún existen dudas y desafíos por superar para asegurar una implementación exitosa y sostenida (Jones, Patel, & Smith, 2019). Este análisis

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destaca tanto las oportunidades como los retos que enfrenta la IA en la gestión de proyectos en

Ecuador, subrayando la necesidad de un enfoque integral para maximizar sus beneficios.=

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Palabras clave: Inteligencia Artificial (AI),= Gestión de Proyectos, Beneficios de IA, Eficiencia

operativa, modelos de adopción.

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Abstract

Over the past decade, artificial intelligence (AI) has emerged as a transformative technology, particularly in project management. This study examines its impact in Latin America,= with a focus on Ecuador. = There is significant interest in AI adoption in Ecuador, driven by favorable policies, economic c= onditions, and technological advancements. Most r= espondents are educators, scientific r= esearchers, and department heads, highlighting the relevance = of AI in educational and scientific fields (Fernández & Fernández, 2019; Hassan, Khairudin, & N= asir, 2019).

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Large organizations, with more = than 200 employees, are <= span class=3DSpellE>better positioned to adopt AI due to<= /span> their greater financial and technical resources (Chui, Henke, & Mirema= di, 2020). However, significan= t barriers persist= , such as technological <= /span>limitations, budgetary constraints, and a lack of managerial support, which complicate its implementation (Smith & Lazarus, 2021).

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D= espite these barriers, most respondents anticipate a significant increase in AI adoption over the next five<= span lang=3DES style=3D'letter-spacing:-.55pt'> years, although doubts and challenges remain that must be addressed to ensure successful and sustained implementation (Jones, Patel,= & Smith, 2019). This analysis underscores both the opportunities <= /span>and challenges that AI faces in project<= span style=3D'letter-spacing:-.35pt'> management in Ecuador, emphasizing the need for a comprehensive = approach to maximize its benefits.

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Keywords: Artificial Intelligence (AI), Project Manage= ment, AI Benefits, Operat= ional Efficiency, Adoption Models.

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Introducción

El marco teórico de esta investigación se fundamenta en la exploración y comprensión

= de la inteligencia ar= tificial (IA) y su impacto en la gestión de proyectos.

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La IA se define como la capacidad = de una máquina para imitar funciones cognitivas humanas, como el aprendizaje, la resoluci&oa= cute;n de problemas y la toma de decisiones (Russell & Norvig, 2020). Esta definición abarca una amplia gama de tecnologías, incluyendo el aprendizaje automático (machine learning), el procesamiento del lenguaje natural (NLP) y los sistemas expertos.

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Inteligencia Artificial y su Aplicación en la Gestión de Proyectos

La gestión de proyectos se refiere a la aplicación de conocimientos, habilidades, herramientas y técnicas para cumplir con los requisitos de un proyecto (Project Management Institute, 2017). La introducci&oacu= te;n de la IA en la gestión de proyectos tiene el potencial de transformar significativamente esta disciplina al mejorar la eficiencia, la precisión y la capacidad de toma de decisiones. Según Batselier & Vanhoucke (2020), las técnicas de = IA, como los algoritmos de aprendizaje automático y los sistemas de reco= mendación, pueden ser aplicadas en varias áreas de la gestión de proyect= os, incluyendo la planificación, la gestión de recursos, la gestión de riesgos y el monitoreo del desempeño.

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El aprendizaje automático, una subdiscipl= ina de la IA, permite a las máquinas aprender de los datos y hacer predicciones o tomar decision= es sin ser programadas explícitamente para ello (Goodfellow, Bengio, & Courville, 2016). En la gestión de proyectos,<= span style=3D'letter-spacing:-.75pt'> esto puede traducirse en la capacidad de predecir retrasos, identificar riesgos potenciales y sugerir acciones correctivas basadas en datos históricos y patrones observados.

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Modelos de= Adopción de Tecnología

Para entender cómo las organizaciones adoptan nuevas tecnologías como la IA, es útil revi= sar modelos teóricos sobre la adopción de tecnología. El Modelo de Aceptación de Tecnología (TAM), propuesto por Davis (1989), sugiere que la percepción de utilidad (PU) y la percepción de facili= dad de uso (PEU) son determinantes clave en la adopción de nuevas tecnologías. La percepción de utilidad se refiere a la creencia de que usar una tecnología mejorará el rendimiento laboral, mientras que la percepción de facilidad de uso se refiere a la creencia de que usar la tecnología será libre de esfuerzo.

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Otro modelo relevante es la Teoría Unificada de Aceptación y Uso de Tecnología (UT= AUT) de Venkatesh y otros (2003), que amplía = el TAM al incluir factores adicionales como las expectativas de rendimiento, las expectativas de esfuerzo, la influencia social y las condiciones facilitadoras. Estos modelos<= span style=3D'letter-spacing:-.65pt'> proporcionan un marco para entender los factores que influyen en la adopción de IA en la gestión de proyectos,<= span style=3D'letter-spacing:-.25pt'> incluyendo las barreras tecnológicas, la disponibili= dad de recursos y el apoyo directivo.

