{"id":32469,"date":"2025-12-11T23:22:15","date_gmt":"2025-12-12T05:22:15","guid":{"rendered":"https:\/\/reseller.lat\/?p=32469"},"modified":"2025-12-11T23:26:34","modified_gmt":"2025-12-12T05:26:34","slug":"desafio-de-la-ia-no-es-tecnico-es-organizacional-red-hat","status":"publish","type":"post","link":"https:\/\/reseller.lat\/?p=32469","title":{"rendered":"Desaf\u00edo\u00a0de la IA\u00a0no es t\u00e9cnico, es organizacional: Red Hat"},"content":{"rendered":"\n

La inteligencia artificial<\/strong> (IA) no s\u00f3lo ofrece soluciones a nivel empresarial, sino que tambi\u00e9n plantea un desaf\u00edo<\/strong> de la IA demasiado importante.<\/p>\n\n\n\n

As\u00ed, las organizaciones enfrentan obst\u00e1culos estructurales<\/strong> que van desde la complejidad de normalizar datos heterog\u00e9neos<\/strong> hasta la escasez de talento especializado. Sin mencionar el fen\u00f3meno de las \u2018alucinaciones\u2019<\/strong> de los modelos de IA, que generan informaci\u00f3n incorrecta con aparente convicci\u00f3n<\/strong>.<\/p>\n\n\n\n

Este contexto complica la materializaci\u00f3n<\/strong> del retorno de inversi\u00f3n esperado.<\/p>\n\n\n\n

En este sentido, un\u00a0reporte del MIT<\/strong>\u00a0afirma que el\u00a095 %<\/strong>\u00a0de los proyectos piloto de IA generativa en entornos corporativos no logran trascender la fase experimental<\/strong>.<\/p>\n\n\n\n

Por su parte, McKinsey se\u00f1ala que, aunque el 80 %<\/strong> de las organizaciones ya adopt\u00f3 tecnolog\u00edas de IA de \u00faltima generaci\u00f3n<\/strong>. Ese mismo porcentaje no experiment\u00f3 mejoras significativas<\/strong> en su rendimiento operativo.<\/p>\n\n\n\n

\u00bfQu\u00e9 explica esta brecha entre la expectativa y realidad del desaf\u00edo de la IA?<\/strong><\/h2>\n\n\n\n

La respuesta radica en que cada organizaci\u00f3n\u00a0enfrenta desaf\u00edos de negocio particulares<\/strong>\u00a0y requiere resultados espec\u00edficos,\u00a0no soluciones gen\u00e9ricas<\/strong>. En consecuencia, durante 2026 las empresas se\u00a0enfocar\u00e1n<\/strong>\u00a0en modelos de IA\u00a0m\u00e1s peque\u00f1os<\/strong>, dise\u00f1ados para casos de uso concretos y medibles<\/strong>.<\/p>\n\n\n\n

Frente a este escenario, el primer paso deber\u00eda ser automatizar procesos<\/strong>, lo que tambi\u00e9n permitir\u00e1 entrenar a los colaboradores<\/strong> y destinarlos a proyectos de IA m\u00e1s innovadores.<\/p>\n\n\n\n

Al mismo tiempo, ser\u00e1 clave trabajar en la normalizaci\u00f3n de los datos<\/strong>: revisarlos, \u2018limpiarlos\u2019 y optimizar<\/strong> su calidad. Una vez concluida esta etapa, las empresas deber\u00edan definir los casos de uso<\/strong> prioritarios, y establecer indicadores de evoluci\u00f3n<\/strong> precisos.<\/p>\n\n\n\n

Entre las\u00a0aplicaciones de IA<\/strong>\u00a0que ganan tracci\u00f3n destacan los agentes conversacionales con\u00a0reconocimiento de voz<\/strong>\u00a0para elevar la calidad de la atenci\u00f3n<\/strong>\u00a0al cliente. Plataformas de capacitaci\u00f3n de personal que\u00a0optimizan el flujo de informaci\u00f3n<\/strong>\u00a0organizacional y la implementaci\u00f3n y refinamiento de\u00a0procesos internos<\/strong>\u00a0con asistencia de IA.<\/p>\n\n\n\n

