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Measuring AI impact made clear: experiments, causal methods, and sanity checks to separate real improvement from coincidence.
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Behind every “AI success” headline lies a harder question - did it really work?
This article gives a structured look at how to measure what your system truly changes - starting with experiments, extending to causal methods, and ending with practical checks for trust.
A readable overview for those entering the space between data science and product impact - a space still few teams navigate well.