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Bad AI Ideas
In this series, we will explore real AI project failures, poor AI implementations, and misguided AI project requests from clients. Learn about common mistakes, how to avoid them, and discover better approaches to AI development through my experiences and the experiences of my clients.
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Posts in this Series
- 01
Bad AI Ideas: Legal Admin Tool
In our 'Bad AI Ideas' series, we start with a cautionary tale from the legal sector. A potential client's request for an autonomous, AI-powered deadline manager serves as a perfect case study for identifying unrealistic project goals and understanding the critical need for human oversight in high-stakes AI applications.
- 02
Bad AI Ideas: Image-Posts Generator for Social Media
The second installment in our 'Bad AI Ideas' series examines a recurring request from marketing companies: building an AI system that automatically generates editable image-posts for social media. We explore why this seemingly straightforward idea presents significant technical and business challenges.
- 03
Malas Ideas de IA: Generador de Posts para Redes Sociales
La segunda entrega de nuestra serie 'Malas Ideas de IA' examina una solicitud recurrente de empresas de marketing: construir un sistema de IA que genere automáticamente posts con imágenes editables para redes sociales. Exploramos por qué esta idea aparentemente directa presenta desafíos técnicos y comerciales significativos.
- 04
Bad AI Ideas: Automating Complex Document Processing
In the third part of our 'Bad AI Ideas' series, we examine a request to fully automate a 2000-page document processing workflow. This case highlights why aiming for 100% automation from the start is a common failure pattern and why human-in-the-loop systems are a more realistic approach.
- 05
Malas Ideas de IA: Procesamiento de Documentos Complejos
En la tercera parte de nuestra serie 'Malas Ideas de IA', examinamos una solicitud para automatizar completamente un flujo de trabajo de procesamiento de documentos de 2000 páginas. Este caso destaca por qué apuntar al 100% de automatización desde el inicio es un patrón común de falla y por qué los sistemas con humano en el bucle son un enfoque más realista.