Why your data infrastructure — not your AI model — will determine whether Agentic AI scales
While significant investment is being directed toward data center infrastructure to support AI compute needs, the scalability of agentic AI systems ultimately hinges on robust data infrastructure. Agentic AI requires seamless access to accurate, integrated data across disparate enterprise systems, a challenge that current pilot programs often obscure. Most enterprises struggle to scale AI beyond experimentation due to fragmented data environments, not limitations in AI models themselves.
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Nearly all the business media coverage of AI focuses on the eye-popping sums being deployed into data center infrastructure that drives the “compute” coveted by leaders in the AI industry. That “compute” provides the raw processing power required to train, build, and run AI systems. Think of it as the engine behind the technology. The tech community is expected to invest more than $750 billion into data centers this year alone. Estimates for total cumulative spend on the humming warehouses reach over $7 trillion by 2030.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at Fortune.
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