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Beneficios de la IA en la Gestión de Proyectos

La implementación de IA en la gestión de proyectos ofrece numerosos beneficios. Entre los más destacados se encuentran la mejora en la eficiencia operativa, la reducción de costos, y el aumento en la precisión de la toma de decisiones. Hassan y otros (2019) señalan que la IA puede automatizar tareas repetiti= vas, lo que permite a los gestores de proyectos concentrarse en actividades de m= ayor valor añadido. Adem&aac= ute;s, los sistemas de IA pueden anal= izar grandes volúmenes de datos para proporcionar in= sights que de otro modo pasarían desapercibidos, mejorando así la capacidad de tomar decisiones informadas.

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El uso de algoritmos de IA también puede mejorar la gestión de recursos al optimizar la asignación y utilización de recursos en función de datos en tiempo real y predicciones basadas en tendencias históricas (Batselier & Vanhoucke, 2020). Asimismo, la IA puede facilitar la gestión del riesgo mediante<= span style=3D'letter-spacing:-.7pt'> la identificación temprana de problemas potenciales y la recomendación de acciones correctivas antes de que= estos se conviertan en problemas significativos.

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Barreras pa= ra la Adopción de IA

A pesar de los numerosos beneficio= s, la adopción de IA en la gestión de proyectos enfrenta varias barreras. Las limitaciones tecnológicas, como la falta de infraestructura adecuada y la complejidad de integrar la IA con sistemas existentes, son desafíos significativos (Smith & Lazarus, 2021). Las restricciones presupuestarias también juegan un papel crucial, ya que la implementación de soluciones de IA puede ser costosa y requiere una inversión considerable en hardware, software y capacitación d= el personal.

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Además, la normativa y los aspectos legales pueden dificultar la adopción de IA, especialmente = en sectores altamente regulados (Chui, Henke, &= ; Miremadi, 2020). La falta de apoyo de la dirección es otra barrera importante, ya que la implementación exitosa de IA requiere un compromiso y una visión claros por parte de los líd= eres organizacionales. Estos desafíos múltiples y entrelazados= sugieren la necesidad de un enfoque integral para superar las barreras y facilita= r la adopción efectiva de IA en la gestión de proyectos.

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El marco referencial de esta investigación se basa en una revisión exhaustiva de la litera= tura existente sobre la inteligencia artificial (IA= ) y su aplicación en la gestión de proyectos. = Este marco proporciona el contexto necesario para comprender cómo las tecnologías de IA están siendo adoptadas y utilizadas en diferentes sectores y cómo esta investigación puede contribuir a ese conocimiento.

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Estudios Pr= evios sobre la IA en la Gestión de Proyectos

La<= /span> implementación de IA en la gestió= ;n de proyectos ha sido <= span lang=3DES style=3D'letter-spacing:-.2pt'>objeto de numerosos estudios qu= e destacan sus beneficios y desafíos. Por ejemplo, Fernández & Fern&aac= ute;ndez (2019) analizaron cómo la IA está cambiando las reglas del ju= ego en la gestión ágil de proyectos, permitiendo una mayor flexibilidad y capacidad de respuesta a los cambios en el entorno del proyecto. Este estudio es= relevante para nuestra investigación, ya que ofrece un marco para entender cómo la IA puede mejorar la adaptabilidad y eficiencia en la gestión de proyectos.

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Hassan y otros (2019) discuten el impacto de la IA en la gestión de proyectos, señalando que su implementación puede llevar a una mejora significativa en la eficien= cia operativa y la precisión en la toma de decisiones. Este estudio es crucial para nuestra investigación, ya que proporciona evidencia emp= írica de los beneficios de la IA, lo cual nos ayudará a validar los result= ados obtenidos en nuestro estudio.

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De acuerdo con Project Management = Institute Sweden (2024), = la llegada de la IA ha provocado que numerosos países y organizaciones intensifiquen las inversiones en tecnología emergente, así co= mo la necesidad de líderes en gestión de proyectos con habilidad= es en innovación y toma de decisiones basadas en datos junto con una sólida comprensión del entorno empresarial. Aunque la IA facilitará diversas tareas, no sustituirá de inmediato el trabajo humano, sino que reorientará la gestión de proyectos hacia un enfoque más estratégico y basado en datos.

