Data

This article critiques the common practice of exhaustive data cleaning before implementing AI, labeling it a consultant-driven "scam." Data cleaning is a never-ending and expensive process, delaying AI implementation while competitors move forward. Instead, I champion a "clean as you go" approach, emphasizing starting with a specific AI use case and cleaning data only as needed. Smart companies prioritize iterative improvement by using AI to fill in data gaps and building safeguards around imperfect data, ultimately achieving faster results. The core message is it’s more important to prioritize action over perfection, enabling quicker AI adoption and thereby competitive advantage.