ai-process-assessment:using-methodology

A structured AI discovery methodology identifies, scores, and sequences automation opportunities through an evidence-gated workflow with mandatory phase conditions, ensuring opportunities are backed by sourced baseline data rather than premature solutioning.

To calculate ROI for AI process assessments, this methodology uses a deterministic Python math engine that computes all cost figures, ensuring no numeric outputs are fabricated in prose and requiring rigorous baseline metrics for deterministic financial modeling.

Yes, this methodology applies directly to operational excellence consulting engagements, providing a standardized 11-phase workflow from scoping to final deliverable that enforces validation gate conditions before proceeding to the next stage.

The best way to sequence AI opportunities is applying a multi-phase workflow that scores opportunities using deterministic financial modeling and mandatory gate conditions, producing a structured roadmap validated by evidence-gated baseline metrics.

Yes, you need rigorous baseline metrics because this methodology is evidence-gated, requiring sourced baseline data and adherence to mandatory gate conditions across 11 distinct phases before opportunities can be scored and sequenced.

AI discovery projects produce unverified value claims when lacking a structured, evidence-gated methodology; this skill eliminates that by enforcing deterministic financial modeling via a Python engine and mandatory phase validation conditions.