{"id":1,"date":"2025-06-14T13:48:38","date_gmt":"2025-06-14T13:48:38","guid":{"rendered":"https:\/\/seonet.codeyuzu.com\/evolvis\/?p=1"},"modified":"2025-10-30T16:56:04","modified_gmt":"2025-10-30T16:56:04","slug":"el-futuro-de-la-ia-empresarial-por-que-las-decisiones-importan-mas-que-los-modelos","status":"publish","type":"post","link":"https:\/\/evolvis.ai\/en\/el-futuro-de-la-ia-empresarial-por-que-las-decisiones-importan-mas-que-los-modelos\/","title":{"rendered":"The Future of Enterprise AI: Why Decisions Matter More Than Models"},"content":{"rendered":"<p><span style=\"font-weight: 400;\">In the fast-paced world of enterprise artificial intelligence, there is a fundamental divide that is redefining how companies leverage this technology. It's not about how advanced your AI model is, but how well it helps you make better decisions.<\/span><\/p>\r\n<p><b>The trap of model-centric AI<\/b><\/p>\r\n<p><span style=\"font-weight: 400;\">Most companies fall into the same trap: they become obsessed with having the most sophisticated model, the most complex algorithm, or the latest version of GPT. They invest millions in technology that promises to revolutionize their operations, only to discover that their teams are still struggling with the same basic questions as ever:<\/span><\/p>\r\n<ul>\r\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Should we launch this product?<\/span><\/li>\r\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">What is the best pricing strategy?<\/span><\/li>\r\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Where should we invest next quarter?<\/span><\/li>\r\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">How can we retain our best customers?<\/span><\/li>\r\n<\/ul>\r\n<p><b>The problem is not a lack of data or sophisticated models. The problem is that these tools are not designed to make decisions.<\/b><\/p>\r\n<p><b>The paradigm shift: decision-centric AI<\/b><\/p>\r\n<p><span style=\"font-weight: 400;\">Decision-centric AI represents a completely different approach. Instead of asking \"what model to use?\", it asks \"what decision do I need to make?\".<\/span><\/p>\r\n<p><b>Key characteristics of a decision-centric system:<\/b><\/p>\r\n<ol>\r\n<li><b> Results oriented<\/b><\/li>\r\n<\/ol>\r\n<ul>\r\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Focuses on specific decisions that impact the business<\/span><\/li>\r\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Combines multiple types of AI as needed (generative, predictive, analytical)<\/span><\/li>\r\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Prioritizes action over information<\/span><\/li>\r\n<\/ul>\r\n<ol start=\"2\">\r\n<li><b> Intelligent adaptability<\/b><\/li>\r\n<\/ol>\r\n<ul>\r\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Not limited to a single model type<\/span><\/li>\r\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Automatically selects the best technology for each decision<\/span><\/li>\r\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Evolves with business needs<\/span><\/li>\r\n<\/ul>\r\n<ol start=\"3\">\r\n<li><b> Business context<\/b><\/li>\r\n<\/ol>\r\n<ul>\r\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Understand your industry's objectives and constraints<\/span><\/li>\r\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Consider factors such as budget, time and resources<\/span><\/li>\r\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Provides actionable recommendations, not just insights<\/span><\/li>\r\n<\/ul>\r\n<p><b>The real impact on business<\/b><\/p>\r\n<p><b>Case study: retail<\/b><\/p>\r\n<p><b>Model-centric AI:<\/b><span style=\"font-weight: 400;\"> A retail chain invests $2M in an ML model to predict demand. They get accurate predictions but still don't know how much inventory to buy or when to buy it.<\/span><\/p>\r\n<p><b>Decision-centric AI:<\/b><span style=\"font-weight: 400;\"> The same retailer implements a system that not only predicts demand, but also recommends specific decisions: \"Buy 500 units of product X for store Y, order them on Tuesday to avoid weekend stock-outs\".<\/span><\/p>\r\n<p><b>Result:<\/b><span style=\"font-weight: 400;\"> 35% reduction in dead inventory and 28% increase in product availability.