The other is having the courage to dedicate genuine resources to innovation. At the large corporate level, often the thing that makes the most sense is to have the innovation be developed at some kind of entity that is firewalled off from the rest of the organization. But that requires resources, so dedicating those resources and then truly granting autonomy to those kinds of spinoffs continues to be a challenge. BROADER ACCESS TO DATA AI and machine learning—they’ve been around for decades. The change now is the development of large language models and the natural language processing technologies. It’s about giving broader accessibility to data. Whatever your corporate data is, it was all structured data. You’re limited to only that structured data information and the people who have access to it are the data scientists. But that meant that the people who genuinely understood the insightful questions you could make of that data weren’t the data scientists. They’re the businesspeople, the ones who are out front interacting with customers and making sales, solving business problems and handling the money.
the business and the people who manage the structured data. The advent of large language models and natural language processing enables the air gap to be closed because the people who understand the business challenges can now query the data using their own words. That’s the first big development. “The change now is the development of large language models and the natural language processing technologies. It’s about giving broader accessibility to data.”
The second big development is that the data that’s held is shifting from being not only structured data, but also unstructured data
In every corporation, we have this unintentional air gap between the people who understand
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