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En Norteamérica, una mayoría significativa de profesionales (76%) considera que la IA transformará la gestión de proyectos en los próximos años, lo<= span style=3D'letter-spacing:.95pt'> que indica que perciben a la IA como esencial para mantenerse en el mercado laboral. En Europa, el surgimiento de la inteligencia<= span style=3D'letter-spacing:-.4pt'> artificial ha aumentado el número de profesionales (71%) en la gestión de proyectos que consideran que adoptar una mentalidad innovadora y desarrollar la capacidad de tomar decisiones basadas en datos serán habilidades fundamentales. Así mism= o, en Asia el 79% de expertos consideran = que la IA beneficiará y transformará la gestión de proyectos = al incluir habilidades específicas y herramientas tecnológicas que mejoren la eficiencia en la ejecución de proyectos; así mismo, África ve en la IA una herramienta potencial para transformar la gestión de proyectos; sin embargo, la región enfrenta desafíos significativos como la brecha de habilidades digitales y la necesidad de mejorar la infraestructura.

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En América Latina, el 87% de los profesionales está co= nvencido de que la inteligencia artificial (IA) tendrá un impacto positivo en la gestión de proyectos. Aunque la región se = encuentra en las primeras fases de adopción de esta tecnología, hay un notable interés en adquirir y desarrollar competencias relacionadas con la IA. Tanto las organizaciones públicas como privadas se enfrentan al desafío= de establecer las condiciones adecuadas para facilitar esta formación, = un paso esencial para reducir la brecha con otras regiones más avanzada= s en la implementación de IA.

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En Oceanía, el 86% de los profesionales considera que la IA transformará la gestión de proyectos. La región est&a= acute; comenzando a implementarla en este ámbito, y se anticipa que los profesionales deberán adaptarse rápidamente a este nuevo paradigma. Las empresas necesitarán

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preparar y capacitar adecuadamente = a sus equipos para que esta transición sea efectiva, con un enfoque particular en la gestión del cambio y en la retención= del talento especializado en IA.

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En el caso del Ecuador, la adopción de la IA en la gestión de proyectos es aún incipiente debido a los desafí= ;os en cuanto a infraestructura y formación, por lo que se están haciendo esfuerzos por seguir desarrollando las bases digitales y fomentar = la educación en tecnología, de modo que se integre la IA en proyectos de gran envergadura; ej= emplo de ello es la mejora de la planificación y ejecución de proyectos en zonas rurales, donde la logística y la gestión de recursos son especialmente complejas (Salinas & Fernández, 2020).

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A medida que Ecuador continú= ;a desarrollando su infraestructura digital y fomenta la educación en tecnología, es probable que la IA juegue un papel cada vez más importante en la gestión= de proyectos a nivel nacional. Este desarrollo es crucial para que el pa&iacut= e;s pueda aprovechar las oportunidades que ofrece la inteligencia artificial y así mejorar la competitividad de sus proyectos en un contexto global (UNESCO, 2021).

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Factores q= ue Influencian en la Adopción de IA

La<= /span> adopción de IA no ocurre en el vac&i= acute;o; está influenciada por varios factores contextuales. Chui y otros (2020) discuten cómo las políticas económicas y tecnológicas de un país pueden afectar la adopción de IA. En América Latina, y específicamente= en Ecuador, estos factores son cruciales para entender por qué algunas organizaciones adoptan<= span style=3D'letter-spacing:-.45pt'> IA más rápidamente que otras. Este estudio proporciona un marco para analizar cómo las condiciones loca= les pueden influir en la adopción de IA en la gestión de proyectos.

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Venkatesh y otros (2003), en su Teoría Unificada de Aceptación y Uso de Tecnología (UTAUT), identifican factores como las expectativas de rendimiento, las expectativas de esfuerzo, la influencia social y las condiciones facilitadoras como determinantes clave en la adopción de tecnología. Este modelo es útil para nuestra investigación, ya que nos permite estructurar nuestro análisis sobre los factores que influyen en la adopción de IA en la gestión de proyectos.

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Barreras pa= ra la Adopción de IA

Smi= th & = Lazarus (2021) exploran las barreras tecnológicas, presupuestarias y normativas que dificultan la adopción = de tecnologías innovadoras. Estos hallazgos son particularmente relevan= tes para nuestra investigación, ya que proporcionan un marco para identificar y analizar las barreras específicas que enfrentan las organizaciones en América Latina al adoptar IA= . La comprensión de estas barreras nos permitirá ofrecer recomendaciones pr&aacut= e;cticas para superarlas.

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Beneficios de la IA en la Gestión de Proyectos

Batselier & Vanhoucke (2020) revisan cómo las técnicas de IA pueden mejorar la gestión de recursos del proyecto, la optimización de cronogramas y el análisis predictivo. Estos beneficios son centrales para nuestra investigación, ya que nos permiten enfocarnos en cómo= la IA puede transformar aspectos específicos de la gestión de proyectos. Este estudio proporcionará una base = sólida para comparar los beneficios observados en nuestra investigación con los hallazgos de estudios previos.

