Data Quality Determines AI Project Success

This podcast covers a Forbes article by Gary Drenik, an expert in AI and consumer data, which addresses why the majority of artificial intelligence projects are unsuccessful. Drenik argues that the failure rate of up to 95% for AI projects, supported by reports from MIT, Gartner, and NIST, is not due to the technology itself but rather the poor quality and lack of representativeness in the data used for training models.

The podcast contrasts this common failure with the success of Exponential Technologies (XTech), whose forecasting models consistently outperform others by using high-quality, targeted datasets rather than large, generic ones.

Ultimately, the podcast concludes that data quality is the most critical factor separating successful AI innovations from failures.