18/08/2026
8 out of 10 AI projects never make it to production. And the #1 reason isn't the budget. It isn't talent. It's the data.
Poor quality training data is cited as the leading cause of AI failure, responsible for 38% of projects that never deliver. You can have the best model architecture, the right team, and the budget to match. But if the data going in is incomplete, mislabeled, or unstructured, the model coming out won't work.
This is why data annotation isn't an afterthought in AI development, it's the foundation. The accuracy of every AI system traces back to the quality of the human work that came before it.
Building AI? Start with the data.
Source: Gartner - Top 10 Data and Analytics Technology Trends; IBM Global AI Adoption Index