Enterprise Data Readiness Matrix

─ METHODS ─

Tools, agents, and models used on this project
TASK	AGENT / TOOL	MODEL / COST
dimension synthesis	4-panel deep research (Gemini DR-Max)	research / $7
rubric design	Green/Yellow/Red floor-rule model	portfolio time
worked example	fictional F500 + 90-day remediation plan	portfolio time
4Q writeup	EXPLANATION.md	portfolio time

TASK

AGENT / TOOL

MODEL / COST

dimension synthesis

4-panel deep research (Gemini DR-Max)

research / $7

rubric design

Green/Yellow/Red floor-rule model

portfolio time

worked example

fictional F500 + 90-day remediation plan

portfolio time

4Q writeup

EXPLANATION.md

portfolio time

─ EXPLANATION ─

The defining barrier to production AI in 2026 isn’t model intelligence; it’s data readiness. This rubric is the diagnostic an AI platform PM runs on a customer’s data layer before a launch date goes on a slide: five dimensions (canonical entity IDs, lineage/provenance, freshness, governance/eligibility, dedup/embedding hygiene), each scored Green/Yellow/Red, with a floor rule (the weakest dimension sets the deployment posture). It ships with a worked example: the matrix applied end-to-end to a fictional Fortune 500 publisher, scored, with a dated 90-day Red→Yellow→Green remediation plan. Grounded in a 4-panel deep-research read of mid-2026 enterprise AI PM hiring criteria; the Green-state examples come from the five readiness problems I had to solve on my own agent fleet’s knowledge base before it would produce citable output.

─ WHAT THIS DOESN'T YET DO ─

It's a readiness diagnostic, not a remediation engine. The customer's data team still owns the migration work; the PM owns the gate and the sequencing.

The Green/Yellow/Red thresholds are practitioner judgment calls; two PMs could score the same customer slightly differently. The worked example exists partly to calibrate where the lines sit.