Artificial intelligence pitfalls and how to avoid them

Abstract

Artificial intelligence (AI) is a mainstream technology and has become a cornerstone in digital transformation initiatives. Founded on sound academic principles, why do so many projects fail? Getting AI into business pipelines is a relatively new discipline that faces several of the same challenges as traditional software development and many that are specific to the discipline. This paper aims to highlight the common pitfalls that hamper AI projects, from initiatives that are interesting science projects to abstract dreams. Putting the proper controls in place will drive success while achieving the fine balance between killing innovation with too much governance or creating the Wild West with too little. There are many foundations needed to help mitigate many of the common pitfalls and are significant contributors to getting AI over the line and into production in a way that creates market differentiation.