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Al finalizar el año, el asistente ya había respondido a más de 330 000 preguntas, lo que ayudó a los usuarios a trabajar más rápido y tomar mejores decisiones.\u003C/p\u003E\n\u003Cp\u003EEn los últimos meses, publicamos varias entradas dirigidas a desarrolladores sobre los fundamentos técnicos para crear un asistente de IA empresarial: cómo estructurar los datos de ventas, habilitar la recuperación de conocimiento a partir de contenido no estructurado, probar el comportamiento del agente y perfeccionar las instrucciones. Esas publicaciones resultaron útiles, pero las preguntas que seguíamos recibiendo no eran meramente técnicas:\u003C/p\u003E\n\u003Cul\u003E\n\u003Cli\u003E\u003Cp\u003E¿Cómo formasteis el equipo del proyecto?\u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cp\u003E¿Cuál fue el calendario de desarrollo?\u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cp\u003E¿Cómo lo hiciste sin quebrar la confianza?\u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cp\u003E¿Qué tácticas de gestión del cambio funcionaron realmente?\u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cp\u003E¿Qué resultados observasteis tras el lanzamiento?\u003C/p\u003E\n\u003C/li\u003E\n\u003C/ul\u003E\n\u003Cp\u003EEste blog se centra en esos aspectos no técnicos: la estructura del equipo, las fases de lanzamiento, la estrategia de alcance y cómo escalamos desde un producto mínimo viable (MVP) hasta una capacidad empresarial de confianza.\u003C/p\u003E\n\u003Ch2\u003EEquipo de desarrollo y fases de lanzamiento\u003C/h2\u003E\n\u003Cp\u003EEmpezamos a finales de febrero de 2025 con un objetivo acotado: crear un asistente de conocimiento basado en la generación aumentada por recuperación (RAG) para que los equipos de comercialización (GTM) pudieran encontrar los documentos adecuados en herramientas, páginas web y unidades de almacenamiento fragmentadas. En aquel momento, el proyecto contaba con un científico de datos dedicado.\u003C/p\u003E\n\u003Cp\u003EA finales de mayo de 2025, decidimos desarrollar un asistente de IA completo para GTM con Snowflake CoWork que no solo indexara contenido de conocimiento, sino que también integrara datos estructurados de ventas y marketing. Ese cambio elevó los requisitos de calidad y fiabilidad, por lo que ampliamos el equipo a entre tres y cuatro científicos de datos e incorporamos un responsable de producto dedicado.\u003C/p\u003E\n\u003Cp\u003EDesde el primer día, tratamos la calidad y la confianza de los usuarios como \u003Cb\u003EP(-1)\u003C/b\u003E. Las entrevistas con usuarios y nuestra experiencia previa en proyectos de IA dejaron algo claro: la primera impresión determina la adopción. Si las primeras experiencias no son fiables, recuperar la confianza exige muchísimo más esfuerzo que hacerlo bien desde el principio.\u003C/p\u003E\n\u003Cp\u003EPor eso elegimos una estrategia de despliegue por fases: empezar con poco, validar la calidad y los flujos de trabajo y, después, ampliar.\u003C/p\u003E\n\u003Cp\u003EEstas fueron, a grandes rasgos, nuestras fases de lanzamiento:\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"image":{"id":"image-0a81605d0c","height":"438","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--cef13f2d-06a1-491a-aaa7-fbca5e23ba4d/pilot-1.png?quality=85&preferwebp=true","alt":"pilot-1","lazyEnabled":true,"isLcpImage":true,"width":"1468",":type":"snowflake-site/components/image"},"blog_text_2105309366":{"id":"blog-text-ca3517995c","text":"\u003Cp\u003EConsejo: Un error habitual que observamos es apresurarse a extender el uso de un agente antes de que cumpla sistemáticamente el nivel de calidad exigido. Crear prototipos de agentes de IA es fácil; lo difícil es lanzar agentes fiables para miles de usuarios. Da a los equipos el tiempo y el espacio necesarios para ganarse la confianza desde el principio: dará sus frutos más adelante.