AI doesn’t know much about golf. Or farming. Or mortgages. Or…

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Subscribe to enjoy similar stories. Earlier this year, the PGA Tour’s digital chief witnessed ChatGPT make the digital equivalent of a double bogey when the chatbot flubbed a question on basic golf lore: How many times has Tiger Woods won the Tour? Generative AI’s foundation models can be trained on vast troves of data from the internet and other sources, but still lack deep, specific knowledge even on topics as mainstream as golf, Scott Gutterman, the Tour’s senior vice president of digital and broadcast technology, realized. “There’s missing data, there’s generalized data.

Those things have just kind of led to generalized responses," Gutterman said. The PGA Tour is not alone. As AI projects creep from the pilot-project stage into operations, corporate users are discovering that many AI models are about as useful out of the box as a new employee entering orientation.

Companies are finding it is critical to augment today’s general models, like those offered by Anthropic or OpenAI, with more industry-specific or business-specific data if they’re going to be useful. (News Corp, owner of The Wall Street Journal, has a content-licensing partnership with OpenAI.) But that augmentation presents a spectrum of options, where higher levels of accuracy and reliability also bring more costs and complexity, said Ritu Jyoti, general manager and group vice president of AI and data as well as global AI lead at research firm International Data Corp. And the augmentation only works if companies have an impeccable handle on their data, which can be difficult, Jyoti said.