How Data Teams Measure AI ROI Without Guesswork

Most companies struggle to prove their artificial intelligence investments are actually working. In this episode of The Data Science Podcast with Fexingo, Lucas and Luna break down the specific framework top tech teams use to measure AI return on investment. They examine how a major logistics firm shifted from vague engagement metrics to tracking actual cost-per-decision saved, revealing why traditional KPIs fail in machine learning environments. The discussion covers the hidden costs of model maintenance, the importance of counterfactual analysis, and why measuring latency matters more than accuracy in production systems. If you are trying to justify your data science budget or understand where your algorithms are leaking value, this episode provides concrete steps to audit your own AI stack.

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