{"id":3361,"date":"2026-10-22T06:59:07","date_gmt":"2026-10-22T11:59:07","guid":{"rendered":"https:\/\/accendoassociates.com\/?p=3361"},"modified":"2026-10-22T07:00:46","modified_gmt":"2026-10-22T12:00:46","slug":"lowering-the-cost-of-serving-clients-with-shared-ai-infrastructure","status":"publish","type":"post","link":"https:\/\/accendoassociates.com\/es\/lowering-the-cost-of-serving-clients-with-shared-ai-infrastructure\/","title":{"rendered":"Reducir los costos de servir a los clientes con una infraestructura compartida de IA"},"content":{"rendered":"

Los prestamistas, las aseguradoras y las empresas agr\u00edcolas del mismo pa\u00eds pagan por construir bases de inteligencia artificial similares. Construir las partes comunes de una vez les ayudar\u00eda a servir mejor a los agricultores familiares y a las peque\u00f1as empresas, a un costo m\u00e1s bajo, mientras que cada uno conservar\u00eda el control de sus clientes, sus datos y sus decisiones.<\/em><\/p>\n\n\n\n

\n

Primera parte de una serie de cuatro partes de Accendo Associates<\/a> y los MSC (MicroSave Consulting)<\/a>, ambos cofundadores de la Alianza de IA Inclusiva<\/a>.<\/p>\n<\/blockquote>\n\n\n\n

Tomemos a Malti, una vendedora de verduras en un pueblo del sur de la India. Ella necesita un peque\u00f1o pr\u00e9stamo para comprar productos antes de la temporada de mayor actividad. Lleva sus cuentas en un cuaderno, recibe su pago principalmente en efectivo y habla un idioma que los sistemas de su prestamista no pueden leer. Las visitas a campo para atenderle suponen costos que el inter\u00e9s del pr\u00e9stamo no puede cubrir, por lo que se le rechaza o se le ofrece un cr\u00e9dito con condiciones m\u00e1s estrictas.<\/p>\n\n\n\n

Un granjero lechero en Kenia, un productor de caf\u00e9 en Colombia y un propietario de una tienda de ropa en Filipinas enfrentan la misma barrera. La inteligencia artificial (IA) puede reducirla al leer un libro mayor manuscrito a partir de una fotograf\u00eda, responder en idiomas locales y ofrecer al prestamista una visi\u00f3n m\u00e1s clara del riesgo. Para hacerlo bien, es necesario que cuatro tipos de IA trabajen juntos; ning\u00fan proveedor individual ofrece todos estos servicios. Propomos construir esa base de manera abierta y buscar socios para financiarla.<\/p>\n\n\n\n

La IA puede ayudar a los clientes a operar, comprar y vender de forma m\u00e1s eficiente.<\/h4>\n\n\n\n

Solo 56% adultos en pa\u00edses de ingresos bajos y medios pueden recaudar fondos de emergencia en un plazo de 30 d\u00edas, seg\u00fan la Global Findex 2025<\/a>, y las peque\u00f1as empresas en mercados emergentes enfrentan una Gasto crediticio de 5,7 billones de d\u00f3lares<\/a> (Estimaci\u00f3n de SME Finance Forum y IFC, 2019). M\u00e1s all\u00e1 de la decisi\u00f3n de otorgar un pr\u00e9stamo, la inteligencia artificial puede ayudar a estas empresas a operar de forma m\u00e1s eficiente, comprar mejor y vender mejor al encontrar proveedores m\u00e1s baratos, un incentivo gubernamental que pueden obtener y nuevos compradores. La prueba de cada herramienta es si la salud financiera, los ingresos y la resiliencia del cliente mejoran.<\/p>\n\n\n\n

Cuatro capacidades de IA deben trabajar juntas para servir bien a los clientes.<\/h4>\n\n\n\n
    \n
  1. Racionalizaci\u00f3n general para tomar decisiones informadas:<\/strong> El cliente recibe asesoramiento sobre c\u00f3mo administrar su negocio y respuestas claras a sus preguntas. El personal del proveedor procesa las solicitudes y obtiene respuestas en cuesti\u00f3n de segundos.<\/li>\n\n\n\n
  2. Idiomas locales y documentos para superar las barreras ling\u00fc\u00edsticas:<\/strong> El cliente recibe asesoramiento en su propio idioma, por texto o por voz, incluso en un tel\u00e9fono b\u00e1sico con un d\u00e9bil se\u00f1al. El proveedor lee un formulario manuscrito sin tener que volver a escribirlo.<\/li>\n\n\n\n
  3. An\u00e1lisis de dominios, para obtener informaci\u00f3n pr\u00e1ctica:<\/strong> Los modelos m\u00e1s peque\u00f1os y espec\u00edficos del sector analizan cadenas de suministro, precios de mercado o lluvias y suelo. El cliente aprende qu\u00e9 cosechar, cu\u00e1ndo y d\u00f3nde vender. El proveedor ve el riesgo de forma m\u00e1s clara.<\/li>\n\n\n\n
  4. Decisiones verificables, para la transparencia y la rendici\u00f3n de cuentas<\/strong>El cliente recibe una raz\u00f3n que pueda entender y una forma de impugnar una decisi\u00f3n que considera err\u00f3nea. El proveedor mantiene un registro que su supervisor puede revisar.<\/li>\n<\/ol>\n\n\n\n

