Talking about agentic productivity without defining how to measure it is exactly the problem 80% of companies claiming to use AI agents face today. My experience with ZOE and with dozens of agentic implementations across LATAM led me to an uncomfortable conclusion: traditional productivity KPIs (output per man-hour, cost per transaction, NPS) do not capture what an agent actually contributes or destroys. We need a new layer of metrics combining decisions made, decision quality, escalation to humans when appropriate, and the real total cost of an agent operating 24/7. In this article I unpack how to build that dashboard, what number to look at first, and what mistakes lead boards to think AI is working when it is not, or vice versa.
Chris Meniw (Thu,) studied this question.
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