\u003C/p\u003E\n\u003Ch2\u003EDe un MVP acotado a la ampliación de capacidades\u003C/h2\u003E\n\u003Cp\u003ELos entornos empresariales son complejos. Solo en nuestra organización de ventas y marketing, damos soporte a más de 15 perfiles distintos, cada uno con flujos de trabajo y necesidades diferentes:\u003C/p\u003E\n\u003Cul\u003E\n\u003Cli\u003E\u003Cp\u003EEjecutivos de cuentas (AE)\u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cp\u003EIngenieros de soluciones (SE)\u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cp\u003EComerciales de prospección (SDR)\u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cp\u003EResponsables de marketing de producto\u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cp\u003EResponsables de partners y alianzas\u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cp\u003EEspecialistas de Snowflake Professional Services\u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cp\u003EDirección de ventas y marketing\u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cp\u003EResponsables de campañas\u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cp\u003E… y otros perfiles\u003C/p\u003E\n\u003C/li\u003E\n\u003C/ul\u003E\n\u003Cp\u003EComo con cualquier producto, evitamos caer en la trampa de “conectarlo todo y esperar que los usuarios sepan qué hacer”. En su lugar, empezamos deliberadamente con un MVP más acotado y nos centramos en los perfiles para los que podíamos aportar el máximo valor con mayor rapidez.\u003C/p\u003E\n\u003Cp\u003EEn función del tamaño de la audiencia y del esfuerzo de desarrollo necesario para cada perfil, focalizamos el lanzamiento de disponibilidad general (GA) en \u003Ci\u003Elos AE, los SE y los SDR.\u003C/i\u003E Estos tres grupos representaban aproximadamente el \u003Ci\u003E50 % de nuestra audiencia objetivo de 6000 usuarios\u003C/i\u003E, lo que nos permitió maximizar el impacto inicial y mantener un alcance viable.\u003C/p\u003E\n\u003Cp\u003ETras la GA, ampliamos de forma deliberada el alcance, habilitando nuevos perfiles y capacidades paso a paso, sin comprometer la fiabilidad para la audiencia principal.\u003C/p\u003E\n\u003Cp\u003EEste es un ejemplo ilustrativo de cómo se ampliaron las capacidades de nuestro agente con el tiempo:\u003C/p\u003E\n\u003Cul\u003E\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003EAlcance de la GA:\u003C/b\u003E Asistente de conocimiento + datos esenciales de Salesforce, uso del producto y finanzas\u003Cbr\u003E\n\u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003EPrimer mes tras la GA:\u003C/b\u003E Corrección de errores y ampliación de la cobertura y la profundidad de las mismas fuentes\u003Cbr\u003E\n\u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003EFases posteriores: \u003C/b\u003ENuevas fuentes de datos, como datos de marketing, transcripciones de llamadas y correos electrónicos, datos de partners, compatibilidad con búsquedas web y mucho más\u003C/p\u003E\n\u003C/li\u003E\n\u003C/ul\u003E\n\u003Cp\u003ECuando lanzamos el asistente de IA a mediados de septiembre de 2025, utilizaba seis vistas semánticas con 48 tablas y unas 1400 columnas. Tras la GA, lanzamos más de 40 funciones y fuentes de datos nuevas, y ampliamos la capa de datos a 10 vistas semánticas con 64 tablas y más de 1750 columnas.\u003C/p\u003E\n\u003Cp\u003EConsejo: Un alcance excesivamente amplio es uno de los motivos más habituales por los que las iniciativas empresariales de IA se estancan. Empieza con un alcance en el que el agente responda a menos preguntas, pero lo haga correctamente. Es más fácil ampliar las capacidades una vez establecida la confianza que recuperar la adopción tras los primeros fallos.