Successful AI implementation depends heavily on a robust infrastructure that includes data architecture, computing power, and governance. Organizations need to ensure that these components are scalable and can evolve with the demands of advancing algorithms. Without a clear vision and defined use cases, many AI projects fail, despite significant investments. Organizations should focus on building an infrastructure that supports both current and future AI needs, adapting to increasing model sizes, latency, privacy requirements, and the need for governance.
"80 percent of all AI projects fail not because the technology is inadequate, but because you don't determine where you want to go beforehand."
"Sustainable AI is only possible if the underlying infrastructure-from data architecture to GPU capacity-grows with it. That requires more than just technical upgrades."
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