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Contribución de la Investigación

Este marco referencial no solo contextualiza nuestra investigación dentro del cuerpo existente de literatura, sino que también identifica las lagunas de conocimiento = que nuestro estudio puede llenar. Por ejemplo, aunque hay estudios significativos sobre los beneficios de la IA,

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hay meno= s investigaciones que aborden las barreras específicas en el contexto latinoamericano. Nuestra investigación contribuirá a llenar esta laguna al proporcionar datos empíricos sobre las barrera= s y facilitadores de la adopción de IA en la gestión de proyectos en Ecuador y otros países de la región.

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Además, al aplicar modelos teóricos como TAM y UTAUT, nuestra investigación no solo identificará los factores que influyen en la adopción de IA, sino que también proporcionará un marco para que otras investigaciones futuras exploren estos factores en diferentes contextos. Esto hará que nues= tros hallazgos sean más generalizables y útiles para una amplia ga= ma de interesados en la gestión de proyectos y la adopción de IA= .

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Materiales y Métodos

La<= /span> metodología de esta investigación se diseñ&= oacute; para proporcionar un análisis comprensivo y detallado sobre el impacto de la inteligencia artificial (IA) en la gestión de proyectos en América Latina, con un énfasis particular en Ecuador. El enfo= que metodológico adoptado combina métodos cuantitativos y cualita= tivos, permitiendo así una comprensión holística de los beneficios, barreras y factores que influyen en la adopción de IA en la gestión de proyectos.

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Diseño de I= nvestigación

El diseño de investigación es de tipo exploratorio y descriptivo. Según Creswell (2014),= un diseño exploratorio es adecuado para estudios donde hay poca información previa disponible, y un diseño descriptivo permite detallar las características de un fenómeno específico. Este enfoqu= e es relevante para nuestra investigación, ya que busca explorar y describir la adopci&oacu= te;n de IA en un contexto regional específico.

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Instrumentos de Recolección de Datos

Se empleó una encuesta estructurada, que incluy&oac= ute; preguntas de opció= ;n múltiple y preguntas con esc= ala Likert, como principal instrumento para la recolección de datos cuantitativos. La encuesta fue diseñada para capturar informaci&oacu= te;n detallada sobre:

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1. Perfil de los encuestados: País de residencia, cargo en la organización, sector industrial, y tamaño d= e la organización.

2.&n= bsp; Uso a= ctual de IA: Preguntas sobre si el departamento o= equipo utiliza actualmente

IA en la gestión de proyectos.

3. Beneficios observados de la IA: Identificación de los beneficios específicos que los

encuestados han observado desde la implementación de IA.

4. Barreras para l= a adopción d= e IA: I= dentificación de l= as principales b= arreras percibidas

para la adopción futura de IA.<= /span>

5. Perspectivas futuras sobre la IA: Selección de diversas opciones sobre el futuro = de la IA en la gestión de proyectos en los próximos cinco años.

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Procedimiento de Recolección de Datos

Se distribuyeron electrónicamente 900 encuestas a personal calificado en la gestión de proyectos en América Latina, utilizando plataformas de correo electrónico (207 encuestas)= y redes sociales profesionales (693 encuestas).= De este total, se recibieron 693 respuestas, lo que representa un 77% del total enviado. La selección de la muestra fue intencional, enfocándose

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en indiv= iduos que laboran en sectores donde la IA tiene un potencial significativo = de aplicación, tales como la educación, la investigación científica, la salud y la tecnología.

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Análisis <= /span>de Datos

Análi= sis Cuantitativo

Los datos cuantitativos recopilado= s se analizaron utilizando técnicas estadísticas descriptivas y análisis inferencial. Las estadí= ;sticas descriptivas (medias, mediana= s, frecuencias y porcentajes) s= e utilizaron para resumir las respuestas de la encuesta y proporcionar una visión general de las tendencias observadas. Según Pal= lant (2020), este enfoque permite identificar patrones y tendencias clave en los datos.

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Se utilizó el software estadístico SPSS (Statistical Package = for the Soci= al Sciences) para realizar el análisis. El análisis inferencial, incluyendo pruebas de chi-cuadrado y análisis de varianza (ANOVA), se empleó para identificar diferencias significativas entre grupos y explorar relaciones entre variables.

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Validez y Confiabilidad

Para asegurar la validez y confiab= ilidad de la investigación, se llevaron a cabo las siguientes acciones:

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• = Validez de contenido:<= span style=3D'letter-spacing:-.75pt'> La encuesta fue revisada por expertos en gestión de proyectos y tecnología de IA para asegurar que las preguntas fueran relevan= tes y comprensivas (Creswell, 2014).

• = Prueba piloto: Se realizó una prueba piloto con un pequeño grupo de profesionales de la gestión de proyectos para identificar y corregir cualquier problema potencial con la encuesta antes de su distribución a gran escala.