\u003C/p\u003E\n\u003Ch2\u003EActivación de usuarios y gestión del cambio\u003C/h2\u003E\n\u003Cp\u003EImplementar soluciones empresariales con éxito lleva tiempo, a menudo un trimestre completo o más. Como ocurre con el lanzamiento de cualquier producto, los usuarios se distribuyen de forma natural a lo largo de la curva de adopción: innovadores, primeros usuarios, mayoría temprana y tardía, y rezagados.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"image_1962251299":{"id":"image-674a7a86fb","height":"684","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--07a85d94-65e8-497c-86db-f661e8bc3160/pilot-2.png?quality=85&preferwebp=true","alt":"pilot-2","lazyEnabled":true,"isLcpImage":false,"width":"1134",":type":"snowflake-site/components/image"},"blog_text_1827519857":{"id":"blog-text-9fcf9e20ec","text":"\u003Cp\u003E\u003Ci\u003ETeoría de la difusión de las innovaciones de Everett Rogers.\u003C/i\u003E\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Cp\u003EDurante las fases piloto y beta, la interacción suele proceder de los usuarios situados en la parte inicial de esa curva, es decir, aquellos deseosos de experimentar con herramientas nuevas. Una vez que se llega a la GA y se escala a miles de usuarios, la realidad cambia. Los usuarios tardíos entran en escena, aumentan las expectativas y puede surgir frustración si la adopción no avanza con suficiente rapidez.\u003C/p\u003E\n\u003Cp\u003EEs aquí donde muchas iniciativas de IA que, por lo demás, son sólidas se estancan: no porque la tecnología no funcione, sino porque los usuarios nunca llegan a adoptarla por completo.\u003C/p\u003E\n\u003Ch3\u003EAsegurarse de que los usuarios realmente lo prueben\u003C/h3\u003E\n\u003Cp\u003EIncorporar un asistente de IA al flujo de trabajo diario supone un cambio de hábito importante. La mayoría de los usuarios no cambiarán a menos que tu solución sea \u003Ci\u003Emuchísimo mejor\u003C/i\u003E (diez veces más rápida y diez veces más fácil) o permita hacer algo que antes sencillamente no podían hacer. La paridad funcional con las herramientas existentes rara vez es suficiente, sobre todo para los usuarios tardíos.\u003C/p\u003E\n\u003Cp\u003EDicho esto, ni siquiera el mejor producto puede ganarse a los usuarios si estos nunca llegan a probarlo.\u003C/p\u003E\n\u003Cp\u003EEntonces, ¿cómo puedes lograr que los usuarios \u003Ci\u003Eprueben\u003C/i\u003E tu asistente de IA y \u003Ci\u003Elo utilicen regularmente\u003C/i\u003E?\u003C/p\u003E\n\u003Ch3\u003EValidar la calidad y optimizar la recurrencia de uso en primer lugar\u003C/h3\u003E\n\u003Cp\u003ENuestro enfoque fue deliberado y secuencial, en consonancia con nuestras fases de lanzamiento:\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"image_361604255":{"id":"image-08390aaad3","height":"318","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--bcb8eeb8-5748-4053-a393-aed378b9c2be/pilot-3.png?quality=85&preferwebp=true","alt":"pilot-3","lazyEnabled":true,"isLcpImage":false,"width":"1306",":type":"snowflake-site/components/image"},"blog_text_1428120683":{"id":"blog-text-711859627d","text":"\u003Cp\u003EDurante la fase piloto, nos centramos exclusivamente en \u003Ci\u003Ela calidad y la confianza\u003C/i\u003E. Nuestro principal objetivo era validar la corrección, la fiabilidad y la utilidad básica; recuerda que la calidad es \u003Cb\u003EP(-1)\u003C/b\u003E.\u003C/p\u003E\n\u003Cp\u003EEn la fase beta, una vez demostrada la calidad, centramos la atención en la \u003Ci\u003Emadurez de las funciones y la recurrencia de uso\u003C/i\u003E. La pregunta pasó a ser: \u003Ci\u003E¿resuelve este agente suficientes problemas reales como para que los usuarios quieran volver a usarlo semana tras semana?