Synthetic data fill AI training gaps, but can’t replace real-world nuance, experts say

As AI adoption accelerates across industries, synthetic data are emerging as a powerful tool for training models, especially when real-world data are scarce or sensitive. This type of data can be a privacy-friendly, cost-effective substitute. While it can mimic patterns, it can not, however, fully capture the messy, unpredictable edge cases of real life, especially…