AI Readiness Executive Checklist

How to use the readiness checklist

For each item, record Yes, No, In Progress, or Not Applicable, plus the evidence, owner, and next action. “In Progress” should not be scored as an established capability simply because funding has been approved. Discuss conflicting answers with the relevant owners.

This checklist is an evidence-preparation tool. The AI readiness calculator is a separate 1–5 questionnaire with a defined scoring method. The worked report shows how the questionnaire result can become a 90-day plan.

Prioritize a mandatory control or serious risk even when another pillar has a lower score. Use the launch readiness checklist before approving an individual system.

Purpose

This checklist helps executive teams evaluate whether the organization is prepared to adopt and govern artificial intelligence responsibly. It is designed for board-level and executive committee discussions, not for technical deep dives alone.

Who Should Complete It

• Executive sponsor for AI
• CIO, CTO, COO, or equivalent
• Chief risk, legal, or compliance officer
• Chief data officer or data lead
• HR or transformation lead
• Business unit leaders sponsoring major AI use cases

Complete it as a cross-functional exercise. Single-function answers usually overstate readiness.

Instructions

For each item, select one response:

• Yes: Evidence exists and the practice is operating
• No: The practice is absent or ineffective
• In Progress: Work has started but is not yet reliable
• Not Applicable: Clearly outside scope, with rationale noted

Use evidence. Prefer documents, inventories, metrics, and named owners over verbal assurance.

Scoring Guidance

Assign points as follows:

• Yes = 2
• In Progress = 1
• No = 0
• Not Applicable = exclude from denominator

Calculate section percentages and an overall percentage of available points. Use the interpretation guidance at the end.

Assessment Questions

Leadership and Vision

1. A named executive sponsor owns enterprise AI readiness. (Yes / No / In Progress / N/A)
2. Leadership has agreed a shared definition of AI success. (Yes / No / In Progress / N/A)
3. Risk appetite for AI use cases is documented. (Yes / No / In Progress / N/A)
4. Board or executive committee receives periodic AI updates. (Yes / No / In Progress / N/A)
5. Leaders model responsible AI use in their own workflows. (Yes / No / In Progress / N/A)
6. Conflicting executive narratives about AI have been resolved. (Yes / No / In Progress / N/A)

Strategy and Budget

7. AI initiatives are linked to explicit business objectives. (Yes / No / In Progress / N/A)
8. A written AI Strategy exists and is current. (Yes / No / In Progress / N/A)
9. Funding covers foundations, not only tools. (Yes / No / In Progress / N/A)
10. Enablement and change management are budgeted. (Yes / No / In Progress / N/A)
11. Opportunity cost is considered in portfolio decisions. (Yes / No / In Progress / N/A)
12. Stop/continue criteria exist for major initiatives. (Yes / No / In Progress / N/A)

Governance and Oversight

13. An AI governance forum or equivalent exists. (Yes / No / In Progress / N/A)
14. Decision rights for approval and pause are clear. (Yes / No / In Progress / N/A)
15. An acceptable-use policy is published and communicated. (Yes / No / In Progress / N/A)
16. High-impact uses require heightened review. (Yes / No / In Progress / N/A)
17. Exceptions are logged with expiry dates. (Yes / No / In Progress / N/A)
18. Human oversight expectations are defined by risk tier. (Yes / No / In Progress / N/A)

Technology

19. Approved AI tools and platforms are documented. (Yes / No / In Progress / N/A)
20. Identity and access controls cover AI systems. (Yes / No / In Progress / N/A)
21. Logging and monitoring exist for production AI. (Yes / No / In Progress / N/A)
22. Secure integration patterns are defined. (Yes / No / In Progress / N/A)
23. Development and production environments are separated where needed. (Yes / No / In Progress / N/A)
24. Technical ownership is assigned for material systems. (Yes / No / In Progress / N/A)

Data

25. Critical data domains for AI have named owners. (Yes / No / In Progress / N/A)
26. Data quality issues for priority use cases are known. (Yes / No / In Progress / N/A)
27. Sensitive data handling rules for AI are enforced. (Yes / No / In Progress / N/A)
28. Legal basis for AI data use is reviewed where required. (Yes / No / In Progress / N/A)
29. Retrieval and training data sources are documented for material systems. (Yes / No / In Progress / N/A)
30. Data access for AI is least-privilege by design. (Yes / No / In Progress / N/A)

