The CTO for an insurance company is presenting the annual AI update to the company’s board. He reports that fourteen AI-assisted processes now operate across underwriting, claims, customer service, and fraud detection. Thirteen of them analyze inputs and produce outputs, and the fourteenth is an agent that places procurement orders autonomously under specified conditions. The CTO reported that since deployment three months ago, the procurement agent has successfully executed 340 purchases. He also provided an update on an AI model deployed by the Digital Experience team to handle first-contact customer inquiries, noting that it has reduced human call center volume by 22 percent without any decrease in customer satisfaction scores.
The board is composed of experienced directors. Most have run large businesses, including one who is the retired CEO of an insurance company. One is the former CFO of a software company. The chair of the Audit Committee was a lead partner in a big 4 firm and holds a certification from an AI education program that he took after he retired from his firm two years ago. The other board members have no direct experience in insurance or technology and look to these directors during the annual AI presentation.
The board congratulates management on their progress in AI, and the Audit chair asks about guardrails for the agent. Assured by the CTO that they worked with an expert to develop a responsible use policy, the directors have no additional questions and the board meeting moves on.
It sounds perfectly reasonable. However, is the board asking the right questions? Do they even know what the red flags are? How can directors properly govern what they don't understand?
This is a story about what is missing when the board does not yet possess sufficient AI literacy to know what questions to ask or how to assess management’s answers.
The Distinction Between Literacy And Expertise
Given how AI is transforming companies and the nature of business itself, we need to recognize that a board member without AI literacy is quickly becoming the equivalent of a board member who has never learned how to read a P&L. However, the governance conversation about AI and board composition tends to focus on whether boards need AI expertise. Most companies that use AI rather than create AI conclude that expertise is not needed, and they leave it to each individual board member to decide whether and to what extent to prioritize personal AI education.
Indeed, expertise has become a somewhat controversial term in the boardroom. In AI, as in other aspects of technology, boards frequently tend to associate expertise with domain-specific technical knowledge, which in the case of AI refers to knowledge that typically lives with the engineers and data scientists building the systems, with technical consultants, and with the few executives who started on the tech side and rose to the C-suite. Expertise is specialized, hard-won, and genuinely valuable in the boardroom when combined with an understanding of strategy and an innate ability to ask critical questions and assess answers affecting the company’s opportunities, risks, and performance without dipping too deeply into the weeds.
AI literacy is something else. It is the essence of a board member’s capacity to engage with strategy in an AI-driven world: to know – or know how to find out - what the company’s AI systems are actually doing in terms of business impact, to recognize how strategy can be supported and transformed with the assistance of AI, how it can fail and which failures can become material, where accountability should sit and what to look for when it is absent, how to weigh the balance of speed and control, and how to assess whether what management is telling you is substantive or simply jargon intended to reassure. AI literacy does not require understanding of how the technology works, but it does require a more than superficial understanding of what AI models do and how they are changing in real time.
Every experienced director already understands the distinction between expertise and literacy in other domains. You do not need to be an actuary to govern insurance risk and you do not need to be an auditor to serve on an audit committee, yet you would not join the board of an insurance company without the ability to understand how insurance companies make and lose money. You do not need to be an HR expert to oversee talent strategy, yet someone who has never led a team would likely not be invited to join the board of a large, complex company. As financial literacy has been described: “This foundational level equips [directors] with the ability to read financial statements and independently develop a strategic perspective on their firm's financial health regarding key areas such as performance, position and solvency.” Substitute AI for financial, and AI literacy comes into focus. As AI increasingly becomes as integral to companies as finance and talent, directors who lack a foundational level of AI literacy will be left behind.
How To Stay Literate As AI Shifts
While board-level certification programs provide a great foundation, AI literacy is not a credential to acquire and carry forward without more. Instead, AI literacy is a practice to build and maintain as the technology moves forward. The reading habits and networks that kept a director current in 2024 are not adequate for 2026, and what is adequate today will need to be revisited again and again and again as AI continues to evolve.
Maintaining AI literacy requires a mindset of curiosity and continuous learning, beginning with a foundational understanding of how AI changes business and continuing engagement to stay current, including high-quality sources read consistently combined with deliberate peer engagement and enough continuing hands-on familiarity with AI tools to support a general view of what AI models do.
AI Governance-Focused Reading
There is a plethora of available information about AI; the challenge is finding those that are geared toward the board. Here are some terrific examples, starting with Alpha, the organization where I am proud to serve as a Fellow at the Alpha Institute for AI Governance.
Alpha provides AI governance intelligence built specifically for board directors and C-suite executives. Its ecosystem addresses the staying-current problem directly rather than leaving directors to assemble their own reading lists from sources designed for other audiences. BoardBrief, published by Alpha, covers the intersection of AI and emerging technologies, governance, and leadership from a director’s vantage point.
In addition, my former organization, KPMG Board Leadership Center, publishes resources for directors with many pieces focused on AI governance, all written with the authority of governance experts who have engaged with board members for decades.
NACD’s Directorship publication and its annual Governance Outlook cover AI from a director’s perspective with practical specificity, and NACD offers numerous educational programs on AI governance at the national and chapter levels.
The Harvard Law School Forum on Corporate Governance publishes peer-reviewed analysis of AI governance developments, regulatory shifts, and board-level liability, and most major law firms circulate client alerts when material AI rules shift. This just highlights a few: there are many more.
Reading Focused on AI Growth and the Competitive Context
McKinsey’s Quantum Black AI practice publishes analyses of where companies are and are not capturing AI value. The BCG AI Radar and EY’s CEO survey series track AI investment patterns and ROI realization at the enterprise level. These are strategy documents, not technical ones, and they provide reference points to evaluate whether management’s AI investment thesis is consistent with what leading organizations are actually experiencing.
