What is it about?
Most companies measure the success of their AI products by looking at dashboards that show metrics going up after launch. But "revenue went up after launch" is not the same as "our AI caused revenue to go up." This article explains why traditional metrics fail, covering problems like selection bias, cannibalization, and Simpson's paradox. It then offers a practical framework built on causal inference, including randomized experiments, propensity score matching, difference-in-differences, and an emerging technique called target metric pre-balancing, to help teams answer the only question that matters: did this AI investment actually cause us to make more money than we spent?
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This page is a summary of: The Reality of an AI Implementation, Communications of the ACM, July 2026, ACM (Association for Computing Machinery),
DOI: 10.1145/3821443.
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