The cost
Scaling a weak pilot multiplies review load, exceptions, and disappointment.
The result is not another validation report. It is a scale decision leadership can defend: scale, narrow, redesign, keep measuring, or stop.
Leadership needs evidence the pilot improved the outcome that matters, under real conditions, at economics the organization can support.
Scaling a weak pilot multiplies review load, exceptions, and disappointment.
A clear scale decision based on live performance, not slides.
We evaluate the pilot on technical results, workflow fit, human workload, adoption, outcomes, economics, and scale readiness.
Weak evidence looks strong until you try to scale it.
We look at how the pilot behaves in the operating environment.
FDA/CDRH highlights how changes in populations, sites, protocols, and inputs can alter AI performance after development. An average pilot result can look acceptable while hiding conditions where benefit, burden, or failure meaningfully changes.
Should the organization scale on the average result, or based on where the evidence shows the AI works under the operating conditions it will face? The scale question is therefore larger than "Did the pilot work?" It also includes where it worked, under what conditions, and what evidence supports broader responsibility.
Commercial terms are confirmed on the fit call.
Pilot Efficacy decides whether the initiative itself should scale. Decision Control Assessment decides how much work AI should do under real conditions, and where a person still needs to step in.
A successful pilot establishes evidence of value. Decision Control becomes relevant when leadership must determine how much operating authority different cases should receive after scale.
Bring the evaluation plan, available results, and the decision leadership needs to make.