\u003C/i\u003E\u003C/p\u003E\n\u003Cp\u003ELos resultados nos dieron confianza:\u003C/p\u003E\n\u003Cul\u003E\n\u003Cli\u003E\u003Cp\u003ENPS \u003E92 % entre los usuarios de la fase beta\u003Cbr\u003E\n\u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cp\u003ETasa de retención de usuarios activos semanales (WAU) \u003E70 %\u003C/p\u003E\n\u003C/li\u003E\n\u003C/ul\u003E\n\u003Cp\u003EEstas métricas nos indicaron que teníamos un producto cuyo escalado merecía la pena.\u003C/p\u003E\n\u003Ch3\u003EGenerar impulso y entusiasmo en la organización\u003C/h3\u003E\n\u003Cp\u003EEn la fase de GA, nuestra pregunta principal pasó a ser sencilla: \u003Ci\u003E¿conocen los usuarios el GTM AI Assistant y lo están probando de verdad?\u003C/i\u003E\u003C/p\u003E\n\u003Cp\u003EPara impulsar la adopción, colaboramos estrechamente con nuestro equipo de capacitación comercial y nos aseguramos de contar con los elementos fundamentales:\u003C/p\u003E\n\u003Cul\u003E\n\u003Cli\u003E\u003Cp\u003EUna página interna dedicada al producto\u003Cbr\u003E\n\u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cp\u003EGuías de usuario claras y documentación práctica\u003Cbr\u003E\n\u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cp\u003EVídeos breves en los que los usuarios más destacados comparten prácticas recomendadas\u003Cbr\u003E\n\u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cp\u003EUn canal de Slack dedicado a comentarios y solicitudes de funciones\u003Cbr\u003E\n\u003Cbr\u003E\n\u003C/p\u003E\n\u003C/li\u003E\n\u003C/ul\u003E\n\u003Cp\u003EA partir de ahí, generamos impulso de forma activa:\u003C/p\u003E\n\u003Cul\u003E\n\u003Cli\u003E\u003Cp\u003ECorreos electrónicos de lanzamiento y demostraciones en directo con cada equipo\u003Cbr\u003E\n\u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cp\u003EMenciones periódicas de los directivos de ventas, e incluso de nuestro CEO, en reuniones generales y de equipo\u003Cbr\u003E\n\u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cp\u003EInformes semanales de adopción compartidos con la dirección de ventas para que pudieran fomentar la participación en sus equipos\u003Cbr\u003E\n\u003C/p\u003E\n\u003C/li\u003E\n\u003C/ul\u003E\n\u003Cp\u003EEsta combinación de capacitación, visibilidad y apoyo del equipo directivo resultó ser crucial.\u003C/p\u003E\n\u003Ch3\u003E¿El resultado? Adopción sostenida a gran escala\u003C/h3\u003E\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"image_76636164":{"id":"image-609eaf79db","height":"722","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--0285f1f1-58e5-4335-885a-28d2b2da1737/pilot-4.png?quality=85&preferwebp=true","alt":"pilot-4","lazyEnabled":true,"isLcpImage":false,"width":"1288",":type":"snowflake-site/components/image"},"blog_text_1129566895":{"id":"blog-text-6531ea426f","text":"\u003Cp\u003E\u003Ci\u003EGracias a una acción disciplinada de activación y gestión del cambio, impulsamos una adopción significativa del GTM AI Assistant durante los tres meses posteriores a la GA.\u003C/i\u003E\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Cp\u003ETodo este esfuerzo dio sus frutos. En las dos primeras semanas tras la GA, aproximadamente el \u003Cb\u003E25 %\u003C/b\u003E de los usuarios habilitados había probado el agente al menos una vez. A finales de año, esa cifra había aumentado hasta casi el \u003Cb\u003E77 % del total\u003C/b\u003E y superaba el \u003Cb\u003E90 %\u003C/b\u003E entre nuestros perfiles principales (AE, SE y SDR).\u003C/p\u003E\n\u003Cp\u003EPara nosotros, esto confirmó una lección clave: \u003Cb\u003Eel escalado de la IA empresarial no es solo un reto técnico, sino también un problema de activación del producto y gestión del cambio.\u003C/b\u003E\u003C/p\u003E\n\u003Ch2\u003EVisión del producto tras el lanzamiento\u003C/h2\u003E\n\u003Cp\u003EEl cambio funciona en ambos sentidos.