Why this path
Boards are increasingly expected to provide AI oversight. This path builds the conceptual foundation for asking the right questions, evaluating management's AI strategy, and fulfilling fiduciary duty in an AI-enabled organization.
10 concepts
AI is an umbrella term for systems that recognize patterns, understand language, make predictions, or support decisions. And what's hiding under that umbrella matters enormously for what you're actually buying, building, or approving.
Why it matters for Boards: What AI actually is — and the hype to be skeptical of
Generative AI produces new content—text, images, code, summaries, audio—on demand, based on patterns learned from vast amounts of existing data.
Why it matters for Boards: The capability driving most current investment and risk
AI strategy is how an organization decides where AI is worth investing in, what it will take to get there, and what it will deliberately leave alone.
Why it matters for Boards: How organizations develop a coherent AI investment thesis
AI governance is the system that determines who can deploy AI, under what conditions, with what oversight, turning ad hoc experimentation into accountable organizational practice.
Why it matters for Boards: The governance system boards should be asking to see
AI risk management is the discipline of deciding which AI systems need controls, what those controls should be, and who is accountable when something goes wrong before something does.
Why it matters for Boards: How AI risk fits into enterprise risk frameworks
Responsible AI is the difference between an organization that says it uses AI ethically and one that can actually prove it.
Why it matters for Boards: What demonstrably responsible AI practice looks like
The rules governing AI are multiplying fast and vary by country, sector, and use case. AI compliance is how your organization stays on the right side of them before a regulator, auditor, or client asks.
Why it matters for Boards: The regulatory landscape management must navigate
When a model is wrong, or right for the wrong reasons, and no one catches it, the decisions it drives keep compounding the error. That's model risk.
Why it matters for Boards: The model risk discipline in financial and regulated industries
Hallucinations are AI outputs that are confidently stated but factually wrong. The model isn't lying or guessing, it's generating plausible-sounding language that happens to be false.
Why it matters for Boards: Why AI output reliability matters for board oversight