<\/span><\/p>\r\n<p><b>How to implement decision-centric AI<\/b><\/p>\r\n<p><b>Step 1: map your critical decisions<\/b><\/p>\r\n<p><span style=\"font-weight: 400;\">Before thinking about technology, identify the 5-10 most important decisions your company makes on a regular basis. For example:<\/span><\/p>\r\n<ul>\r\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Pricing decisions<\/span><\/li>\r\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Marketing budget allocation<\/span><\/li>\r\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Recruitment and retention of talent<\/span><\/li>\r\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Geographic expansion<\/span><\/li>\r\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Product development<\/span><\/li>\r\n<\/ul>\r\n<p><b>Step 2: define the value of each decision<\/b><\/p>\r\n<p><span style=\"font-weight: 400;\">How much is a better pricing decision worth? What does a bad hire cost? Quantify the economic impact of optimizing each decision.<\/span><\/p>\r\n<p><b>Step 3: design decision-to-action flows<\/b><\/p>\r\n<p><span style=\"font-weight: 400;\">For each critical decision, design a flow from data to specific action. Don't settle for insights; demand clear recommendations.<\/span><\/p>\r\n<p><b>Step 4: implement technology agnostics<\/b><\/p>\r\n<p><span style=\"font-weight: 400;\">Choose systems that can use generative AI for qualitative analysis, predictive AI for forecasting, and traditional analytics where appropriate. The best technology is the one that solves the problem, not the most advanced.<\/span><\/p>\r\n<p><b>The future is decision-centric<\/b><\/p>\r\n<p><span style=\"font-weight: 400;\">The companies that will lead the next decade will not be those with the most sophisticated models, but those that make the best decisions the fastest.<\/span><\/p>\r\n<p><span style=\"font-weight: 400;\">Decision-centric AI is not just a technology trend; it is a fundamental shift in how we think about the value of artificial intelligence in business. It's the shift from asking \"what can this AI do?\" to \"what decision do I need to make and how can AI help me make it better?\"<\/span><\/p>\r\n<p><span style=\"font-weight: 400;\">In a world where everyone has access to advanced AI models, competitive advantage will not come from the technology you use, but how well you use it to make better decisions.<\/span><\/p>\r\n<p><i><span style=\"font-weight: 400;\">Is your company optimizing models or optimizing decisions? The difference could determine who leads your industry in the coming years.<\/span><\/i><\/p>\r\n\r\n\r\n<div style=\"margin-top: 40px; padding-top: 20px;\">\r\n<p style=\"margin-bottom: 10px;\"><strong>Sobre el autor:<\/strong> Carlos Carrasco es CTO de Evolvis AI. Doctor en Sistemas Inteligentes por el Tecnol\u00f3gico de Monterrey, candidato a MBA, y ex investigador senior del Barcelona Supercomputing Center. Con m\u00e1s de 19 publicaciones cient\u00edficas, y proyectos de gran envergadura, su investigaci\u00f3n ha avanzado aplicaciones de IA en m\u00faltiples industrias.<\/p>\r\n<p style=\"margin-bottom: 10px;\">Cont\u00e1ctenos: <a href=\"mailto:info@evolvis.ai\">info@evolvis.ai<\/a><\/p>\r\n<\/div>","protected":false},"excerpt":{"rendered":"<p>En el vertiginoso mundo de la inteligencia artificial empresarial, existe una divisi\u00f3n fundamental que est\u00e1 redefiniendo c\u00f3mo las empresas aprovechan esta tecnolog\u00eda. No se trata de qu\u00e9 tan avanzado es tu modelo de IA, sino de qu\u00e9 tan bien te ayuda a tomar mejores decisiones. La trampa de la IA centrada en modelos La mayor\u00eda de las empresas caen en la misma trampa: se obsesionan con tener el modelo m\u00e1s sofisticado, el algoritmo m\u00e1s complejo, o la \u00faltima versi\u00f3n de GPT. Invierten millones en tecnolog\u00eda que promete revolucionar sus operaciones, solo para descubrir que sus equipos siguen luchando con las mismas preguntas b\u00e1sicas de siempre: \u00bfDebemos lanzar este producto? \u00bfCu\u00e1l es la mejor estrategia de precios? \u00bfD\u00f3nde deber\u00edamos invertir el pr\u00f3ximo trimestre? \u00bfC\u00f3mo podemos retener a nuestros mejores clientes? El problema no es la falta de datos o modelos sofisticados. El problema es que estas herramientas no est\u00e1n dise\u00f1adas para tomar decisiones. El cambio de paradigma: IA centrada en decisiones La IA centrada en decisiones representa un enfoque completamente diferente. En lugar de preguntarse \u00ab\u00bfqu\u00e9 modelo usar?\u00bb, se pregunta \u00ab\u00bfqu\u00e9 decisi\u00f3n necesito tomar?