[{"data":1,"prerenderedAt":1018},["ShallowReactive",2],{"/es/the-source/ai/4-steps-for-measuring-the-impact-of-ai":3,"footer-es":35,"the-source-banner-es":343,"the-source-navigation-es":355,"the-source-newsletter-es":382,"footer-source-/es/the-source/ai/4-steps-for-measuring-the-impact-of-ai/":393,"article-site-categories-es":398,"4-steps-for-measuring-the-impact-of-ai-article-hero-category-es":400,"4-steps-for-measuring-the-impact-of-ai-the-source-source-cta-es":423,"4-steps-for-measuring-the-impact-of-ai-category-es":438,"4-steps-for-measuring-the-impact-of-ai-the-source-resources-es":451,"4-steps-for-measuring-the-impact-of-ai-article-hero-author-es":469},{"_path":4,"_dir":5,"_draft":6,"_partial":6,"_locale":7,"config":8,"seo":13,"content":17,"type":26,"category":5,"slug":27,"_id":28,"_type":29,"title":30,"_source":31,"_file":32,"_stem":33,"_extension":34},"/es/the-source/ai/4-steps-for-measuring-the-impact-of-ai","ai",false,"",{"layout":9,"template":10,"author":11,"sourceCTA":12},"the-source","TheSourceArticle","taylor-mccaslin","source-lp-how-to-get-started-using-ai-in-software-development",{"title":14,"description":15,"ogImage":16},"4 pasos para medir el impacto de la IA","Para juzgar el éxito de las iniciativas de IA, es crucial tener el marco de medición adecuado. Consulte cuatro pasos para enfocarse en las métricas correctas.","https://res.cloudinary.com/about-gitlab-com/image/upload/v1751463600/yv1v4ywk7hbobfvlxwhf.png",{"title":14,"date":18,"description":19,"timeToRead":20,"heroImage":16,"keyTakeaways":21,"articleBody":25},"2024-10-29","Para juzgar el éxito de las iniciativas de IA, es fundamental contar con el marco de medición adecuado. Aquí encontrará cuatro pasos que le ayudarán a enfocarse en las métricas correctas.","Lectura de 5 min",[22,23,24],"La eficacia de la IA para desarrollar software no debe medirse solo mediante métricas de productividad, como la generación de código, sino que también debe considerar su impacto en la calidad, el mantenimiento, las pruebas y la seguridad del código.","Una integración exitosa de la IA requiere un enfoque holístico que combine datos cuantitativos de todo el ciclo de desarrollo de software con información cualitativa de los desarrolladores sobre el impacto real de la IA en su trabajo y estrategias.","Con el enfoque correcto, la IA puede potenciar la colaboración, mejorar la calidad del código y respaldar los objetivos comerciales, sin comprometer la calidad ni la seguridad del software","La IA se ha convertido rápidamente en una pieza central de la pila tecnológica de las organizaciones. Las herramientas de productividad con tecnología de IA prometen mejorar la eficiencia al automatizar tareas de programación repetitivas. Sin embargo, muchas organizaciones tienen dificultades para medir el impacto de sus iniciativas de IA y evalúan constantemente las métricas para asegurarse de que estén alineadas con los resultados comerciales deseados.\n\n Históricamente, medir la productividad de los desarrolladores ha sido un desafío, tanto con herramientas con tecnología de IA como sin ellas. [Según una investigación realizada por GitLab](https://about.gitlab.com/developer-survey/), menos de la mitad de los directores de experiencia (CXO) están satisfechos con el enfoque actual de sus organizaciones para medir la productividad de los desarrolladores, y el 36 % consideran que sus mediciones de productividad actuales son deficientes.\n\nLa evaluación de la productividad de la programación impulsada por IA requiere un enfoque más matizado que las métricas tradicionales, como las líneas de código, las confirmaciones de código o la finalización de tareas. Esto implica un cambio de enfoque hacia los resultados comerciales concretos que equilibren la velocidad de desarrollo, la calidad del software y la seguridad.