[insight]
How to set a target metric, a control, and a kill criterion so an AI initiative can be judged in the language of the business.
Every AI initiative has two scoreboards. The first is technical: accuracy, recall, latency, cost per token. The second is commercial: volume, margin, cost to serve, cycle time, risk events. Only the second one is read by the people who fund the next phase.
A single metric the business already reports, owned by a named person, is worth more than a balanced scorecard of ten. It forces a decision about what the initiative is really for and gives a clear answer to whether it worked.
Uplift claimed without a baseline is an adjective. Where possible, run a randomised control: a segment, a region, or a set of agents that continues with the existing process. Where randomisation is not possible, use a matched baseline or a pre-period with seasonality adjusted.
A kill criterion is the result at which the team stops. Agreeing it before work starts removes the sunk-cost argument later and makes it safe to test bolder ideas, because a cheap, fast no is a legitimate outcome.
Translate every result into the units the business uses. A churn model is reported as retained revenue, a pricing model as margin, a document agent as hours returned and cycle time reduced. Model metrics go in the appendix.
Programmes run this way are smaller, faster, and stop earlier when they are wrong. They also compound, because each proven outcome funds the next and the organisation builds a track record it can point to.
Tell us about the decision or workflow you want to change. We'll come back with an honest view on whether it's worth proving, and what it would take.
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