AI Data Readiness Checklist (Production-Focused)

This checklist reflects what we see repeatedly when helping teams move AI from prototype to real-world deployment.

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1. Data Foundations

☐Clear ownership for core datasets (clinical, operational, financial)

☐Consistent definitions for key business metrics

☐Data quality thresholds defined and monitored

☐Known sources of truth (no duplicate "gold" datasets)

2. Data Integration & Interoperability

☐Reliable pipelines across core systems (EHR, claims, ops)

☐Standardized identifiers and entity resolution

☐Interoperability standards in place (FHIR / HL7 where applicable)

☐Latency and freshness aligned with AI use cases

3. AI-Ready Data Architecture

☐Scalable, cloud-native or hybrid data platform

☐Separation of raw, curated, and feature-ready data

☐Ability to support batch + near-real-time workloads

☐Clear lineage from source → model → output

4. Governance, Security & Compliance

☐Data access controls aligned to least-privilege principles

☐Auditability for data usage and AI outputs

☐Compliance requirements mapped (HIPAA, regulatory, internal policies)

☐Clear rules for PHI / PII handling in AI workflows

5. Operational Readiness

☐Defined deployment model (cloud, VPC, on-prem)

☐Monitoring for data drift and model behavior

☐Human-in-the-loop processes where required

☐Clear ownership for AI systems in production

6. Business Alignment

☐AI use cases tied to measurable outcomes

☐Clear success metrics beyond "model accuracy"

☐Stakeholders aligned across product, engineering, and operations

☐Roadmap from pilot → production → scale

Quick Self-Assessment

0-6 checked:You're experimenting

7-15 checked:You're piloting

16+ checked:You're ready to scale AI responsibly

Final Thought

AI success isn't about choosing the right model — it's about building the right foundations.

At Nuvii Partners, we help teams assess readiness, modernize data platforms, and operationalize AI safely and at scale.

Related Resources

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