📌 Board AI Readiness Checklist
As artificial intelligence (AI) becomes a key driver of business strategy and innovation, boards of directors must ensure their organizations are prepared to leverage AI responsibly, effectively, and strategically. AI presents both opportunities and risks, from enhancing decision-making and operational efficiency to raising ethical, regulatory, and cybersecurity concerns. A well-prepared board must oversee AI adoption with a focus on governance, risk management, compliance, and business alignment.
This draft Board AI Readiness Checklist provides a structured approach to help directors evaluate their AI preparedness, ensuring AI-driven initiatives align with corporate goals, adhere to ethical principles, and deliver long-term value.
By following this checklist, boards can foster AI innovation while safeguarding against bias, legal exposure, and reputational risks.
🔹 1. AI Strategy & Business Alignment
✅ Has the board defined AI’s role in the company’s strategy?
✅ Does AI adoption align with long-term business objectives?
✅ Are AI initiatives delivering measurable ROI and competitive advantage?
✅ Has the board evaluated industry-specific AI trends and risks?
🔹 Example: A retail board ensures AI-powered recommendations improve customer experience and revenue while respecting privacy.
🔹 2. AI Governance & Risk Management
✅ Is there an AI governance framework in place?
✅ Are there policies ensuring responsible AI usage (bias, transparency, accountability)?
✅ Are AI-driven decisions regularly audited for fairness and compliance?
✅ Has the company established a crisis plan in case AI fails or creates reputational damage?
🔹 Example: A financial board mandates regular AI bias audits to prevent discriminatory lending decisions.
🔹 3. Ethical AI & Compliance
✅ Is AI being used ethically and in compliance with global regulations (GDPR, EU AI Act, CCPA)?
✅ Is there a policy ensuring AI does not reinforce discrimination or bias?
✅ Is there human oversight in AI-driven critical decisions (hiring, lending, medical diagnosis)?
✅ Are AI models explainable and transparent to stakeholders?
🔹 Example: A healthcare board ensures AI-powered diagnostics undergo human review before medical decisions.
🔹 4. AI Literacy & Talent Readiness
✅ Does the board have members with AI expertise or access to AI advisors?
✅ Are executives and employees trained to work alongside AI?
✅ Is AI talent being recruited or developed internally?
✅ Is there an AI ethics officer or AI governance committee?
🔹 Example: A manufacturing board hires AI consultants to train leadership on AI-driven supply chain optimization.
🔹 5. AI Investment & Technology Evaluation
✅ Are AI investments aligned with business objectives and financial sustainability?
✅ Are partnerships with AI vendors evaluated for security, ethics, and transparency?
✅ Has the board considered a “build vs. buy” strategy for AI development?
✅ Are AI-driven innovations being monitored for effectiveness and ROI?
🔹 Example: A logistics board assesses third-party AI logistics tools for cost, scalability, and data privacy before adoption.