Four stages that repeat
"The metrics that actually matter after launch" provides the measurement foundation. "Why an AI solution quietly gets worse: quality drift" shows how problems show up in it. "When a tool should be retired" provides the decision logic for the extreme case. Together they form four stages: measure, detect, decide, improve – and after the last stage, the first one starts again.
Why a loop, not a project close-out
"Why the launch is only the beginning" introduced the core idea behind this whole course: an AI solution isn't done after launch. Anyone who treats operation as a one-time task – measure once, check once, then tick it off – misses exactly the gradual changes this course has been about all along.
A fixed cadence instead of ad-hoc reaction
The loop only works with a fixed cadence – for example a monthly or quarterly session where the metrics from module 2 are systematically checked against the baseline. Without a fixed schedule, the loop degrades into ad-hoc reaction to complaints instead of catching problems early.
Who owns the loop
A loop without clear ownership fizzles out. Just as "Rolling out AI to your team" recommends clear ownership for the rollout itself, ongoing operation needs a clear owner too – a person or small team that keeps the schedule and actually makes the call on adjusting or retiring, instead of deferring it.
Why this matters to you as a decision-maker
The difference between an AI solution that's just as reliable a year in as it was on launch day, and one that quietly loses value, rarely comes down to the original technology choice. It comes down to whether this loop is actually lived – or only ever existed once, at launch.