AI Readiness Assessment
Methodology
How we score, benchmark, and ground the assessment in real frameworks.
Five pillars, weighted contribution
| Pillar | Weight | What it measures |
|---|---|---|
| Strategy & Leadership | 20% | Ambition, sponsorship, investment posture. |
| Data Readiness | 25% | Catalogue, quality, lineage, governance. |
| Infrastructure & Technology | 15% | Cloud, MLOps, RAG, sandbox, observability. |
| Talent & Skills | 15% | Specialists, literacy, career paths. |
| Governance & Risk | 25% | AUP, inventory, oversight, vendor, regulation. |
Framework mapping
- NIST AI Risk Management Framework (Govern, Map, Measure, Manage)
- EU AI Act (Article 4, 9, 10, 13, 14, 15, 17, 62 where relevant)
- ISO/IEC 42001 clauses 5, 7, 8, 9, 10
- HIPAA sections 164.308 / 164.314 / 164.514 for the healthcare bank
- SR 11-7, NYDFS Part 500, SOX 404, PCI, ECOA 1002 for the financial services bank
FAQ
How is each pillar weighted?
Strategy & Leadership 20%, Data Readiness 25%, Infrastructure & Technology 15%, Talent & Skills 15%, Governance & Risk 25%. Governance and Data carry the most weight because they are the most common blockers we see in production AI programmes.
How is each question scored?
Every question maps to a 0-4 integer. Likert 1-5 maps to 0-4. Yes/No maps to 0 or 4. Percent-range buckets (0-10%, 10-30%, 30-60%, 60-85%, 85%+) map to 0-4. A pillar score is the weighted sum of its questions divided by the max weighted sum, then normalised to 0-100.
Where do benchmark numbers come from?
Benchmarks are seeded from published research (McKinsey, Deloitte, Gartner, IDC) and continuously refreshed from anonymised user data. A segment (industry × size × pillar) needs at least 30 completed assessments to appear; otherwise we fall back to the industry-wide average.
How are recommendations chosen?
We score each recommendation by impact × ease (both on a 1-5 scale), filter by trigger conditions (for example, pillar score below 55), then surface the top 5. The full library is browsable on your results page.
What frameworks are referenced?
Recommendations and questions are tagged against NIST AI RMF (Govern / Map / Measure / Manage), EU AI Act (Article 4, 9, 10, 13, 14, 15, 17, 62 where relevant), and ISO/IEC 42001 clauses. Vertical banks also reference HIPAA sections (healthcare), and SR 11-7 / NYDFS 500 / ECOA (financial services).
What is a maturity tier?
Tier is derived from the overall 0-100 score: Nascent (0-25), Emerging (26-50), Scaling (51-75), Integrated (76-100). Tiers are shorthand; the real value is in the pillar breakdown.
Do you use my answers to train AI models?
No. Answers are stored in our Supabase project under the buzzi-tools schema and used only to score your own assessment and - in aggregate, anonymised form - to refresh peer benchmarks and our annual State of AI Readiness report.