Most organizations assume their AI bottleneck is compute, so they buy more GPUs, expand storage, collect more data, and scale infrastructure.
But what if the real problem starts much earlier?
A surprising amount of AI and enterprise compute is consumed by unclear objectives, weak evidence, incompatible data, and projects that were never properly defined in the first place.
This article explores a different approach: increasing effective capacity by reducing waste before it enters the system. Define the outcome first, admit only the evidence that matters, and stop unnecessary complexity from consuming resources downstream.
If you're involved in AI, data strategy, interoperability, governance, or digital transformation, this perspective may challenge some common assumptions about what actually limits performance.
Read the full article at DataUniversa