•= ; = Consistencia interna: Se utilizó el coeficiente Alfa de Cronbach para evaluar la

consistencia interna de las escalas utiliza= das en la encuesta (Pallant, 2020).

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Limitaciones de la Investigación

Es importante reconocer ciertas limitaciones en esta investigación. La muestra intencional, aunque adecuada para el enfoque exploratorio, puede limitar la generalizació= ;n de los resultados a toda la población de profesionales de la gestión de proyectos en América Latina. Además, la recolección de datos a través de encuestas electrónicas puede introducir sesgos de respuesta debido a= la accesibilidad y disposición de los encuestados a participar.<= /p>

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Contribución de la Metodología a la Investigación

Est= a metodología colaborará en la investigación al proporcionar = una visió= n integral de la adopción de IA en la gestión de proyectos. La combinación de datos cuantitativos y cualitativos permitirá una comprensión más profunda de los benefici= os y barreras percibidas. Adem&aacu= te;s, el análisis estadístico ayudará a identificar tendenci= as significativas y relaciones entre variables, lo que es esencial para formul= ar recomendaciones prácticas y basadas en evidencia.

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Al<= /span> emplear un enfoque metodológico robusto y sistemático, esta investigaci&= oacute;n contribuirá a llenar las lagunas de conocimiento existentes y proporcionará información valiosa p= ara los profesionales de la gestión de proyectos que buscan implementar = IA en sus organizaciones.

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Resultados<= /span>

= Resultados = y Discusión

Tabla 1

= Nacionalidad de los Participantes

= PAÍS	= NÚMERO
= Argentina=	7
= Cuba=	21
= Ecuador=	392
El Salvador	14
= Guatemala=	7
= Honduras=	14
= México=	91
= Perú=	98
República Dominicana	21
= Venezuela=	28

Como se observa en la Tabla 1, la mayoría de los encuestados provienen de Ecuador, seguido por otros países de la región. Esto sugiere que hay un mayor inter&eacu= te;s o disposición a responder encuestas sobre la IA en la gestión de proyectos en Ecuador. Esta distribución puede influir en la interpretaci&oa= cute;n de los resultados, considerando las políticas, economías y avances tecnológicos de cada región.

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Figura 1

Cargo que Desempeñan de los Participantes

En<= /span> la Figura 1 se observa que la mayoría de los encuestados son docentes (448), seguidos por investigadores científicos (91) y directo= res de departamento (63). Los administradores constituyen una minor&iacut= e;a con 7 respuestas. Este dato destaca la relevancia de la IA en el ámbito educativo y la investigación científica, indicando que estos roles están más involucrados o interesados en la adopci&oacut= e;n de IA en la gestión de proyectos.

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Figura 2

Sector en el que Laboran los Participantes

En la Figura 2 se aprecia que los sectores educativos y de investigación científica son los más representados, seguidos por la salud y la tecnología; lo que indica que dichos ámbitos están más interesados en= la implementación y en el impacto de la IA en la gestión de proyectos.

Figura 3

Tamaño de la organización=

De acuerdo con la Figura 3, las organizaciones grandes (más de 200 empleados) son las m&aacu= te;s representadas, lo que indica que éstas tienen más recursos para adoptar la IA en la gestión de proyectos.<= /p>

Figura 4

Uso = de la Inteligencia Artificial en la Gestión de Proyectos

En la Figura 4 se aprecia una dife= rencia notable entre los departamentos que utilizan IA y aquellos que no lo hacen.= Una proporción mayor de departamentos no está empleando actualmente IA en sus proyectos, lo cual sugiere que, a pesar del creciente interés en la adopción de esta tecnología, aún hay muchas organizaciones que = no han comenzado a implementarla.

= ;

