Enterprise AI Maturity Assessor

What it does

Turns an organization's (or a single department/process line's) AI and automation readiness into an evidence-based score and ties it to action. It first applies a 6-dimension maturity rubric — D1 Strategy & sponsorship, D2 Data readiness, D3 Process maturity, D4 Technology & integration, D5 Skills & culture, D6 Governance & risk — each scored 0-4, weighted and scaled to 0-100. Every point is anchored to an evidence sentence; where evidence is missing the dimension is flagged "not measurable" and the remaining weights are re-normalized (no artificial zero). The total maps to the Gartner AI Maturity 5 levels (Awareness → Active → Operational → Systemic → Transformational).

It then builds the process portfolio: each candidate is scored with an automation-suitability rubric across 5 criteria (rules-based, volume, standardization, structured-data, stability — each 0-20, total 0-100). For every candidate it computes annual value, payback period, and a simple NPV, then places it on the Gartner TIME quadrant (Tolerate/Invest/Migrate/Eliminate). The Invest quadrant is ranked with RICE and split into 30/60/90 days with MoSCoW. Each roadmap step carries owner · success metric · pre-condition.

When to use it

When you need to answer "Where do we start?", "Which process should we automate?", "Will this AI investment pay back?", or "What should our digital-transformation roadmap be?" — for an executive-ready readiness assessment, a consulting pre-diagnosis, or a first-sprint foundation for a dev team. Whenever you want the portfolio ranked by ROI instead of picking a single showcase pilot.

Method / frameworks

Five named frameworks are interleaved: Gartner AI Maturity Model (level anchor), NIST AI RMF 1.0 — Govern/Map/Measure/Manage (D6 governance rubric), CRISP-DM / CRISP-ML(Q) + Data Readiness Levels (D2 data readiness), the automation-suitability rubric (process scoring), and Gartner TIME (portfolio ranking). Economics uses payback period + NPV; prioritization uses RICE + MoSCoW. In the TR context KVKK is non-skippable in the governance dimension. False precision is forbidden: no unverified "sector average X%", and missing payback inputs yield a typical range flagged in gaps.