Free Resource
The Enterprise AI Readiness Checklist
25 questions to gauge whether you're ready to ship AI to production. Score yourself — the more you can tick, the closer you are. Stuck on several? That's exactly what our Strategy Sprint fixes.
1.Strategy & value
- You have named 1–3 specific business problems AI should solve (not "do AI").
- Each use-case has an owner and a measurable success metric.
- You can estimate the ROI (cost saved / revenue / time) of the top use-case.
- Leadership agrees on the first use-case to fund.
- You know your build-vs-buy stance for core capabilities.
2.Data & knowledge
- The data the use-case needs is identified and accessible.
- Data quality and freshness are good enough to trust.
- Access controls / entitlements on that data are documented.
- You have a place to store embeddings / a vector store (or a plan for one).
- Sensitive data handling (PII/PHI) is understood and compliant.
3.Security & governance
- You know where the AI will run (your cloud vs. a vendor).
- Prompt-injection and data-exfiltration risks are considered.
- There is a human-in-the-loop plan for high-stakes actions.
- Audit logging and monitoring requirements are defined.
- Compliance needs (SOC 2 / HIPAA / GDPR / RBI) are mapped.
4.Technical foundation
- You are model-agnostic (not locked to a single provider).
- You have (or can add) an evaluation harness to measure quality.
- Latency and cost budgets for the use-case are defined.
- Integration points (APIs, ERPs, CRMs, EHR/core systems) are listed.
- You have a path from pilot to production, not just a demo.
5.Team & operating model
- Someone owns the AI initiative end-to-end.
- Your team can maintain what gets built (or you have a partner).
- End-users are involved early to drive adoption.
- You have a feedback loop to improve the system after launch.
- A realistic timeline and budget are agreed.