Executive AI Readiness Checklist

We have an executive sponsor accountable for AI outcomes and risk. AI is tied to explicit business objectives (not experimentation alone). We maintain a prioritized portfolio of AI initiatives with owners and milestones. We have defined where AI is allowed, prohibited, and restricted. We have a budget model for AI initiatives (build, buy, operate). We have KPIs that measure both value and risk for AI systems. We communicate AI strategy internally in plain language. We have a policy for employee use of consumer AI tools. Our board reviews AI risk and value at a defined cadence. We maintain a process for retiring AI systems that no longer meet requirements.

We classify AI systems by impact and apply tiered controls accordingly. We require documentation for AI systems (purpose, data, limitations, owners). We have an approval workflow for high-impact AI use cases. We perform privacy reviews for AI systems using personal or sensitive data. We conduct security reviews for AI vendors and internally built AI systems. We have a process to detect, triage, and remediate AI incidents. We have guardrails for human oversight in high-risk decisions. We test for bias and harmful outputs appropriate to use-case context. We track third-party model updates and monitor resulting behavioral changes. We have audit trails for key AI decisions, approvals, and deployments.

We know which datasets are used for which AI systems (lineage). We enforce role-based access control for data and model endpoints. We have data quality checks and monitoring for critical datasets. We have a standard approach for model evaluation and benchmarking. We run red-team or adversarial testing for important AI systems. We monitor model drift and performance degradation in production. We have controls to prevent sensitive data leakage (prompts, logs, outputs). We have an architecture standard for integrating AI safely into products. We have clear environments (dev/test/prod) and change management controls. We have an inventory of AI systems, models, tools, and vendors in use.

We have a cross-functional AI council or steering group. We have clear roles for product, engineering, legal, risk, and data governance. We have training for staff on safe AI use and escalation paths. We have guidelines for prompt design, data handling, and disclosure. We have a defined process for onboarding new AI vendors/tools. We have a repeatable delivery process (intake → build → approve → deploy → monitor). We staff or contract the capabilities needed (ML/LLM, security, compliance). We can respond quickly to AI incidents (who, when, how). We have a communication plan for external stakeholders if AI incidents occur. We review and update policies as AI regulations and tools evolve.

We can quantify expected benefits for AI initiatives (cost, revenue, risk). We use a consistent ROI model including assumptions and sensitivity. We measure realized value after deployment and compare to projections. We have a framework to stop/iterate initiatives that are not delivering. We have a playbook for scaling successful pilots into production. We track adoption and user behavior to ensure AI features are actually used. We capture operational cost of AI (inference, data, licenses, support) over time. We define SLAs and reliability expectations for AI systems. We include fairness, safety, and security criteria in go/no-go decisions. We maintain a backlog of improvements based on monitoring and feedback.