AI Executive Readiness Checklist

Description

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AI Executive Readiness Checklist

31 actionable checkboxes for AI business case validation, cost modelling, and organizational readiness.

This role-based checklist contains 31 ready-to-use checkboxes extracted from the LLM Production Readiness — Complete Checklist (v8 consolidated). It covers the strategic, financial, and organizational decisions that executive leadership must validate before committing to production AI deployment.

What’s Inside:

• 31 checkboxes across 3 domains: POC Validation (14), Cost Modelling (7), Org Readiness (10)
• Model selection: model weights integrity verification (SHA-256 checksums), domain-specific benchmarking (minimum 3 models), hallucination rate testing on use-case inputs, latency evaluation at P50/P95/P99, commercial license verification, and hallucination leaderboard scoring (Vectara/RAGTruth)
• Threat modelling & AI inventory (pre-deployment gate): formal MITRE ATLAS threat modelling, AI Bill of Materials (AIBOM) creation, NIST AI RMF mapping (Map/Measure/Manage/Govern), and NIST AI 600-1 Generative AI Profile cross-referencing
• AI security framework evaluation: MAESTRO (Cloud Security Alliance, 7-layer architecture for agentic AI), CSA AI Controls Matrix (243 control objectives across 18 domains with AI-CAIQ self-assessment), Google SAIF alignment assessment, and IEEE P2894 agent interoperability tracking
• Business case: measurable success criteria (accuracy/latency/cost thresholds), baseline performance of current non-AI solution, total cost of ownership (inference + infra + human review + compliance + monitoring), API cost exposure mapping ($0.10-$15/million input tokens with monthly/annual run-rate projection), self-hosted breakeven analysis (~2M tokens/day threshold), RAG vs prompt engineering vs fine-tuning decision with documented rationale, and user population/concurrent load/unacceptable failure mode definition
• Governance: observability signal ownership assignment, acceptable use policy documentation, incident review war-room process with escalation paths, and AI system registry creation (risk classification, owner, next review date)
• Training: LLM security training for developers (prompt injection, data leakage, MCP server supply chain risks) and failure mode training for support/product teams (hallucination, drift, silent failures, agentic loops)
• SLA & continuity: latency targets (P95 < 2s), uptime targets (99.9%), hallucination rate ceilings, fallback strategy for provider outage (backup provider or graceful degradation), and disaster recovery playbook testing including model re-deployment from artifact registry
• Bias & fairness: Fundamental Rights Impact Assessment (FRIA, EU AI Act Article 27) covering non-discrimination, privacy, and fairness
• Interactive HTML with progress tracking — check off items as you complete them

Use Cases:

• AI investment go/no-go decisions with pre-defined numeric gates
• Total cost of ownership modelling and API cost exposure projection
• Organizational readiness assessment: governance, training, SLAs, and incident response
• Executive-level AI risk framework evaluation (MITRE ATLAS, NIST AI RMF, MAESTRO, CSA AICM)
• Bias and fundamental rights impact assessment for EU AI Act compliance

Perfect For: CEOs, CTOs, CIOs, VPs of Engineering, board members, strategic planners, and department heads evaluating or approving AI production deployments.

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