How Boards Should Design AI Strategy

AI has now become a core agenda item that boards must handle directly. According to a Deloitte survey, 66% of respondents said their boards have "limited or little knowledge" of AI, and 40% said AI has prompted them to rethink board composition itself.

Boards should focus on four pillars: Impact, Risk, Governance, ROI. AI can contribute to cost reduction, improved customer experience, and new revenue creation, but it simultaneously increases risks around data breaches, security, IP infringement, bias, hallucination, and regulation.

On the governance front, boards need to clarify board-level accountability structures and establish an AI strategic plan aligned with business objectives and risk tolerance. It's also necessary to reference frameworks such as the NIST AI Risk Management Framework or ISO/IEC 42001 to institutionalize fairness, transparency, and accountability.

ROI must be proven as actual value beyond the experimentation stage. Boards should track KPIs such as financial performance, productivity, customer satisfaction, and AI adoption rates, while also examining TCO that includes software, hardware, data, integration, maintenance, and talent.

There are clear warning signs to watch for.

• Shadow AI: use of unapproved AI tools
• Investments driven by vendor hype
• Fragmented AI stacks scattered across departments

Finally, boards should be asking questions like "What is our AI strategy," "Who is accountable," "What are our risk limits," and "Is there a path to enterprise-wide scaling." Gartner's Hype Cycle, updated in June 2025, also shows that attention is shifting from the generative AI hype toward foundational capabilities such as AI-ready data, AI agents, and ModelOps.