Tabla 2

Beneficios = de la Implementación de la IA en la Gestión de Proyectos

¿Qué b= eneficios ha observado desde la implementaci&oacut= e;n de la IA en la gestión de proyectos?
= BENEFICIOS	NÚMERO DE RESPUESTAS
Avances en la investigación.	126
Mejora de la eficiencia operativa.	49
Mejora de la eficiencia operativa, avances en la investigación.	49
Mejora de la eficiencia operativa, mejora de la satisfacción del cliente/ = estudiante.=	14
Mejora de la eficiencia operativa, mejora de la satisfacción del cliente/ estudiante, avances en la investigación= .	28
Mejora de la eficiencia operativa, mejora de la satisfacción del cliente/ estudiante, reducción del tiempo del proy= ecto.	7
Mejora de la eficiencia operativa, mejora en la toma de decisiones.	14
Mejora de la eficiencia operativa, mejora en la toma de decisiones, avances en la investigación.	35
Mejora de la eficiencia operativa, mejora de la toma de decisiones, mejora de la satisfacci&oacu= te;n del cliente/estudiante.<= /o:p>	7
Mejora de la eficiencia operativa, reducción de costes.	7
Mejora de la eficiencia operativa, reducción de costes, avances en la = investigación.=	7
Mejora de la eficiencia operativa, reducción de costes, mejora de la satisfacción del cliente/estudiante, av= ances en la investigación.	7
Mejora de la eficiencia operativa, reducción de costes, mejora en la toma de decisiones, a= vances en la investigación.	7
Mejora de la eficiencia operativa, reducci&oacut= e;n de costos, mejora en la toma de decisiones, mejora de la satisfacci&oacut= e;n del cliente/estudiante, avances en la investigación.	7=
Mejora de la satisfacción del cliente/estudiante.	14
Mejora = de la = satisfacción del = cliente/estudiante, avances = en la = investigación.=	7
Mejora en la toma de decisione= s, avances en la investigaci&oacut= e;n.	28
Mejora en la toma de decisiones, mejora en la satisfacción <= /span>del cliente/ = alumno.=	7
Mejora en la toma de decisiones, mejora en la satisfacción <= /span>del cliente/ estudiante, avances en la investigación= .	21
No implementado en la instit= ución.	7
Reducción de costes, avances en la investigación.	7

En la Tabla 2 se observa que los principales beneficios tras= la implementación de la IA en la gestión de proyectos incluyen avances significativos en la investigación (126 respuestas) y mejoras en la eficiencia operativa (49 respuestas). La intersecció= n de estos dos beneficios es especia= lmente notable, con 49 respuestas adicionales que subrayan mejoras tanto en la eficiencia operativa como en los avances en investigación. Además, 35 respuestas señalaron una combinación de mejoras en la eficiencia oper= ativa, la toma de decisiones y los avances en investigación, lo que indica el valor integral de la IA en múltiples aspectos cr&iacu= te;ticos de la gestión de proyectos.<= /p>

&nb= sp;

Otros beneficios reportados incluy= en mejoras en la toma de decisiones, reducción de costos y un aumento en la satisfacción de clientes o estudiantes, con frecuencias que oscilan entre 7 y 28 respuestas. Estos resultado= s sugieren que la IA no solo optimiza las operaciones diarias y aumenta la productividad,= sino que también impulsa la innovación y fortalece la capacidad de investigación, beneficiando a diversas áreas dentro de la organización.

&nbs= p;

Tabla 3

Barreras <= /span>en la Inclusión de la IA en la Gestión de Proyectos

= ¿Qué barreras considera que son las más significativas para la adopción futura de IA en la gestión de proyectos?
= BARRERAS	NÚMERO DE RESPUESTAS
Falta de apoyo gerencial.	28
Falta de apoyo gerencial, aspectos reg= ulatorios y legales.	14
= Limitaciones tecnológicas.	98
Limitaciones tecnológicas, falta de apoyo gerencial.	42
Limitaciones tecnológicas, falta de apoyo gerencial, ética.=	7
Limitaciones tecnológicas, aspectos regulatorios y legales.	56
Limitaciones tecnológicas, restricciones presupuestarias.	56
<= span lang=3DES style=3D'font-size:9.0pt;mso-bidi-font-size:11.0pt;line-height:= 103%'>Limitaciones tecnológicas, restricciones presupuestarias, falta de apoyo gerencial.<= /p>	21
<= span lang=3DES style=3D'font-size:9.0pt;mso-bidi-font-size:11.0pt;line-height:= 103%'>Limitaciones tecnológicas, restricciones presupuestarias, falta de apoyo gerencial, aspectos regulatorios y legales.	28
Limitaciones tecnológicas, restricciones presupuestarias, aspectos regulatorios y legales.	14
Aspectos regulatorios y legales.	70
= Restricciones presupuestarias.	49
Restricciones presupuestarias, falta de apoyo gerencial.	7
Restricciones presupuestarias, falta de apoyo gerencial, aspectos regulatorios y l= egales.	21
Restricciones presupuestarias, aspectos regu= latorios y legales.	35
Todo lo anterior.	7

En la Tabla 3 se puede observar que las principales barreras para la adopción de IA en la gestión de proyectos se identificaron en las limitaciones tecnológicas, mencionadas en 98 ocasiones, seguidas por las restricciones presupuestarias, con 49 menciones, y los aspectos normativos y legales, con 70 menciones. Además, la falta de apoyo de la dirección emergió como una barrera significativa, con 28 menciones.

&nbs= p;

Estas barreras suelen presentarse = de manera combinada, lo que refleja la complejidad del desafío: 56 respuestas mencionaron conjuntamente limitaciones tecnológicas y restricciones presupuestarias, mientras que otras 56 combinaron limitaciones tecnológicas con aspectos normativos y legales. Asimismo, 42 respues= tas señalaron la coexistencia de limitaciones tecnológicas y la f= alta de apoyo directivo, y 28 respuestas identificaron la conjunción de limitaciones tecnológicas, restricciones presupuestarias y falta de apoyo de la dirección.

