Those spiffy AI systems that tech companies keep promising require mountains of training data, but high-quality sources may have already run out—unless enterprises can unlock the information trapped behind their firewalls, according to Goldman Sachs

Training data is the Achilles heel of massive new AI models, as detailed by George Lee, co-head of the Goldman Sachs Global Institute, in a recent webcast on data's role in AI.

"The quality of the outputs from these models, particularly in enterprise settings, is highly dependent on the quality of the data that you're sourcing and referencing," Lee said.

The problem is finding enough quality data, according to Neema Raphael, Goldman Sachs' chief data officer and head of data engineering. Some developers may be resorting to synthetic data or training models on the output of existing AI systems.

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