    Ning\u00fan proveedor individual ofrece todos los cuatro. Las herramientas de razonamiento m\u00e1s potentes se alquilan de empresas globales, se ejecutan fuera de la mayor\u00eda de estos pa\u00edses y funcionan mejor en ingl\u00e9s. Las herramientas en idiomas locales est\u00e1n mejorando, con esfuerzos regionales como Sarvam<\/a> En la India, SEA-LION<\/a> en todo el Sudeste Asi\u00e1tico, y Latam-GPT<\/a> Desde Chile. Sin embargo, como muestra el trabajo de campo de MSC, leer la escritura manuscrita en un alfabeto local o responder por voz en la mayor\u00eda de los idiomas africanos a\u00fan no es confiable. Los modelos espec\u00edficos del dominio o los modelos \u201cde peque\u00f1a inteligencia artificial\u201d construidos para un prop\u00f3sito espec\u00edfico necesitan datos locales. Las decisiones verificables requieren que el proveedor conserve la evidencia, las reglas y las versiones del modelo detr\u00e1s de una decisi\u00f3n, para que un cliente o un regulador pueda entender c\u00f3mo se lleg\u00f3 a ella.<\/p>\n\n\n\n

    Pocos proveedores cuentan con el personal necesario para administrar y mantener estos sistemas internamente.<\/p>\n\n\n\n

    Una capa de confianza permite a un proveedor mezclar proveedores y mantener el control.<\/h4>\n\n\n\n

    Nuestro dise\u00f1o coloca una capa de Enrutamiento y Control de IA entre el proveedor y cada sistema de IA que utiliza. Cada proveedor ejecuta su propio sistema. Imagine esto como un tablero de interruptores con un manual de reglas. Cada llamada procedente del personal, de los sistemas y de las aplicaciones pasa a trav\u00e9s de \u00e9l, al igual que cada solicitud procedente de un tel\u00e9fono que el dispositivo no puede manejar. Para cada solicitud, la capa decide dos cosas: qu\u00e9 puede ver el sistema de IA y qu\u00e9 puede hacer.<\/p>\n\n\n\n

    Cuando un modelo necesita contexto, como el historial de un cliente, la pol\u00edtica de cr\u00e9dito del proveedor o un formulario escaneado, la capa lo recupera bajo las autorizaciones del proveedor. Los datos identificados del cliente solo se env\u00edan a los modelos que se ejecutan en el pa\u00eds, y los identificadores se eliminan antes de que cualquier informaci\u00f3n llegue al extranjero. Cuando un modelo necesita actuar, como verificar el saldo o consultar la informaci\u00f3n de la agencia de cr\u00e9dito, lo hace a trav\u00e9s de la capa, nunca directamente. El proveedor escribe las reglas de forma que pueda leerlo su supervisor, y la capa registra cada pregunta, respuesta y acci\u00f3n.<\/p>\n\n\n\n

    Los modelos utilizados para las decisiones empresariales, como la calificaci\u00f3n crediticia, permanecen dentro del entorno del proveedor y alimentan sus propios motores de toma de decisiones. En los tel\u00e9fonos de los agentes y los clientes, el trabajo se ejecuta en el dispositivo donde puede hacerlo, incluso sin conexi\u00f3n a Internet, como ya hace la plataforma x-VOX de MSC. Solo lo que el dispositivo no puede hacer llega a esta capa.<\/p>\n\n\n\n

    \"\"<\/figure>\n\n\n\n

    Debido a que cada llamada pasa por un solo lugar, el proveedor puede cambiar de proveedores a medida que la tecnolog\u00eda, los precios y las regulaciones cambian, siempre que la licencia del nuevo sistema permita el uso comercial, lo que algunos modelos abiertos, como InkubaLM<\/a>, No lo hagas. Aunque construir este dise\u00f1o es una cuesti\u00f3n sencilla, ponerlo en funcionamiento es algo en lo que muchos proveedores tendr\u00e1n dificultades.<\/p>\n\n\n\n

    Construya las partes compartidas una vez.<\/h4>\n\n\n\n

    Tomemos como ejemplo a 12 proveedores de servicios financieros en un solo pa\u00eds. Comparten un supervisor, un sistema de identidad, una red de pagos y un conjunto de idiomas, pero cada uno construye las mismas conexiones, herramientas de idiomas y pruebas por s\u00ed solo. Cada uno a\u00fan financia lo que compite con su propio balance de capital, y sin evidencia de retorno de los dem\u00e1s, la mayor\u00eda de los consejos de administraci\u00f3n esperan. Poco capital externo llena el vac\u00edo. A partir de septiembre de 2026, la inclusi\u00f3n financiera y la agricultura recibieron 445 millones de d\u00f3lares en 12 pa\u00edses, de los 90 mil millones de d\u00f3lares en capital de IA monitorizados por la Mapa de Accendo Capital Flows<\/a> en los mercados emergentes. Es la mitad de un centavo por cada d\u00f3lar, y casi todo se lo llevaron las empresas individuales.<\/p>\n\n\n\n