\u00bb. Caracter\u00edsticas clave de un sistema decision-centric: Orientaci\u00f3n a resultados Se enfoca en decisiones espec\u00edficas que impactan el negocio Combina m\u00faltiples tipos de IA seg\u00fan la necesidad (generativa, predictiva, anal\u00edtica) Prioriza la acci\u00f3n sobre la informaci\u00f3n Adaptabilidad inteligente No est\u00e1 limitado a un solo tipo de modelo Selecciona autom\u00e1ticamente la mejor tecnolog\u00eda para cada decisi\u00f3n Evoluciona con las necesidades del negocio Contexto empresarial Entiende los objetivos y restricciones de tu industria Considera factores como presupuesto, tiempo y recursos Proporciona recomendaciones accionables, no solo insights El impacto real en los negocios Caso de estudio: retail IA centrada en modelos: Una cadena de retail invierte $2M en un modelo de ML para predecir demanda. Obtienen predicciones precisas pero siguen sin saber cu\u00e1nto inventario comprar o cu\u00e1ndo hacerlo. IA centrada en decisiones: El mismo retailer implementa un sistema que no solo predice demanda, sino que recomienda decisiones espec\u00edficas: \u00abCompra 500 unidades del producto X para la tienda Y, ord\u00e9nalas el martes para evitar desabasto del fin de semana.\u00bb Resultado: 35% de reducci\u00f3n en inventario muerto y 28% de aumento en disponibilidad de productos. C\u00f3mo implementar IA centrada en decisiones Paso 1: mapea tus decisiones cr\u00edticas Antes de pensar en tecnolog\u00eda, identifica las 5-10 decisiones m\u00e1s importantes que tu empresa toma regularmente. Por ejemplo: Decisiones de pricing Asignaci\u00f3n de presupuesto de marketing Contrataci\u00f3n y retenci\u00f3n de talento Expansi\u00f3n geogr\u00e1fica Desarrollo de productos Paso 2: define el valor de cada decisi\u00f3n \u00bfCu\u00e1nto vale una mejor decisi\u00f3n de pricing? \u00bfQu\u00e9 cuesta una mala contrataci\u00f3n? Cuantifica el impacto econ\u00f3mico de optimizar cada decisi\u00f3n. Paso 3: dise\u00f1a flujos decision-to-action Para cada decisi\u00f3n cr\u00edtica, dise\u00f1a un flujo que vaya desde datos hasta acci\u00f3n espec\u00edfica. No te conformes con insights; exige recomendaciones claras. Paso 4: implementa tecnolog\u00eda agn\u00f3stica Elige sistemas que puedan usar IA generativa para an\u00e1lisis cualitativo, IA predictiva para forecasting, y an\u00e1lisis tradicional cuando sea apropiado. La mejor tecnolog\u00eda es la que resuelve el problema, no la m\u00e1s avanzada. El futuro es decision-centric Las empresas que lideren la pr\u00f3xima d\u00e9cada no ser\u00e1n aquellas con los modelos m\u00e1s sofisticados, sino aquellas que tomen las mejores decisiones m\u00e1s r\u00e1pido. La IA centrada en decisiones no es solo una tendencia tecnol\u00f3gica; es un cambio fundamental en c\u00f3mo pensamos sobre el valor de la inteligencia artificial en los negocios. Es el paso de preguntar \u00ab\u00bfqu\u00e9 puede hacer esta IA?\u00bb a \u00ab\u00bfqu\u00e9 decisi\u00f3n necesito tomar y c\u00f3mo me puede ayudar la IA a tomarla mejor?\u00bb En un mundo donde todos tienen acceso a modelos avanzados de IA, la ventaja competitiva no vendr\u00e1 de la tecnolog\u00eda que uses, sino de qu\u00e9 tan bien la uses para tomar mejores decisiones. \u00bfTu empresa est\u00e1 optimizando modelos u optimizando decisiones? La diferencia podr\u00eda determinar qui\u00e9n lidera tu industria en los pr\u00f3ximos a\u00f1os. Sobre el autor: Carlos Carrasco es CTO de Evolvis AI. Doctor en Sistemas Inteligentes por el Tecnol\u00f3gico de Monterrey, candidato a MBA, y ex investigador senior del Barcelona Supercomputing Center. Con m\u00e1s de 19 publicaciones cient\u00edficas, y proyectos de gran envergadura, su investigaci\u00f3n ha avanzado aplicaciones de IA en m\u00faltiples industrias. Cont\u00e1ctenos: info@evolvis.ai<\/p>","protected":false},"author":1,"featured_media":1853,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[],"class_list":["post-1","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-blog"],"_links":{"self":[{"href":"https:\/\/evolvis.ai\/en\/wp-json\/wp\/v2\/posts\/1","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/evolvis.ai\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/evolvis.ai\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/evolvis.ai\/en\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/evolvis.ai\/en\/wp-json\/wp\/v2\/comments?post=1"}],"version-history":[{"count":4,"href":"https:\/\/evolvis.ai\/en\/wp-json\/wp\/v2\/posts\/1\/revisions"}],"predecessor-version":[{"id":2897,"href":"https:\/\/evolvis.ai\/en\/wp-json\/wp\/v2\/posts\/1\/revisions\/2897"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/evolvis.ai\/en\/wp-json\/wp\/v2\/media\/1853"}],"wp:attachment":[{"href":"https:\/\/evolvis.ai\/en\/wp-json\/wp\/v2\/media?parent=1"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/evolvis.ai\/en\/wp-json\/wp\/v2\/categories?post=1"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/evolvis.ai\/en\/wp-json\/wp\/v2\/tags?post=1"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}