Appointing a director who "knows AI" is not the same as appointing a director who can govern it. The most useful test of a candidate is not their technical résumé but their ability to oversee AI as a business transformation: challenging investment decisions, monitoring whether adoption creates value responsibly, and pressing management on risk and controls. This article distills guidance from corporate governance bodies into five considerations boards can apply when vetting candidates who claim AI or tech expertise.
Beyond Buzzwords: Why Boards Need Genuine AI Governance Expertise
The appeal of a dedicated AI expert on the board is obvious, but the Harvard Law School Forum on Corporate Governance cautions that such appointments can present practical and governance challenges. The pool of individuals combining deep AI expertise with the qualifications to serve effectively as a public company director is limited, and the percentage of companies for which AI is so fundamental that it requires a board-level expert is very small. An AI-focused appointment can also raise questions about gaps in other areas of enterprise risk, such as cybersecurity, political or environmental risks.
That does not make the appointment wrong; it makes the vetting essential. A candidate who can genuinely strengthen oversight must demonstrate capability across strategy, risk, and governance — not merely fluency in the vocabulary of machine learning.
Strategic Acumen: Guiding AI's Direction and Value Creation
The first consideration is whether the candidate can help the board oversee AI as a business transformation rather than a science project. PwC's governance guidance frames the board's role as helping management stay focused on the decisions that matter most: challenging where the company should invest, and monitoring whether AI adoption is creating value responsibly.
Boards should expect management to prioritize high-risk uses, embed controls from the outset, escalate significant incidents, and report on business value as well as risk. A strategically capable candidate will ask how the company decides where to lead, lag, or exit in AI adoption, and whether investments are producing measurable outcomes that support long-term strategy. Ask candidates directly: how would you challenge an AI investment case in this industry, and what evidence of value creation would you demand?
Risk Management Oversight: Navigating AI's Complexities
Second, can the candidate actually oversee AI-related risk? Diligent's self-assessment template for board directors suggests directors benchmark their readiness across strategy, risk, and personal development — clarifying their understanding of key AI concepts and risks, assessing their ability to oversee AI strategy, governance, and risk, and identifying where the board has, or lacks, AI expertise and controls.
Adapt that discipline to the interview. A candidate suited to board-level oversight should be able to identify where the company's AI use cases carry the greatest exposure, explain what controls and escalation paths they would expect management to have in place, and describe how they would know whether those controls are working. Technical depth matters here only insofar as it enables credible challenge; a candidate who cannot translate risk concepts into governance questions is not adding oversight capacity.
Governance Framework Knowledge: Ensuring Ethical and Compliant AI
Third, does the candidate understand how AI governance is actually implemented inside an organization? Guidance from the National Association of Corporate Directors and the Data & Trust Alliance on implementing AI governance emphasizes that AI's business value depends on the quality of the data that feeds it, and that trustworthy AI — with outputs that are accurate, reliable, and safe — requires significant investment in foundational data and AI capabilities, platforms, and tools.
That means probing deployment, governance, and stewardship of data, not just model selection. Candidates should also show awareness of the broader governance landscape: the OECD AI Principles, first intergovernmental standard on AI, adopted in 2019 and updated in 2024, are among the reference points cited in competency frameworks for executive and board leadership. A director who can evaluate governance frameworks and practices — and knows when to bring in legal or technical advisors — is worth more than one who can only build systems.
Practical Application vs. Theory: Distinguishing Experience
Fourth, separate lived, board-relevant experience from theoretical knowledge. Writing in NACD's Directorship magazine, Kim Box recommends that boards consider appointing a director with a technology background and AI acumen to provide broader expertise on AI-related matters — but she frames this as one element of a multifaceted effort to build board competency, involving traditional and innovative educational opportunities and cross-collaboration with diverse technical and non-technical stakeholders.
The right questions are behavioral: Where has the candidate made decisions about AI investment, deployment, or risk in an organizational context? Have they guided AI strategy at a scale comparable to yours? Can they describe a governance failure they helped prevent or correct? Candidates whose experience is purely theoretical, or confined to a single technical niche, may struggle to contribute to the collective judgment a board requires.
The Challenge of the 'AI Expert' Director and Alternative Strategies
Fifth, weigh the appointment against the alternatives. Boards can enhance their understanding of AI by receiving regular reports from management on AI applications, associated risks, and governance practices. This can involve engaging with executives overseeing technology and risk management, and establishing cross-functional committees at the management level dedicated to AI governance. Additionally, legal, consulting, and technical advisors can offer board-level education, assisting directors in evaluating governance frameworks, assessing risks related to specific AI use cases, and identifying business opportunities presented by AI.
All directors should, at a minimum, possess baseline AI literacy. A single expert cannot substitute for a literate board — and a board that relies on one director for AI matters may defer rather than deliberate.
AI Director Assessment Checklist
Use this decision matrix to evaluate candidates against the four dimensions of board-level AI competence, plus the structural question of whether the appointment is the right vehicle at all. Score each row for every candidate; the goal is a clearer decision on whether their AI expertise suits board-level oversight, with specific strengths and gaps identified.
| Assessment area | What to look for | Key question to ask |
|---|---|---|
| AI strategy oversight | Can challenge where the company should invest in AI and monitor whether adoption creates value responsibly, including measurable outcomes tied to long-term strategy | How would you challenge our AI investment case, and what value evidence would you demand from management? |
| AI risk management | Understands key AI concepts and risks well enough to oversee controls, escalation of significant incidents, and reporting on risk as well as business value | Where are our greatest AI risk exposures, and what controls would you expect to see in place? |
| AI governance frameworks | Knows how deployment, governance, and data stewardship are implemented, including the data quality and platform investments that make AI outputs accurate, reliable, and safe | How have you operationalized AI governance in an organization, and which external frameworks do you draw on? |
| Practical application | Demonstrates lived decision-making on AI strategy, deployment, or risk at relevant scale, plus commitment to ongoing learning as AI evolves | Describe an AI governance failure you helped prevent or correct — and what you have learned since? |
| Appointment fit | Recognizes that the pool combining deep AI expertise with public-company director qualifications is limited, and that AI rarely justifies crowding out other risk expertise on the board | What oversight gaps might your appointment create, and how would you help close them? |
A candidate scoring strongly across the first four rows genuinely strengthens strategic oversight and governance. Weakness in a single row may be addressable through the alternatives above — management-level AI governance committees, periodic management updates, and external advisors — rather than disqualifying an otherwise strong director.
Strengthening Board AI Oversight: A Portfolio Approach
Boards should adopt a structured, multi-faceted approach to integrate AI expertise, combining targeted director appointments with continuous learning and external advisory support, rather than relying on a single 'AI expert' director. The appointment decision, in other words, is one instrument in a portfolio, evaluated against the checklist above and revisited as the technology and the company's use of it evolve. The measurable indicator of success is regular board self-assessments showing improved AI literacy and the successful integration of AI risks and opportunities into strategic discussions — a standard any nominating committee can put in place before the next director search begins.
Sources
- Board Oversight of AI: Do Boards Need AI Experts? — The Harvard Law School Forum on Corporate Governance
- Board oversight of AI transformation — PwC
- AI self-assessment template for board directors — Diligent
- Director Essentials: Implementing AI Governance — Nacdonline
- AI Competency Assessment for Executive and Board — American Institute of Business & Technology
- How to Build the Board’s AI Competency — Nacdonline
