A Practical Guide to Measuring Business Impact in AI/ML Projects
Description
This story was originally published on HackerNoon at: https://hackernoon.com/a-practical-guide-to-measuring-business-impact-in-aiml-projects.
Measuring AI impact made clear: experiments, causal methods, and sanity checks to separate real improvement from coincidence.
Check more stories related to machine-learning at: https://hackernoon.com/c/machine-learning.
You can also check exclusive content about #ai, #ml, #performance-measurement, #product-management, #analytics, #causal-inference, #experimentation, #ai-business-impact, and more.
This story was written by: @vladyslav_chekryzhov. Learn more about this writer by checking @vladyslav_chekryzhov's about page,
and for more stories, please visit hackernoon.com.
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.
United States
