Por Oscar Romero | Director de Ventas para México y el Norte de América Latina
La inteligencia artificial (IA) en las empresas es un tema de moda en el sector de tecnología especialmente considerando que es un boom dentro de las preferencias de los consumidores, sin embargo, la misma estrategia para el mercado de este sector no puede replicarse directamente en las empresas. Los consumidores impulsan sus expectativas al máximo en lo que respecta a la IA y las empresas deben prepararse cuidadosamente. De hecho, esto es lo que deben realizar las empresas cualquiera sea su tamaño: adoptar, entender y ver el valor del uso de la IA.
En el área del consumidor, los clientes están generalmente dispuestos a adoptar la nueva tecnología, y a tomar más riesgos. Sin embargo, en el área empresarial, hay demasiado en riesgo. Por ejemplo, no se puede confiar ciegamente en las soluciones de IA para que ejecuten totalmente o asistan en una tarea relacionada a la producción, ventas o distribución que sea multimillonaria. Los usuarios no pueden tomar ese riesgo, por lo tanto, se deben definir bien las tareas y objetivos, con parámetros específicos, además de ciclos constantes de feedback.
Esta no es una tarea fácil, especialmente cuando se refiere a los mismos temas que las soluciones empresariales deben enfrentar, como integraciones complejas, análisis de datos, mejoras, y más. Todas las empresas de software tendrán éxito al capitalizar y focalizarse en los siguientes temas:
Almacén de datos autosustentables
Unir soluciones para mantenerlas sincronizadas no es sencillo, especialmente en el caso de las soluciones en la nube, donde el departamento de TI tiene acceso limitado y se basa en el soporte. A medida que ingresamos en la era de las modificaciones, aunque están disminuyendo, el proceso de upgrades a las nuevas tecnologías puede ser una pesadilla, especialmente en el caso de la IA.
Esto puede ser más fácil si el acceso a los datos y a las herramientas mismas son parte de la solución, desde un comienzo. Todos sabemos que el valor de la IA es impulsado por la calidad de los datos. Colocar los servicios por separado no es suficiente, las mejores soluciones permiten la recepción de datos, esto ayuda a controlar los puntos complejos de integración para preparar, limpiar, y hasta actualizar los puntos de los datos. Los usuarios no deben ser responsables de este mantenimiento.
Modelo de seguridad integrado
La seguridad en la era de la IA empresarial es un gigante dormido. No todos los modelos empresariales pueden tener riesgos de seguridad adicionales. Actualmente, con las soluciones impulsadas por voz, este abre una forma totalmente nueva de amenazas de seguridad. Algunas soluciones pueden contar con controles de seguridad integrados, pero ¿cómo lo asociamos con la seguridad integral del negocio?
Quizás la respuesta es autenticar una base de usuarios por su voz, contar con un PIN con análisis facial, pero ¿cómo se hace efectivamente para reconocer el rol, asociación, grupos u algún otro protocolo de seguridad definido según el negocio? A menos que esto ya esté integrado dentro de la solución, existirán los mismos problemas de integración, solo que en este caso relativos a la seguridad. Las empresas que puedan resolver este dilema desde el comienzo serán exitosas.
Mecanismos constantes de feedback
Los ciclos de feedback (retroalimentación) son un tema indispensable para cualquier solución de IA. Sin un seguimiento adecuado de auditoria o de validación, los usuarios nunca confiarán plenamente en la tecnología y ni lograrán su verdadero potencial. Una plataforma de IA debe contar con validez visual de los datos y de su ejecutivo, acceso a la validación de la fuente de información y hasta de los datos sistemáticos para la toma de decisiones.
Las personas a veces luchan para describir sus propios parámetros de la toma de decisiones, y llevar esto a otro nivel con mayor capacitación lo hace aún más complejo. Los mecanismos de feedback, deben considerarse de la forma más creativa posible en todo entorno.
Herramienta de desarrollo ciudadano
Finalmente, la IA es un conocimiento adquirido. Confiamos en código de expertos y en científicos de datos para definir la experiencia, de la misma manera, en que la terminología de desarrollo ciudadano se trasformó en la clave para el desarrollo del software, la ciencia de los datos sigue esta misma tendencia. La matemática y el desarrollo de habilidades continuará siento una necesidad crítica, pero también se debe brindar una nueva capa de experiencia para capitalizar la oportunidad que genera la IA. Los usuarios no deben temer de experimentar con las funcionalidades debido al desarrollo de conocimiento limitado.
A medida que la tecnología continúa innovando, los consumidores utilizan lo que está actualmente disponible para explorar las opciones. No es necesario saber cómo y por qué, solo necesitan ver los resultados. Esto es lo que finalmente impulsa la revolución tecnológica, el desarrollo del conocimiento con nuevo pensamiento crucial es posible utilizando innovación previa. Spielberg puede no conocer como funciona una cámara filmadora, sim embargo puede ser el director de films sobresalientes utilizándola. La misma noción puede aplicarse a la IA, focalizarse en la experiencia, cómo se puede innovar, y no en cómo funciona la tecnología.
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Por Oscar Romero | Director de Ventas para México y el Norte de América Latina