= ;

Estas múltiples barrer= as subrayan que las organizaciones enfrentan desafíos complejos e interrelacionados que deben abordarse de manera integral para facilitar la adopción efectiva de la IA en la gestión de proyectos.

&nb= sp;

Figura 5

Futuro de la IA en la Gestión de Proyectos

En la Figura 5, se aprecia que la mayoría de los encuestados anticipa un aumento significativo en la adopción de IA en la gestión de proyectos durante los próximos cinco años, con aproximadamente 350 menciones. Esto refleja un optimismo generalizado sobre el potencial de la IA para transformar la gestió= n de proyectos en el futuro cercano. No obstante, un número considerable de encuestados, alrededor de 100, expresó incertidumbre sobre el futuro de la adopción de IA, lo que señala que aún persisten dudas y desafíos por resolver.

&nbs= p;

Adicionalmente, un número n= otable de respuestas, cerca de 75, prevé un incremento moderado en la adopción, mientras que un grupo más pequeño (aproximadamente 25 menciones) espera una estabilización sin cambios significativos. Solo un pequeño porcentaje, me= nos de 10 menciones, anticipa una reducción en la adopción de IA. Estos resultados sugieren una tendencia positiva hacia la adopción de IA, aunque acompañada de cierta cautela y la necesidad de abordar las preocupaciones subyacentes para garantizar una implementaci&oacu= te;n exitosa y sostenida.

&nbs= p;

Discusión

La elevada participación de encuestados provenientes de Ecuador sugiere un notable interés y disposición hacia la adopción de inteligencia<= span style=3D'letter-spacing:-.35pt'> artificial (IA) en la gestión de proyectos dentro del país. Este hallazgo es significativo, ya que refleja la influencia de políticas locales favorables, condiciones económicas adecuadas y avances tecnológicos en la aceptación y uso de la IA. Ecuador podría estar posicion&a= acute;ndose como un líder regional en términos de adopció= ;n de IA, sirviendo como modelo para otras naciones con menores tasas de implementación.

&nbs= p;

El predominio de docentes (448) en= tre los encuestados, seguido por investigadores científicos (91) y directores de departamento (63), subraya la importancia de la IA en los sectores educativo y de investigación científica. Esta tendencia<= span style=3D'letter-spacing:-.5pt'> sugiere que estos ámbitos están particularmente comprometidos con la integración de IA, posiblemente debido a la necesidad de gestionar grandes volúmenes de datos y mejorar la eficiencia en procesos académicos y de investigación. En contraste, la baja participación de administradores (7) podría indicar que, aunque la IA es reconocida como una herramienta valiosa, su adopción aún no se ha generalizado en los roles administrativos.

&nb= sp;

La fuerte representación del sector educativo y de investigación científica, seguido por la salud y la tecnología, reafirma la percepción d= e la IA como una herramienta esenci= al para mejorar la eficiencia y efectividad en estos campos. La adopción de IA en estos sectores responde a la creciente demanda de soluciones innovad= oras para problemas complejos que requieren procesamiento de datos en tiempo rea= l y decisiones informadas.

&nb= sp;

Las grandes organizaciones, con más de 200 empleados, dominan la muestra, lo que podría indicar que disponen de mayores recursos financieros y técnicos para implement= ar IA en sus operaciones. Este hecho podría deberse a la disponibilidad de presupuestos más amplios, infraestructuras robustas y personal especializado, facilitan= do así la integraci&o= acute;n de tecnologías avanzadas como la IA.

&nb= sp;

La significativa diferencia entre los departamentos que utilizan IA (190) y= los que no

(489) rev= ela que, a pesar del creciente interés, muchas organizaciones aún se encuentran en las primeras etapas de implementación. Esta situación podría atribuirse a barreras como limitaciones tecnológicas, restricciones presupuestarias= y la necesidad de mayor apoyo directivo y normativo.

&nbs= p;

Entre los principales beneficios d= e la implementación de IA se destacan los avances en investigaci&o= acute;n (126 menciones) y las mejoras en la eficiencia opera= tiva (49 menciones). La combinación de estos beneficios es particularmente significativa,= ya que 49 menciones adicionales destacaron mejoras tanto en la eficiencia operativa como en la investigación, y 35 menciones indicaron una combinaci&= oacute;n de mejora en la eficiencia operativa, tom= a de decisiones y avances en investigaci= ón. Esto sugiere que la IA está generando un valor integral en diversos aspectos críticos de la gestión de proyectos. Otros beneficios, como mejoras en la toma de decisiones, reducción de costos y aumento en la satisfacción del cliente o estudiantes, aunque menos frecuentes, también son importantes, evidenciando que la IA = no solo optimiza operaciones diarias,= sino que también impulsa la innovación y la capacidad de investigación.

