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Artwork for Machine Learning Tech Brief By HackerNoon
Machine Learning Tech Brief By HackerNoon · October 5 · 8 min

AI Is Now an Enterprise Resource. So Why Are We Still Managing It Like Software?

This story was originally published on HackerNoon at: https://hackernoon.com/ai-is-now-an-enterprise-resource-so-why-are-we-still-managing-it-like-software. AI costs aren't the real problem. Visibility is. Why cheaper tokens won't save you, and how to govern AI by outcomes, not licenses. Check more stories related to machine-learning at: https://hackernoon.com/c/machine-learning. You can also check exclusive content about #artificial-intelligence, #artificial-intelligence-trends, #ai, #enterprise-ai, #ai-costs, #ai-strategy, #digital-transformation, #management-discipline, and more. This story was written by: @irynashymko. Learn more about this writer by checking @irynashymko's about page, and for more stories, please visit hackernoon.com. Most companies track AI as a software line item. But AI is increasingly doing the work, not just supporting it, and that means the real question isn't "how much are we spending on AI?" but "what outcomes is it producing, and who owns them?"

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This story was originally published on HackerNoon at: https://hackernoon.com/ai-is-now-an-enterprise-resource-so-why-are-we-still-managing-it-like-software.
AI costs aren't the real problem. Visibility is. Why cheaper tokens won't save you, and how to govern AI by outcomes, not licenses.
Check more stories related to machine-learning at: https://hackernoon.com/c/machine-learning. You can also check exclusive content about #artificial-intelligence, #artificial-intelligence-trends, #ai, #enterprise-ai, #ai-costs, #ai-strategy, #digital-transformation, #management-discipline, and more.

This story was written by: @irynashymko. Learn more about this writer by checking @irynashymko's about page, and for more stories, please visit hackernoon.com.

Most companies track AI as a software line item. But AI is increasingly doing the work, not just supporting it, and that means the real question isn't "how much are we spending on AI?" but "what outcomes is it producing, and who owns them?"

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