People and Operations

31. Role-based AI literacy training is underway. (Yes / No / In Progress / N/A)
32. Managers are prepared to coach AI-enabled teams. (Yes / No / In Progress / N/A)
33. Workflow redesign accompanies tooling for priority cases. (Yes / No / In Progress / N/A)
34. Support channels exist for AI issues and escalation. (Yes / No / In Progress / N/A)
35. Shadow AI usage is understood and addressed. (Yes / No / In Progress / N/A)
36. Operating teams can sustain systems after launch. (Yes / No / In Progress / N/A)

Risk and Compliance

37. An AI inventory exists and is maintained. (Yes / No / In Progress / N/A)
38. Material AI risks are recorded in a risk register. (Yes / No / In Progress / N/A)
39. Vendor AI due diligence is required. (Yes / No / In Progress / N/A)
40. Incident response includes AI-specific scenarios. (Yes / No / In Progress / N/A)
41. Regulatory obligations relevant to AI are mapped. (Yes / No / In Progress / N/A)
42. Privacy and security reviews are triggered by risk. (Yes / No / In Progress / N/A)

Success Metrics and Roadmap

43. Baseline metrics are captured before major launches. (Yes / No / In Progress / N/A)
44. Adoption and outcome metrics are reviewed together. (Yes / No / In Progress / N/A)
45. Risk indicators are part of executive dashboards. (Yes / No / In Progress / N/A)
46. An Executive Roadmap sequences foundations and use cases. (Yes / No / In Progress / N/A)
47. Roadmap owners and dates are explicit. (Yes / No / In Progress / N/A)
48. Weak initiatives can be retired without political deadlock. (Yes / No / In Progress / N/A)

Portfolio Discipline

49. The active AI portfolio is visible to leadership. (Yes / No / In Progress / N/A)
50. Priority initiatives have business owners. (Yes / No / In Progress / N/A)
51. Dependencies on scarce talent are managed. (Yes / No / In Progress / N/A)
52. Duplicate tools are identified and rationalized. (Yes / No / In Progress / N/A)
53. Benefits claims require evidence. (Yes / No / In Progress / N/A)
54. Customer or citizen impact is assessed for relevant systems. (Yes / No / In Progress / N/A)

Assurance and Continuous Improvement

55. Audit evidence can be produced for material systems. (Yes / No / In Progress / N/A)
56. Lessons from failed pilots are captured. (Yes / No / In Progress / N/A)
57. Policies and standards are reviewed at least annually. (Yes / No / In Progress / N/A)
58. Readiness is reassessed after major changes. (Yes / No / In Progress / N/A)
59. External framework alignment is considered where relevant. (Yes / No / In Progress / N/A)
60. Executive decisions reference readiness evidence, not anecdotes alone. (Yes / No / In Progress / N/A)
61. Cross-functional accountability is enforced in practice. (Yes / No / In Progress / N/A)
62. Communication to staff about AI intent is clear and current. (Yes / No / In Progress / N/A)
63. High-risk autonomy is constrained until controls mature. (Yes / No / In Progress / N/A)
64. The organization can pause AI systems when needed. (Yes / No / In Progress / N/A)
65. Board-level questions on AI risk can be answered with evidence. (Yes / No / In Progress / N/A)

Interpretation of Results

High readiness (about 80% or above): Foundations are largely in place. Focus on disciplined scale, measurement quality, and continuous improvement.

Moderate readiness (about 50% to 79%): Proceed selectively. Close critical governance, data, and ownership gaps before expanding high-impact use cases.

Low readiness (below 50%): Limit expansion. Prioritize sponsorship, inventory, policy, and assessment. Uncontrolled growth is likely to create avoidable risk.

Section scores matter. A high overall score with weak governance or data sections is not a green light for scale.

Recommended Next Actions

1. Discuss section scores in the executive committee.
2. Assign owners and dates to every critical "No."
3. Commission or update an AI Readiness Assessment where evidence is thin.
4. Update the Executive Roadmap to reflect gap priority.
5. Re-run this checklist quarterly until moderate or high readiness is stable.

Frequently Asked Questions

Can one person complete this checklist?

They can draft it, but cross-functional validation is required for credibility.

Is "In Progress" acceptable for board reporting?

Yes, if evidence of progress exists and residual risk is acknowledged.

How does this relate to technical audits?

This checklist is an executive readiness view. Technical audits remain necessary for system-level assurance.

Summary

This executive checklist provides a board-ready view of AI readiness across leadership, strategy, governance, technology, data, people, risk, metrics, and roadmap discipline. Used with evidence and regular review, it helps organizations prepare for responsible adoption rather than mistaking tool access for readiness.