AI Readiness & Governance Checklist
A structured 18-point checklist designed for C-level executives evaluating generative and predictive AI readiness across governance, data infrastructure, risk, and commercial viability.
1. Business Value & Use Case Qualification
Is each proposed AI use case tied to a measurable unit-economic improvement or operational efficiency KPI?
Avoid exploratory technology pilots that lack executive sponsorship or clear baseline performance measurements.
Have you evaluated build vs. buy tradeoffs for foundation model integration vs. off-the-shelf SaaS?
Proprietary fine-tuning is rarely required where Retrieval-Augmented Generation (RAG) over structured enterprise corpora suffices.
2. Data Quality & Architectural Accessibility
Is enterprise data accessible via governed, documented APIs and clean data lakehouse partitions?
AI output reliability is directly constrained by source data hygiene, schema consistency, and update frequency.
Are metadata, data lineage, and role-based access controls (RBAC) enforced at the query level?
LLM contextual injection must strictly respect existing user permissions and tenancy boundaries.
3. Governance, Privacy & Regulatory Compliance
Does the organisation maintain a published Acceptable Use Policy for generative AI tools?
Employees will use public consumer AI models unless enterprise-grade, privacy-preserving alternatives are provisioned.
Are enterprise inputs protected against model training retention in vendor terms of service?
Verify that commercial API agreements explicitly prohibit vendor training on enterprise context data.