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Image: IEEE Spectrum
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6 Guidelines for Governing AI (opens in a new tab)

For the first 10 years of my career, I worked in product management and data analytics by myself. I wrote database queries that pulled numbers out of corporate systems, built statistical models to predict what customers would buy, and shipped data pipelines that moved informatio…

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Image: MIT Technology Review
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Connecting AI agents to enterprise knowledge (opens in a new tab)

For all the data that AI systems continually amass and analyze, enterprise AI agents often suffer from a curious shortcoming: a lack of knowledge. More than data, knowledge is the understanding of what the data means in the context of individual organizations. AI agents need thi…

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Bringing predictive analytics to the agentic AI era (opens in a new tab)

In 2026, the question for enterprise AI is no longer whether predictive models can outperform statistical forecasts—that argument is settled. The big question now is how to enable predictive systems to act on their own conclusions without drifting from business intent. The front…

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People really hate AI, so why can’t they get enough? (opens in a new tab)

Over the summer I talked to the CEO of Springboards, a startup building an LLM that’s designed to come up with a wider variety of responses than its mainstream rivals do. At the start of the call, he said something that’s been stuck in my head since: “We often say that we’re a s…

Read at MIT Technology Review (opens in a new tab)
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