Completan el listado, la gesti\u00f3n predictiva de proyectos<\/strong> basada en el an\u00e1lisis de iniciativas previas y los sistemas de detecci\u00f3n<\/strong> y prevenci\u00f3n de fraude en operaciones financieras.<\/p>\n\n\n\n

Durante el pr\u00f3ximo a\u00f1o, los agentes de IA<\/strong> (tambi\u00e9n denominados Agentic AI) alcanzar\u00e1n un rol protag\u00f3nico<\/strong>.<\/p>\n\n\n\n

Estos sistemas, capaces de\u00a0ejecutar procesos complejos<\/strong>\u00a0con comprensi\u00f3n contextual, representan un\u00a0salto cualitativo<\/strong> respecto a la automatizaci\u00f3n tradicional.<\/p>\n\n\n\n

Gartner proyecta<\/strong> que hacia fines del pr\u00f3ximo a\u00f1o, el 40 %<\/strong> de las aplicaciones empresariales integrar\u00e1n agentes de IA especializados por tarea<\/strong>. Ello, en contraste con menos del 5 %<\/strong> que registr\u00f3 en 2025.<\/p>\n\n\n\n

Estos agentes operan con datos rigurosamente gestionados<\/strong>, en cumplimiento de marcos regulatorios y normativas regionales<\/strong>. Por tanto, la normalizaci\u00f3n de datos se convierte en un requisito impostergable<\/strong> para cualquier empresa que busque resultados exitosos<\/strong>.<\/p>\n\n\n\n

Alternativas<\/strong><\/h2>\n\n\n\n

Para apoyar estas innovaciones<\/strong>, Red Hat propone soluciones para automatizaci\u00f3n inteligente. Adem\u00e1s, de una plataforma abierta y flexible<\/strong> que permite a las compa\u00f1\u00edas definir los aceleradores<\/strong> de software seg\u00fan cada caso de uso.<\/p>\n\n\n\n

Al seguir estos pasos y trabajar sobre\u00a0plataformas abiertas<\/strong>, est\u00e1ndar e h\u00edbridas (combinando modelos on-premise y en la nube), los desarrollos de IA empresariales<\/strong>\u00a0tienen muchas m\u00e1s posibilidades de\u00a0alcanzar el \u00e9xito<\/strong>.<\/p>\n\n\n\n

Por Alejandro Raffaele<\/a>, director senior de Ventas Enterprise para Latinoam\u00e9rica de Red Hat<\/a><\/em><\/p>\n","protected":false},"excerpt":{"rendered":"

La inteligencia artificial (IA) no s\u00f3lo ofrece soluciones a nivel empresarial, sino que tambi\u00e9n plantea un desaf\u00edo de la IA demasiado importante. As\u00ed, las organizaciones enfrentan obst\u00e1culos estructurales que van desde la complejidad de normalizar datos heterog\u00e9neos hasta la escasez de talento especializado. Sin mencionar el fen\u00f3meno de las \u2018alucinaciones\u2019 de los modelos de IA, que generan informaci\u00f3n incorrecta con aparente convicci\u00f3n. Este contexto complica la materializaci\u00f3n del […]<\/p>\n","protected":false},"author":3,"featured_media":32475,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[84,61],"tags":[2586,3112,88,3109,247,1774,3108,180,1348,111,3107,3106,3111,3110,352],"class_list":["post-32469","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-opinion","category-todas-las-noticias","tag-agentic-ai","tag-alejandro-raffaele","tag-america-latina","tag-ansible","tag-automatizacion-2","tag-desafios","tag-gartner","tag-ia","tag-ia-empresarial","tag-inteligencia-artificial","tag-mckinsey","tag-mit","tag-normalizacion-de-datos","tag-openshift","tag-red-hat"],"yoast_head":"\nDesaf\u00edo de la IA no es t\u00e9cnico, es organizacional: Red Hat<\/title>\n<meta name=\"description\" content=\"Cada organizaci\u00f3n enfrenta retos particulares y requiere resultados espec\u00edficos, no 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