&nb= sp;

Las principales barreras para la adopción de IA incluyen limitaciones tecnológicas (98 menciones), restricciones presupuestarias (49 menciones) y as= pectos normativos y legales (70 menci= ones). La falta de apoyo de la dirección también se destaca como una barrera significativa (28 menciones). La combinación de estas barrera= s, como las limitaciones tecnológicas con restricciones presupuestarias= (56 menciones) y con aspectos normativos y legales (56 menciones), indica una preocupación multifacética que requiere un enfoque integral p= ara ser superada. Estas barreras múltiples revelan= que las organizaciones enfrentan desaf&= iacute;os complejos e interrelacionados que de= ben abordarse para facilitar la adopción efectiva de IA.

&nbs= p;

Fin= almente, la mayoría de los encuestados prevé un aumento significativo en la adopció= ;n de IA en los próximos cinco añ= ;os (350 menciones), lo que sugiere un optimismo generalizado sobre su potencial transformador. Sin embargo, aproximadamente 100 encuestados<= span style=3D'letter-spacing:-.5pt'> expresaron incertidumbre, <= span style=3D'letter-spacing:-.2pt'>señalando la persistencia de dudas y desafíos por superar. Un número considerable de respuestas (75) anticipa un incremento moderado en la adopción, mientras que solo un = pequeño porcentaje espera una reducción. Estos resultados reflejan una tendencia positiva hacia la adopción de IA, aunque es necesario abordar las preocupaciones subyacentes para asegurar una implementación exitosa y sostenida.

&nb= sp;

Conclusiones

Ecuador muestra un mayor interés o disposición hacia la IA en la gestión de proyectos, reflejado en la alta participación de encuestados de este país. Este inter&eac= ute;s puede ser impulsado por políticas favorables, condiciones económicas adecuadas y avances tecnológicos en la región.

&nb= sp;

La<= /span> alta <= span lang=3DES style=3D'letter-spacing:-.1pt'>participación de docentes, investigadores científicos y directores de departamento indica que la IA es particularme= nte relevante en los ámbitos educativo y científico. Estos sectores están <= /span>más avanzados en la adopción de IA debido a la necesidad de manejar grandes volúmenes= de datos y mejorar la eficiencia en sus procesos.

&nbs= p;

El sector educativo y de investigación científica son los más representados, seguidos por salud y tecnología. Esto sugiere que estos sectores reconocen la importancia de la IA para mejorar sus operaciones y resultados.

&nb= sp;

Las organizaciones grandes son las más representadas, lo que sugiere que estas tienen más recursos para implementar IA. La disponibilidad de presupuesto= , infraestructuras robustas y per= sonal especializado son factores que facilitan la integración de IA en organizaciones más grandes.

&nb= sp;

La adopción de IA aún está en una fase de transición, con una mayor proporció= ;n de departamentos que no la utilizan. Esto indica que, aunque hay interés, muchos departamentos aún no han comenzado a implemen= tar IA, probablemente debido a diversas barreras.

&nbs= p;

Los= principales <= /span>beneficios de la implementación de IA incluyen avances en investigación y mejoras en la eficiencia ope= rativa. Además, la IA también contribuye sig= nificativamente a la mejora en la toma de decisiones, reducción de costos y aumento en la satisfacción del cliente/ estudiante. Esto demuestra el valor integral de la IA en diversos aspectos crítico= s de la gestión de proyectos.<= /p>

&nb= sp;

Las= principales <= /span>barreras incluyen limitaciones = tecnológicas, restricciones presupuestarias y normativas y aspectos legales. La falta = de apoyo= de la direcci&oacu= te;n también es significativ= a. Estas barreras múltiples y entrelazadas indican que las organizaciones e= nfrentan desafíos complejos que requieren un enfoque integral para superarse.<= /span>

&nbs= p;

La mayoría de los encuestad= os anticipan un aumento significativo en la adopción de IA en los próximos cinco a&ntil= de;os, lo que refleja un optimismo generalizado. Sin embargo, existe incertidumbre= y la necesidad de abordar preocupaciones para asegurar una implementaci&oacut= e;n exitosa y sostenida.

&nbs= p;

En resumen, aunque hay un claro interés y optimismo sobre el uso de IA en la gestión de proyectos, existen barreras significativas= que deben abordarse. La adopci&oacu= te;n es más avanzada en sectores educativos y científicos y en organizaciones grandes que tienen más recurs= os. Los beneficios observados son diversos, lo que subraya el potencial transformador de la IA, pero superar las barreras tecnológicas, pres= upuestarias y normativas es crucial para su adopción generalizada.

&nb= sp;

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