\n\nA continuación, se presentan algunas medidas que las organizaciones pueden tomar hoy para garantizar que puedan medir el impacto total de la IA en sus procesos de desarrollo de software.\n\n## 1. Establezca objetivos claros para la implementación de la IA\nAl adoptar la IA en el desarrollo de software, las organizaciones deben tener objetivos y métricas claros para medir el éxito. Esto incluye tanto objetivos a corto como a largo plazo que estén alineados con la estrategia empresarial general. Por ejemplo, un objetivo a corto plazo podría ser reducir el tiempo de revisión del código en un 30 % utilizando herramientas con tecnología de IA, mientras que un objetivo a largo plazo podría ser mejorar las valoraciones de satisfacción del cliente mediante ciclos de lanzamiento más rápidos y un código de mayor calidad.\n\nAdemás, los líderes de la organización deben involucrar a los desarrolladores para definir estos objetivos y métricas. Los desarrolladores tienen experiencia de primera mano con respecto al impacto de la IA en su trabajo y pueden aportar información valiosa sobre cómo ha mejorado o dificultado la productividad. [Según un estudio de GitLab](https://about.gitlab.com/developer-survey/), el 63 % de los desarrolladores espera que la IA cambie significativamente su puesto en los próximos cinco años, mientras que el 56 % considera que la incorporación de la IA en el ciclo de vida del desarrollo de software conlleva riesgos. Al consultar a los desarrolladores sobre las oportunidades que identifican para que la IA les asista, así como sus inquietudes al respecto, las organizaciones pueden definir métricas de éxito más significativas y relevantes que reflejen el impacto real de la IA en los equipos de desarrollo de software.\n\nAsimismo, es importante que las organizaciones revisen y reevalúen estos objetivos de manera regular a medida que continúan integrando la IA en sus procesos. La tecnología evoluciona rápidamente, al igual que las necesidades y prioridades comerciales. La definición de objetivos claros permite a los equipos dar seguimiento al progreso y realizar los ajustes necesarios.\n\n## 2. Mire más allá de las métricas de código\nLa productividad va más allá de las tasas de aceptación o las líneas de código generadas. Los desarrolladores dedican [más del 75 %](https://about.gitlab.com/developer-survey/) de su tiempo a tareas que no están vinculadas con la generación de código. Por lo tanto, usar de forma eficiente la IA podría disminuir el tiempo que los desarrolladores dedican a revisar, probar y mantener el código.Para aprovechar al máximo los beneficios del desarrollo de software asistido por IA, las organizaciones deben adoptar una visión holística sobre el [impacto de la IA en la productividad](https://about.gitlab.com/the-source/ai/how-ai-helps-devsecops-teams-improve-productivity/) y en sus resultados a lo largo del ciclo de desarrollo de software (SDLC). La mejor estrategia combina datos cuantitativos de todo el SDLC con información cualitativa de los desarrolladores sobre el impacto real de la IA en su trabajo diario y su influencia en las estrategias de desarrollo a largo plazo.\n\nUna técnica de medición efectiva es el [marco DORA](https://about.gitlab.com/solutions/value-stream-management/dora/), que evalúa el desempeño de un equipo de desarrollo durante un período específico. Las métricas DORA evalúan la frecuencia de implementación, el plazo de realización de los cambios, el plazo medio de resolución, el índice de fracaso de los cambios y la confiabilidad. Estas métricas proporcionan visibilidad sobre la agilidad, la eficiencia operativa y la velocidad de un equipo y, por lo tanto, son un indicador de qué tan bien una empresa de ingeniería puede equilibrar la velocidad, la calidad y la seguridad.