Context
Most enterprise AI initiatives fail not because of bad technology but because of bad sequencing. AARI was designed to solve that.
Problem
Enterprises needed a standardized way to assess AI readiness across data infrastructure, governance, talent, business alignment, and financial measurement.
Product Design
AARI scores enterprises 0 to 100 across five weighted pillars: Data Infrastructure, Governance and Risk, Talent and Capability, Business Alignment, and Financial Measurement. Each pillar exposes capability gaps and generates a readiness score.
Dashboard Layer
Translates maturity scores into decision-ready visibility. Leadership sees score breakdowns, peer benchmarks, compliance exposure, and risk concentration. Identifies where AI initiatives will stall before they fail.
Financial Modeling
Embeds ROI forecasts, NPV scenarios, payback period analysis, and risk-adjusted projections. Ties AI maturity directly to capital allocation decisions.
Governance Architecture
Integrates explainability requirements, compliance guardrails, model monitoring, and operational accountability into scoring logic.
Business Model
Subscription SaaS with tiered pricing by company size, premium benchmarking as expansion layer, advisory services as upsell path.
Results
Fully productized AI readiness platform combining five pillar scoring, phased rollout sequencing, financial modeling, governance guardrails, and monetization strategy.
Learnings
- • The most valuable PM skill in enterprise AI is translating ambiguity into a framework stakeholders can act on.
- • Governance and financial modeling are not afterthoughts. They are the product.
Artifacts
AARI scoring framework, five pillar diagnostic model, dashboard wireframe, phased rollout plan, NPV and ROI financial model, SaaS pricing model.