Convening to Learn
Another plug for Alpha: The Alpha Council is an invite-only global leadership membership network of board directors and C-suite leaders actively governing AI deployments at scale. Membership provides access to a private global peer network, board workshops and committee education through Alpha Advisory, and in-person Alpha Table dinners convening directors in cities across the world. Alpha also runs half-day forums and full-day summits that bring directors into direct conversation with leaders in the technology ecosystem. Alpha Advisory provides AI governance audits and board red-team engagements.
Using AI Tools Yourself
Practice compounds faster than any reading list. Every director should set aside time to spend playing with AI tools directly, to develop a nose for what these systems do well, where they fail, and what their limitations feel like from the inside. A director who has spent thirty minutes a day interacting with a large language model has a different and more useful frame of reference than one who has only read about it. By analogy, a director of a gaming company who has no interest in video games and has never tried to play one, or a director of a pet-focused company that does not like pets would likely not the best fit for those companies.
The directors asking the sharpest questions in AI discussions may not be the ones with the most formal credentials. They are often the ones who have read widely and experimented enough to develop their instincts: when something sounds plausible but may not have been thought through, when something that the competition is doing successfully is missing from management’s strategy, when other board members sit silently through an AI presentation because they don’t know what questions to ask.
The Nom/Gov Committee’s Role
Individual directors can choose to develop AI literacy on their own initiative, but literacy that depends entirely on individual motivation is fragile, uneven across the board, and often invisible to the company’s shareholders and regulators. The nominating and governance committee is the structural mechanism for converting individual effort into collective board capability, and on AI it has largely not yet played that role at most companies.
The committee’s starting point is the skills matrix. Most boards have added AI or digital expertise to the matrix in recent years, but a checkbox indicating that one director holds AI credentials is not an assessment of whether the entire board has the literacy to govern AI. The nom/gov committee should define what AI literacy means at the board level for their company specifically, and assess each board member against that standard, not just the director whose biography mentions it.
As an enabler, the committee should set explicit expectations for continuing education and begin to build them into the board’s annual calendar. Externally led education on what AI means for the board’s oversight function, including how the technology is shifting and what new governance questions are emerging, will help the entire board build a foundation of literacy. The committee should also consider whether the board has sufficient access to expert perspective for specific questions.
Director recruitment is the long-term lever. As board seats turn over, the nom/gov committee should treat AI literacy as a substantive competency requirement for all directors. That means asking candidates specific questions about AI governance experience in the interview process, testing for the ability to interrogate rather than simply recognize AI topics, and being candid about the board’s current literacy gaps when articulating what the next director needs to provide.
Finally, the committee should establish a regular cadence for reviewing the board’s AI literacy against the company’s evolving AI footprint. A company that operated thirteen AI-assisted processes last year now operates fourteen, one of which acts autonomously. The board’s literacy should keep pace with that deployment curve. The nom/gov committee is the body responsible for ensuring that it does, and should include an AI literacy assessment as part of its board evaluations, education expectations, and succession planning processes.
What AI Literacy Looks Like In The Boardroom
A director who has developed genuine AI literacy changes what management presents, because the director’s questions will determine what management knows will be tested. A substantive AI briefing for the insurance company outlined at the start of this article, prepared in response to a board that knows what to ask, goes deeper than simply summarizing the company’s AI projects and already-measured results. On the risk side, it reviews how management identifies which systems, if they performed incorrectly for a period of time, would produce significant harm that is difficult or impossible to reverse. It names the error detection mechanism for each consequential system, the individual accountable for its performance between reviews, the system’s known limitations, what management doesn’t know about how it works, and the escalation path if things go wrong. For agentic systems, it also specifies the authority boundaries, the human review sequencing, and how alerts and shutdowns would be handled. On the growth side, it measures AI performance against the competitive landscape, the company’s agreed-upon level of AI adoption speed, and the progress against the business case that justified each investment, and saves time for open discussion about the possibilities that may enable a shift in the business model or open up new sources of revenue in the next few years.
A management team that can present this type of picture coherently is operating AI well. A board in which every member knows what to ask about each of these areas and how to assess the quality of the responses is governing well.
Questions For The Nom/Gov Committee:
• How does the board’s current AI literacy compare to the AI footprint the company is operating now and wants to operate in the future, and where are the specific gaps?
• What structured AI education is on the board’s calendar for the next twelve months, and who is providing it independently of management?
• What expectation has been set for individual board members for continuing education in AI governance, and how is it tracked and enforced?
• In the next board recruitment cycle, will AI literacy be considered a foundational competency rather than an optional credential?
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Suggested reading
Alpha, https://boardbrief.alpha.ac/the-end-of-noses-in-fingers-out-the-rise-of-continuous-governance/
KPMG Board Leadership Center, https://kpmg.com/us/en/board-leadership/technology-and-innovation.html
BCG. “Split Decisions: The BCG CEOs and Boards Survey.” May 4, 2026. https://www.bcg.com/publications/2026/ai-governance-gaps-where-ceos-and-boards-disagree
McKinsey Quantum Black. “The State of AI in 2025: Agents, Innovation, and Transformation.” November 5, 2025. https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai
McKinsey. “Where AI Will Create Value — and Where It Won’t.” April–May 2026. https://www.mckinsey.com/capabilities/strategy-and-corporate-finance/our-insights/where-ai-will-create-value-and-where-it-wont
EY. CEO Priorities 2026: Growth, Resilience, and AI ROI. May 2026. https://www.ey.com/en_gl/ceo/ceo-outlook-global-report
Directors & Boards. “Navigating AI Adoption and Cybersecurity Oversight.” February 17, 2026. https://www.directorsandboards.com/board-issues/ai/navigating-ai-adoption-and-cybersecurity-oversight/
