What does success actually mean?
Define the primary operating, clinical, quality, financial, or decision outcome; secondary outcomes; guardrails; and the threshold that would justify expansion.
Pilot Efficacy & Scale Readiness
Your AI pilot looks promising. Is there enough evidence to scale it?
A scoped evaluation engagement for organizations that already have an AI pilot or early deployment but still need a defensible answer to whether it works in the real workflow, creates enough value, and is ready for broader use.
MEASURE
PROVE
SCALE
PROVE
SCALE
The scale decision
Answer the expensive question before expanding the pilot.
Technical performance is not the same as real-world efficacy. A scale decision needs evidence that the AI improved the outcome that matters in the actual workflow, under conditions leadership understands, at economics the organization can support.
What it answers
Did the pilot create enough real value to justify broader deployment?
The engagement is built around the decision leadership needs to make, not around producing an evaluation report for its own sake.
Can the pilot produce credible evidence?
Review the cohort, baseline or comparator, exposure, sample support, observation window, confounders, instrumentation, and missing outcome data.
What happened in the real workflow?
Measure adoption, task completion, human intervention, exceptions, delays, failure modes, downstream effects, and whether model performance translated into operating performance.
Where does it work, and where does it not?
Look beyond the average result to identify users, cases, contexts, and operating conditions where benefit, burden, or failure meaningfully changes.
Do the economics survive scale?
Estimate operating value against retained human handling, rework, delay, AI/control cost, and implementation effort using the evidence the pilot actually produced.
What should happen next?
Recommend whether to scale, narrow the use case, redesign the pilot, extend measurement, collect specific missing evidence, change the workflow, or stop.
How the work proceeds
Experimental design is the method. The deliverable is a scale decision.
The exact design depends on the pilot, available data, operational constraints, and whether prospective evaluation is still possible.
01
Frame the decision
Define the scale/no-scale question, stakeholders, claimed value, consequence of being wrong, and evidence already available.
02
Audit the evaluation
Review endpoints, comparator, cohort, instrumentation, workflow exposure, confounding, adoption measurement, and economics.
03
Analyze or repair the proof plan
Use the strongest feasible design for the environment, which may include prospective measurement, quasi-experimental comparison, stratified analysis, or a redesigned pilot.
04
Make the scale decision actionable
Return the evidence, remaining uncertainty, conditions for expansion, and the specific next move.
Where CONTROL enters.
A pilot can prove useful and still require decision-level control before broader operational authority is appropriate. If the unresolved question becomes how much authority AI should have in production, when verification or human review is required, or where persistent control is needed after launch, the next step may be a Decision Control Assessment.
Have a pilot that needs a credible scale decision?
Bring the current evaluation plan, available results, and the business decision leadership needs to make. We will determine whether this engagement is the right fit.