Customizing generative AI for unique value
We surveyed 300 technology leaders in mostly large organizations in different industries to learn how they are seeking to leverage these opportunities. We also spoke in-depth with a handful of such leaders. They are all customizing generative AI models and applications, and they shared with us their motivations for doing so, the methods and tools they’re using, the difficulties they’re encountering, and the actions they’re taking to surmount them.
Our analysis finds that companies are moving ahead ambitiously with customization. They are cognizant of its risks, particularly those revolving around data security, but are employing advanced methods and tools, such as retrieval-augmented generation (RAG), to realize their desired customization gains.
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This content was produced by Insights, the custom content arm of MIT Technology Review. It was not written by MIT Technology Review’s editorial staff.
