Probabilistic forecasting for operators, not data scientists
Why ranges beat point estimates, and how planners learn to trust the numbers.
The short version
Operators don't need a single number — they need to know how wrong it might be, and what to do about it.
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Why it matters for the enterprise
Most AI initiatives stall at the last mile: integration, evaluation, and the operational controls that let a system run unattended. The gap between a convincing demo and a governed production deployment is where value is won or lost.
Treating evaluation as a release gate — rather than an afterthought — is what separates systems teams trust from ones they quietly switch off.
What to do next
Start by baselining a single high-volume workflow: its cost, cycle time, and error rate. That baseline turns 'AI strategy' into a measurable bet.
From there, scope the smallest deployment that can clear a real production bar, and instrument it so payback is provable from day one.
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Field notes on putting AI into governed production — for the operators and engineers who own it.
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