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    Databricks CRO names enterprise AI’s four C’s: context, control, choice and cost

    Ron Gabrisko says cloud choice matters alongside model choice, warns that lock-in can become expensive, and calls for controls on how AI agents access and disclose company data.

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    TLDR

    Databricks chief revenue officer Ron Gabrisko outlines four priorities for enterprise AI: context, control, choice and cost. He says AI needs company data to inform decisions and predictions, with governance over data access and how agents use it. Choice covers both models and cloud providers: he argues that frontier models are great for complex tasks, while open-source models handle much of the rest. On cost, he warns that lock-in can drive up spending and says CIOs are blowing through their budgets.

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    3 Sources, first seen 19d ago

    Combined views

    8.1K

    3 Sources, first seen 19d ago

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    19d ago
    first seen 19d ago
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    7 comments
    9 saves
    6 reposts

    Sentiment

    Positive——Negative

    Summary

    Not enough discussion yet.

    No sentiment analysis available yet.

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    3 Sources

    @TurnerNovak.@databricks CRO Ron Gabrisko on the four C's of enterprise AI: context, control, choice, cost: "I've talked to thousands of customers, and I call it the four C's of what matters for these companies. The first is context. How do I attach AI to my data? It needs the context of my data to be smart about the decisions and predictions I want to make. The second is control. I need it governed. I can't have everyone with access to all the data, especially in a world of models and agents. I need to know how these agents will use it, and make sure they're not disclosing it. Third is choice. Everyone's focused on model choice, but cloud choice matters too. We're open source, so you can plug anything in, and we serve all the models. Frontier models are great for the complex tasks. Open source models handle a lot of the rest. Because if you get locked into one, you end up spending a fortune. Which is the last one: cost. Costs have been through the roof. CIOs are blowing through their budgets. So how do you put the right controls and guardrails on it?"
    @ThePeelPod.@databricks CRO on the real reason enterprises still struggle with AI adoption: "Everybody has an FDE model now, but they need help wiring it all together. It's not simple. The biggest challenge is the data. Everybody knows the data is the key to these enterprise use cases. But in a lot of cases, the data isn't in the right place. It's all over the place, in legacy systems, old formats, proprietary formats. Getting your data into a good place to attach AI is a tough, complicated problem that they're using Databricks for."

    3 Sources

    @TurnerNovak.@databricks CRO Ron Gabrisko on the four C's of enterprise AI: context, control, choice, cost: "I've talked to thousands of customers, and I call it the four C's of what matters for these companies. The first is context. How do I attach AI to my data? It needs the context of my data to be smart about the decisions and predictions I want to make. The second is control. I need it governed. I can't have everyone with access to all the data, especially in a world of models and agents. I need to know how these agents will use it, and make sure they're not disclosing it. Third is choice. Everyone's focused on model choice, but cloud choice matters too. We're open source, so you can plug anything in, and we serve all the models. Frontier models are great for the complex tasks. Open source models handle a lot of the rest. Because if you get locked into one, you end up spending a fortune. Which is the last one: cost. Costs have been through the roof. CIOs are blowing through their budgets. So how do you put the right controls and guardrails on it?"
    @ThePeelPod.@databricks CRO on the real reason enterprises still struggle with AI adoption: "Everybody has an FDE model now, but they need help wiring it all together. It's not simple. The biggest challenge is the data. Everybody knows the data is the key to these enterprise use cases. But in a lot of cases, the data isn't in the right place. It's all over the place, in legacy systems, old formats, proprietary formats. Getting your data into a good place to attach AI is a tough, complicated problem that they're using Databricks for."