\n\nAdemás, los equipos deben utilizar el [análisis de flujo de valor](https://about.gitlab.com/solutions/value-stream-management/) para examinar todo el flujo de trabajo, desde el concepto hasta la producción. El análisis del flujo de valor supervisa continuamente métricas como el plazo de realización, la duración del ciclo, la frecuencia de implementación y los defectos de producción, y se centra en los resultados comerciales en lugar de en las acciones individuales de los desarrolladores. Este enfoque integral garantiza un proceso de desarrollo más productivo y eficiente.\n\n## 3. Prepárese para los desafíos futuros\nSi bien la IA puede acelerar la producción de código, también puede contribuir a la deuda técnica si el código resultante carece de calidad y seguridad. El código generado por IA a menudo requiere más tiempo de revisión, prueba y mantenimiento. Al inicio, la IA puede ahorrarles tiempo a los desarrolladores, pero es probable que este tiempo se utilice más adelante en el ciclo de desarrollo de software. Además, cualquier falla de seguridad en el código generado por IA requerirá la atención de los equipos de seguridad, lo que implicará tiempo adicional para abordar posibles problemas. Como resultado, los equipos de desarrollo y seguridad pueden inicialmente mostrarse escépticos con respecto a la IA.\n\nPara comenzar, los equipos deberían desarrollar prácticas recomendadas implementando la IA en áreas de menor riesgo antes de expandir sus aplicaciones. Este enfoque cauteloso garantiza una escalabilidad segura y sostenible. Por ejemplo, la IA puede facilitar la generación de código, la generación de pruebas, la corrección de sintaxis y la documentación, lo que ayuda a los equipos a tomar impulso y mejorar los resultados a la vez que aprenden a utilizar la herramienta de manera más efectiva.\n\nAl principio, la productividad podría verse disminuida a medida que los equipos se adaptan a los nuevos flujos de trabajo. Las organizaciones deben proporcionar un período de adaptación para que los equipos puedan determinar la mejor manera de integrar la IA en sus procesos.\n\n## 4. Integre la IA de forma holística con una plataforma de DevSecOps\nUna forma en que las organizaciones pueden mitigar las dificultades iniciales de implementar IA en sus procesos de desarrollo es usar una plataforma de DevSecOps que integre funcionalidades de IA, como la generación de código con tecnología de IA, los resúmenes de discusiones y explicaciones de vulnerabilidades, a lo largo de todo el ciclo de desarrollo del software. Los desarrolladores y los equipos de seguridad pueden colaborar de manera más efectiva e [identificar posibles problemas al principio del proceso de desarrollo](https://about.gitlab.com/the-source/ai/4-ways-ai-can-help-devops-teams-improve-security/) gracias al flujo de trabajo centralizado y optimizado que proporcionan las plataformas de DevSecOps.\n\nLas [herramientas de revisión y prueba de código con tecnología de IA](https://about.gitlab.com/blog/how-gitlab-duo-helps-secure-and-thoroughly-test-ai-generated-code/) dentro de una plataforma de DevSecOps pueden ayudar a identificar y corregir fallas de seguridad o errores de codificación antes de que lleguen a producción. Esto no solo permite ahorrar tiempo, también ayuda a reducir la deuda técnica y mejorar la calidad general del software. Cuando las herramientas de IA forman parte de una plataforma integrada, los equipos también pueden [combinar la IA con el análisis de causa raíz](https://about.gitlab.com/blog/developing-gitlab-duo-blending-ai-and-root-cause-analysis-to-fix-ci-cd/) para corregir errores en los pipelines de CI/CD y entregar código seguro de manera más rápida. El objetivo es aplicar análisis automatizados de calidad de código y de seguridad a todo el código que produce la organización, especialmente al código generado por IA.\n\nAdemás, los equipos pueden dar seguimiento fácilmente al ROI de la IA mediante [análisis integrados en la plataforma que miden el impacto de la IA en la productividad](https://about.gitlab.com/blog/developing-gitlab-duo-ai-impact-analytics-dashboard-measures-the-roi-of-ai/).