\u003C/p\u003E\n\u003Cp\u003ETransformar los flujos de trabajo de comercialización con agentes de IA empresarial supone un cambio importante, no solo para los usuarios, sino también para los equipos de datos que crean esos agentes.\u003C/p\u003E\n\u003Cp\u003ETradicionalmente, los equipos de datos están acostumbrados a trabajar en proyectos relativamente acotados: crear pipelines de datos, entrenar modelos o lanzar paneles de control, a menudo con una evolución limitada después del lanzamiento. Una vez implementados, estos activos suelen requerir únicamente un mantenimiento incremental.\u003C/p\u003E\n\u003Cp\u003ELos agentes de IA son radicalmente distintos.\u003C/p\u003E\n\u003Cp\u003EUn asistente de IA empresarial es un \u003Ci\u003Eproducto vivo\u003C/i\u003E. Interactúa continuamente con los usuarios, se adapta a nuevos datos, evoluciona conforme cambian los flujos de trabajo y debe seguir el ritmo de la rápida innovación en plataformas y modelos. Esto exige un cambio hacia una visión del producto (product thinking), un diseño a nivel de sistema y una colaboración sostenida entre los equipos a lo largo del tiempo.\u003C/p\u003E\n\u003Ch3\u003EAdaptación del equipo tras la GA\u003C/h3\u003E\n\u003Cp\u003ETras un lanzamiento satisfactorio en GA, tuvimos que cambiar nuestra forma de trabajar como equipo.\u003C/p\u003E\n\u003Cp\u003ECasi de inmediato, observamos:\u003C/p\u003E\n\u003Cul\u003E\n\u003Cli\u003E\u003Cp\u003EUn aumento de solicitudes de funciones por parte de los usuarios\u003Cbr\u003E\n\u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cp\u003EMayores expectativas en cuanto a capacidad de respuesta y fiabilidad\u003Cbr\u003E\n\u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cp\u003EUna necesidad creciente de equilibrar la iteración rápida y el mantenimiento de la confianza\u003Cbr\u003E\n\u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cp\u003ECambios continuos en la plataforma de IA subyacente que nos obligaban a replantearnos decisiones de diseño anteriores\u003Cbr\u003E\n\u003C/p\u003E\n\u003C/li\u003E\n\u003C/ul\u003E\n\u003Cp\u003EAl mismo tiempo, no podíamos permitir que la calidad disminuyera. Había que corregir los errores rápidamente, evitar regresiones e implementar mejoras sin desestabilizar la experiencia en la que los usuarios habían aprendido a confiar.\u003C/p\u003E\n\u003Cp\u003EEl escalado del producto implicaba el escalado de nuestro modelo operativo.\u003C/p\u003E\n\u003Ch3\u003ENuestra inversión tras el lanzamiento\u003C/h3\u003E\n\u003Cp\u003EPara evitar vernos desbordados y mantener el impulso, invertimos desde el principio en las siguientes áreas:\u003C/p\u003E\n\u003Cul\u003E\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003EUna forma de trabajo ágil:\u003C/b\u003E Adoptamos un modelo de desarrollo basado en sprints, con un marco claro de recepción, triaje y priorización para equilibrar la corrección de errores, las solicitudes de funciones y las mejoras a largo plazo.\u003Cbr\u003E\n\u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003EAmpliación del equipo con habilidades complementarias:\u003C/b\u003E Además de ciencia de datos, aumentamos nuestra capacidad de ingeniería de analíticas y de back-end de acuerdo con las necesidades del roadmap y la complejidad del sistema.\u003Cbr\u003E\n\u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003EPruebas automatizadas e integración y desarrollo continuos (CI/CD): \u003C/b\u003EPoco después de la GA, invertimos en marcos de pruebas automatizadas y pipelines de CI/CD para aumentar la velocidad de implementación sin rebajar nuestro elevado nivel de calidad.