During the Model Evaluation Go/No-Go meeting for the demand-forecasting model, data scientists present precision, recall, and mean absolute percentage error figures to a room of route-planning and finance executives who are unfamiliar with those terms. What is the AI project manager's most valuable role in this meeting?

CPMAI positions the AI project manager as the bridge between technical work and business decision-making, and nowhere is that more visible than in a Go/No-Go meeting where technical evaluation metrics meet non-technical decision-makers. The PM's most valuable contribution here is translating precision, recall, and error-rate figures into what they actually mean for the business decision at hand — for instance, what a given demand-forecast error rate implies for over- or under-scheduling capacity and the dollar impact of getting it wrong — so route-planning and finance leaders can make an informed Go/No-Go judgment without needing a statistics background. Staying silent abandons this responsibility and risks a decision made on misunderstood or ignored metrics. Overruling the data scientists' figures with an independent recalculation oversteps the PM's role — the PM's job is to interpret and contextualize technical findings for stakeholders, not to substitute their own competing technical analysis for the data science team's work. Postponing the meeting until every executive is statistically literate is impractical and unnecessary; the entire reason a skilled translator role exists in the room is precisely so that expertise gap doesn't have to be closed by training. The exam point: bridging technical results and business stakeholders is a named, core PM responsibility throughout CPMAI, and it shows up concretely at evaluation and Go/No-Go moments.