\n\nLa IA desempeñará un papel fundamental en la evolución de las plataformas de DevSecOps; redefinirá la forma en que los equipos de desarrollo, seguridad y operaciones colaboran para acelerar el desarrollo de software sin comprometer la calidad y la seguridad. Los líderes de negocios querrán conocer cómo sus inversiones en herramientas con tecnología de IA están generando resultados, y los desarrolladores deberían apreciar este interés y aprovechar la oportunidad para demostrar cómo su trabajo se alinea con los objetivos generales de la organización. \n\nAl adoptar un enfoque holístico que evalúa la calidad del código, la colaboración, los costos del downstream y la experiencia del desarrollador, los equipos pueden aprovechar las tecnologías de IA para mejorar los esfuerzos humanos.\n\n> #### Cómo empezar a utilizar la IA en el desarrollo de software\n> \n> Lea nuestro e-book (en inglés) para consultar información concreta que le ayudará a crear un marco estratégico de IA para desarrollar software seguro más rápidamente (disponible en inglés). \n> [Leer el e-book](https://page.gitlab.com/ebook-beginner-guide-ai.html){class=\"button\"}","article","4-steps-for-measuring-the-impact-of-ai","content:es:the-source:ai:4-steps-for-measuring-the-impact-of-ai.yml","yaml","4 Steps For Measuring The Impact Of Ai","content","es/the-source/ai/4-steps-for-measuring-the-impact-of-ai.yml","es/the-source/ai/4-steps-for-measuring-the-impact-of-ai","yml",{"_path":36,"_dir":37,"_draft":6,"_partial":6,"_locale":7,"data":38,"_id":339,"_type":29,"title":340,"_source":31,"_file":341,"_stem":342,"_extension":34},"/shared/es/main-footer","es",{"text":39,"source":40,"edit":46,"contribute":51,"config":56,"items":61,"minimal":331},"Git es una marca registrada de Software Freedom Conservancy, y nuestro uso de «GitLab» está bajo licencia",{"text":41,"config":42},"Ver fuente de la página",{"href":43,"dataGaName":44,"dataGaLocation":45},"https://gitlab.com/gitlab-com/marketing/digital-experience/about-gitlab-com/","page source","footer",{"text":47,"config":48},"Editar 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La industria y las agencias a menudo le piden sus comentarios y respuestas sobre políticas.",{"altText":803,"config":813},{"src":814},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1751463423/mkmdhuxsjggfvokdmdv7.jpg",{"componentName":487},"joel-krooswyk","content:es:the-source:authors:joel-krooswyk.yml","es/the-source/authors/joel-krooswyk.yml","es/the-source/authors/joel-krooswyk",{"_path":821,"_dir":472,"_draft":6,"_partial":6,"_locale":7,"config":822,"seo":823,"content":825,"type":488,"slug":837,"_id":838,"_type":29,"title":824,"_source":31,"_file":839,"_stem":840,"_extension":34},"/es/the-source/authors/josh-lemos",{"layout":9},{"title":824},"Josh Lemos",[826,836],{"componentName":478,"componentContent":827},{"config":828,"name":824,"role":831,"bio":832,"headshot":833},{"gitlabHandle":829,"linkedInProfileUrl":830},"joshlemos","https://www.linkedin.com/in/joshlemos/","Director de Seguridad de la Información","Josh Lemos es director de Seguridad de la Información en GitLab Inc. Sus 20 años de experiencia dirigiendo equipos de seguridad de la información lo convierten una parte fundamental del equipo. 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Además, se encarga de establecer la visión y la dirección de cómo permitir que los usuarios de GitLab aprovechen la ciencia de datos como parte de su programa de DevOps. Antes de unirse a GitLab, ocupó cargos en Indeed, Duo Security y WP Engine.",{"altText":1001,"config":1011},{"src":1012},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1751463427/trfasilaeasosxfcxmsm.jpg",{"componentName":487},"content:es:the-source:authors:taylor-mccaslin.yml","Taylor Mccaslin","es/the-source/authors/taylor-mccaslin.yml","es/the-source/authors/taylor-mccaslin",1761814442381]