\u003Cbr\u003E\n\u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003ETiempo dedicado al estado y la evolución de la plataforma: \u003C/b\u003ECada sprint incluía capacidad reservada de forma explícita para la refactorización, la evolución de la arquitectura y las actualizaciones de la plataforma, con el fin de evitar la deuda técnica a largo plazo.\u003Cbr\u003E\n\u003C/p\u003E\n\u003C/li\u003E\n\u003C/ul\u003E\n\u003Ch3\u003ELa conclusión clave\u003C/h3\u003E\n\u003Cp\u003EEl lanzamiento de un agente de IA empresarial no es la meta, sino el punto de partida.\u003C/p\u003E\n\u003Cp\u003EEl éxito a largo plazo exige tratar los agentes de IA como productos, no como proyectos: con responsabilidad, iteración, rigor operativo e inversión continua. Los equipos que adoptan este cambio están mucho mejor posicionados para escalar de forma responsable, adaptarse a los cambios y seguir aportando valor a medida que evolucionan tanto las expectativas de los usuarios como las capacidades de la IA.\u003C/p\u003E\n\u003Ch2\u003EEl impacto y el retorno de la inversión\u003C/h2\u003E\n\u003Cp\u003ELlegados a este punto, quizá te preguntes: \u003Ci\u003E¿cuáles fueron los resultados reales y mereció la pena la inversión?\u003C/i\u003E\u003C/p\u003E\n\u003Cp\u003EVeámoslo en detalle.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"image_777003060":{"id":"image-2faffbd496","height":"736","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--62806163-a5a6-4a28-a91d-7e1b52311beb/pilot-5.png?quality=85&preferwebp=true","alt":"pilot-5","lazyEnabled":true,"isLcpImage":false,"width":"1302",":type":"snowflake-site/components/image"},"blog_text_560111879":{"id":"blog-text-4a548c130e","text":"\u003Cp\u003EA finales de 2025, el GTM AI Assistant respondía a más de \u003Cb\u003E35 000 preguntas a la semana\u003C/b\u003E para más de \u003Cb\u003E2500 usuarios activos semanales\u003C/b\u003E, frente a unas \u003Cb\u003E10 000 preguntas semanales\u003C/b\u003E en el lanzamiento inicial.\u003C/p\u003E\n\u003Cp\u003EEste crecimiento no se debió únicamente a la adopción.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"image_1372633756":{"id":"image-88afb34e76","height":"802","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--0a1d497a-3156-439b-9f46-e2b78c9d9f29/pilot-6.png?quality=85&preferwebp=true","alt":"pilot-6","lazyEnabled":true,"isLcpImage":false,"width":"1314",":type":"snowflake-site/components/image"},"blog_text_1906359881":{"id":"blog-text-e68f08af82","text":"\u003Cp\u003EDurante los tres meses posteriores a la GA, a medida que ampliábamos las capacidades del asistente y los usuarios empezaban a incorporarlo a sus flujos de trabajo diarios, \u003Cb\u003Ela intensidad de uso aumentó significativamente\u003C/b\u003E. La media de preguntas por usuario activo semanal aumentó de \u003Cb\u003E8,5 a 14\u003C/b\u003E, lo que reflejaba tanto una mayor confianza como una integración más profunda en el trabajo real.\u003C/p\u003E\n\u003Ch3\u003EAumento de la productividad a escala empresarial\u003C/h3\u003E\n\u003Cp\u003EEl ahorro de tiempo variaba según el caso de uso:\u003C/p\u003E\n\u003Cul\u003E\n\u003Cli\u003E\u003Cp\u003EMinutos para consultas sencillas de conocimientos o datos\u003Cbr\u003E\n\u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cp\u003EHoras para análisis más complejos que, de otro modo, requerirían SQL personalizado o la ayuda de un analista\u003Cbr\u003E\n\u003C/p\u003E\n\u003C/li\u003E\n\u003C/ul\u003E\n\u003Cp\u003EIncluso partiendo de una \u003Cb\u003Eestimación conservadora de cinco minutos ahorrados por consulta\u003C/b\u003E, el impacto se multiplica rápidamente. A nuestra escala, esto equivalía a \u003Cb\u003Ela productividad anual de más de 65 empleados a tiempo completo\u003C/b\u003E en una organización de 6000 personas.