    Propomos construir un espacio com\u00fan abierto, gestionado por una organizaci\u00f3n sin fines de lucro, que publiquen tres cosas: un dise\u00f1o abierto para la capa, herramientas en idiomas locales y modelos espec\u00edficos del dominio con pesos abiertos que cualquiera pueda inspeccionar. Cada proveedor los implementa localmente en sus propios sistemas, bajo sus propias reglas, y los adapta a su propio mercado. Los proveedores obtienen menores costos, resultados comparables antes de comprometerse, y est\u00e1ndares comunes que les permiten cambiar de proveedor sin que ello afecte a su responsabilidad ante su supervisor.<\/p>\n\n\n\n

    Las subvenciones construir\u00edan los servicios comunes, y las cuotas de los miembros los mantendr\u00edan. Buscamos socios de financiaci\u00f3n que apoyen la infraestructura compartida y queremos escuchar a proveedores, vendedores y supervisores que vean el problema de manera diferente.<\/p>\n\n\n\n

    Malti, la vendedora de verduras, nunca ver\u00e1 la capa de encaminamiento y control de inteligencia artificial. Ver\u00e1 a un prestamista que puede leer su cuaderno, responderle en su propio idioma y ofrecer t\u00e9rminos que sean adecuados para su negocio. Una base com\u00fan hace que esto sea accesible para todos los proveedores de su pa\u00eds.<\/p>\n\n\n\n

    Este es el primer de cuatro cap\u00edtulos. El segundo cap\u00edtulo profundiza en la arquitectura: el jard\u00edn vallado y con acceso restringido que establece qu\u00e9 propiedad tiene un proveedor y qu\u00e9 alquila, c\u00f3mo funciona la capa en los dispositivos y c\u00f3mo se gobierna y financia el bien com\u00fan. El tercer cap\u00edtulo analiza los riesgos, incluidos los modelos compartidos que fallan juntos, y c\u00f3mo el dise\u00f1o responde a ellos. El cuarto cap\u00edtulo plantea preguntas espec\u00edficas a los financiadores, supervisores y proveedores, as\u00ed como a un primer grupo de proveedores en un solo pa\u00eds.<\/p>\n\n\n\n

    Seguir\u00e1 llegando m\u00e1s informaci\u00f3n.<\/p>\n\n\n\n

    <\/p>\n\n\n\n

    \n\n\n\n

    <\/a><\/p>","protected":false},"excerpt":{"rendered":"

    Una infraestructura compartida de IA, un AI Commons, podr\u00eda ayudar a los prestamistas, las aseguradoras y las empresas agr\u00edcolas a reducir los costos y a servir mejor a las peque\u00f1as empresas y a los agricultores.<\/p>","protected":false},"author":2,"featured_media":3382,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"nf_dc_page":"","_kad_blocks_custom_css":"","_kad_blocks_head_custom_js":"","_kad_blocks_body_custom_js":"","_kad_blocks_footer_custom_js":"","_EventAllDay":false,"_EventTimezone":"","_EventStartDate":"","_EventEndDate":"","_EventStartDateUTC":"","_EventEndDateUTC":"","_EventShowMap":false,"_EventShowMapLink":false,"_EventURL":"","_EventCost":"","_EventCostDescription":"","_EventCurrencySymbol":"","_EventCurrencyCode":"","_EventCurrencyPosition":"","_EventDateTimeSeparator":"","_EventTimeRangeSeparator":"","_EventOrganizerID":[],"_EventVenueID":[],"_OrganizerEmail":"","_OrganizerPhone":"","_OrganizerWebsite":"","_VenueAddress":"","_VenueCity":"","_VenueCountry":"","_VenueProvince":"","_VenueState":"","_VenueZip":"","_VenuePhone":"","_VenueURL":"","_VenueStateProvince":"","_VenueLat":"","_VenueLng":"","_VenueShowMap":false,"_VenueShowMapLink":false,"_kadence_starter_templates_imported_post":false,"_swpsp_post_exclude":false,"_kad_post_transparent":"","_kad_post_title":"","_kad_post_layout":"","_kad_post_sidebar_id":"","_kad_post_content_style":"","_kad_post_vertical_padding":"","_kad_post_feature":"","_kad_post_feature_position":"","_kad_post_header":false,"_kad_post_footer":false,"_kad_post_classname":"","nfd_image_url":"","nfd_onboarding_generated":"","footnotes":""},"categories":[36],"tags":[59,67,64,68,66],"class_list":["post-3361","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-insights","tag-artificial-intelligence","tag-local-llms","tag-responsible-ai","tag-shared-ai","tag-small-ai"],"yoast_head":"\nLowering 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