Approval drag cost
Model review labor, delay cost, and avoidable error instead of hiding behind adoption rhetoric.
This is a public ROI proof surface for Decionis decisioning infrastructure. Model one workflow, expose the current approval drag, inspect the governed state, and decide whether the pilot pays back fast enough to deserve expansion.
The page is designed to answer three questions fast: what approval drag costs now, what changes under governed execution, and whether the pilot repays inside 60 days.
This model exposes the economic bottleneck in one governed workflow. Replace the public defaults with your own signal volume, cycle time, and pilot assumptions.
Modeled monthly cost of review labor, approval delay, avoidable loss, and low-signal work.
4.5d down to 1.9d in the governed state.
65 operator hours recovered by not creating work on weak signals.
$50,924 realized in 60 days against $35,140 of deployment cost.
It stays on one workflow because broad platform ROI is a weak close story. Approval drag, time-to-decision compression, and no-action wins are the signals a CFO, COO, or risk sponsor can defend.
Model review labor, delay cost, and avoidable error instead of hiding behind adoption rhetoric.
A governed decision layer creates value when it blocks low-signal work from consuming operator time.
The page uses explicit ramp and deployment assumptions so the close case can be challenged, not hand-waved.
If the model clears the 60-day bar, move into the assessment or guided pilot. If it does not, the right answer is to narrow the workflow until the economics are defensible.
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