Existen problemas operativos relacionados con la naturaleza de la tecnología y la Inteligencia Artificial. Existen tres escenarios en donde uno realmente puede implementar un sistema de IA predictivo. El primero es un sistema de control de bucle cerrado que controla el ambiente. Por ejemplo, un sistema que controla de forma inteligente el aire acondicionado de un edificio para mantener una temperatura óptima mientras minimiza la energía utilizada. Este sistema sabe cuándo existe un error, ya que su objetivo es mantener la temperatura en cierto rango mientras trata de reducir la energía que consume. En pocas palabras, sabe cuándo está mal y se puede autocorregir.
De forma similar, un sistema para mejorar una oferta podría implementarse para maximizar el número de conversiones otorgadas a un inventario de descuentos y ofertas previas. Esto se convertiría en una implementación de IA que se puede optimizar automáticamente utilizando diferentes técnicas como el “aprendizaje reforzado”. Si la empresa es susceptible a la experimentación comercial y lleva a cabo implementaciones de campo, dichos sistemas pueden trabajar bien y mejorar con el paso del tiempo.
El problema es que no muchos escenarios se prestan tan fácilmente para las soluciones de control de bucle cerrado. Comúnmente contamos con sistemas de IA de control de bucle abierto. Por ejemplo, consideren un sistema de preguntas y respuestas que le permita a los usuarios darles retroalimentación cuando piensen que su respuesta es incorrecta, al igual que un bucle cerrado. En este caso, necesitamos poner en práctica un flujo de trabajo humano para descifrar cuál sería la respuesta correcta para cada tipo de retroalimentación negativa, la cual posteriormente se puede utilizar para crear mejores datos de capacitación y mejorar el sistema con el paso del tiempo.
Esto requiere un cambio en la manera de pensar organizacional, es decir que la naturaleza de una implementación de IA necesita un soporte continuo para la recapacitación al ofrecer los marcadores adecuados. Las empresas tradicionales realmente no comprenden o aprecian el hecho de que dichos flujos de trabajo sean necesarios para implementar de forma exitosa y eficiente la mayoría de los sistemas IA.
Necesidad de protocolos de prueba
Para analizar y validar el trabajo del sistema, uno tendría que emplear verificadores, lo cual es igual de bueno que realizar la conversión de forma manual. Por otro lado, si únicamente se realizaran pruebas rápidas, podrían ocurrir errores costosos que incrementarían por siempre.
Uno realmente necesita seguir el protocolo calibrado; por ejemplo, tomar un recurso pequeño, pero de tamaño razonable y verificar manualmente que el sistema lo esté haciendo bien y, de no ser así, proporcionarle los datos correctos de capacitación para que lo pueda hacer mejor. Así que, usted incurre en este costo mientras prepara al sistema. Después, usted comienza a implementarlo en campo y a realizar pruebas rápidas inicialmente por un alto porcentaje del resultado.
Gradualmente, conforme uno confía más, las pruebas pueden reducirse en términos de porcentaje, mientras que el volumen de los datos que uno procesa es más y más grande. La idea de que usted necesita un protocolo de capacitación para implementar un sistema técnico también es nueva tanto para el área de TI de la empresa como para el negocio, y esto es algo que debe comprenderse.
Por lo tanto, existen varios motivos, algunos culturales, algunos tecnológicos y otros operativos que hasta ahora han evitado la implementación generalizada de IA en las empresas tradicionales; esto en comparación con la economía nueva, donde todo está en línea y fácil de procesar.
En un mundo donde la agilidad y la anticipación son clave, los gemelos digitales de la cadena de suministro se están convirtiendo en herramientas indispensables para empresas líderes que buscan respuestas rápidas y efectivas ante la incertidumbre en evolución.
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