\u003C/p\u003E\n\u003Cp\u003EY se trata de una estimación de mínimos. \u003C/p\u003E\n\u003Ch3\u003EDemostración del retorno de la inversión a escala\u003C/h3\u003E\n\u003Cp\u003EA finales de 2025, realizamos un análisis detallado para comprender tanto la estructura de costes como el retorno de la inversión del GTM AI Assistant.\u003C/p\u003E\n\u003Cp\u003EEl análisis mostró que el \u003Cb\u003Ecoste por usuario activo era comparable al de las herramientas estándar de productividad empresarial\u003C/b\u003E por usuario. Al compararlo con el aumento de la productividad conseguido, el asistente ofrecía\u003Cb\u003E un retorno de la inversión superior a 5 veces\u003C/b\u003E, incluso antes de llevar a cabo iniciativas específicas de optimización de costes.\u003C/p\u003E\n\u003Cp\u003EEs importante señalar que esta estimación refleja únicamente el \u003Cb\u003Eahorro directo de productividad\u003C/b\u003E. No incluye beneficios secundarios, como una menor carga para los analistas, ciclos de ventas más rápidos y una mejor calidad de las decisiones de los equipos de ventas y marketing. En otras palabras, la rentabilidad era convincente mucho antes de que la solución alcanzara su plena madurez o eficiencia operativa.\u003C/p\u003E\n\u003Ch2\u003EReflexiones finales\u003C/h2\u003E\n\u003Cp\u003ELa IA está transformando radicalmente los equipos empresariales y de comercialización.\u003C/p\u003E\n\u003Cp\u003ELa era de las demostraciones aisladas y los experimentos a pequeña escala está llegando a su fin. La IA empresarial ya está generando un \u003Cb\u003Eimpacto real y cuantificable a escala\u003C/b\u003E. Pero lograr ese impacto requiere algo más que modelos potentes o prompts ingeniosos: exige la mentalidad adecuada, una ejecución disciplinada y una atención especial a la gestión del cambio.\u003C/p\u003E\n\u003Cp\u003EComo hemos explicado en este artículo, el escalado satisfactorio de los agentes de IA empresarial implica:\u003C/p\u003E\n\u003Cul\u003E\n\u003Cli\u003E\u003Cp\u003EConsiderar la calidad y la confianza como aspectos irrenunciables\u003Cbr\u003E\n\u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cp\u003ERealizar el lanzamiento de forma deliberada y ampliar el alcance de manera intencionada\u003Cbr\u003E\n\u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cp\u003EInvertir en activación y adopción, no solo en tecnología\u003Cbr\u003E\n\u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cp\u003EOrientar a los equipos de datos hacia la responsabilidad sobre el producto a largo plazo\u003Cbr\u003E\n\u003C/p\u003E\n\u003C/li\u003E\n\u003C/ul\u003E\n\u003Cp\u003ELas empresas que lo hagan bien en los próximos meses no solo mejorarán la productividad, sino que transformarán profundamente la forma de trabajar de sus equipos y obtendrán una ventaja competitiva significativa.\u003C/p\u003E\n\u003Cp\u003EEl cambio ya está en marcha. Las empresas que actúen ahora definirán la próxima generación de la ejecución de la estrategia de GTM.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/blog/blog-text"}},":itemsOrder":["blog_text","image","blog_text_2105309366","image_1962251299","blog_text_1827519857","image_361604255","blog_text_1428120683","image_76636164","blog_text_1129566895","image_777003060","blog_text_560111879","image_1372633756","blog_text_1906359881"],":type":"wcm/foundation/components/responsivegrid"},"responsivegrid_premium_content_banner":{"columnCount":12,"columnClassNames":{},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","appliedCssClassNames":"snowflake-responsive-component-top-padding-medium",":items":{},":itemsOrder":[],":type":"wcm/foundation/components/responsivegrid"},"container_author_chip":{"layout":"RESPONSIVE_GRID","columnCount":12,"columnClassNames":{"author_chip":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 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