AI is advancing at a faster rate than board governance structures and board talent are able to mix. The company’s future, both short- and long-term are riding on the proficiency, discipline and competency of its board in topics surrounding AI. This includes the board becoming an AI operator of layered insights as well as an upskilling of the board to the point that they all have not only competency and fluency, but are able to provide useful insights to ask the right strategic and operational questions.
According to a recent 2026 Protiviti survey,only 23% of boards include AI as a standing agenda item. Boards that consistently include AI are far more likely to drive results. In fact, 63% of high-ROI companies have AI on every board agenda versus 13% of low-ROI companies. What this really means is only one in four boards have AI as a recurring agenda topic, and that 25% of boards that do are disproportionately top performers.
Recently, I had the privilege of coaching my son’s high school basketball team. I was thrilled, as they are great, respectful young men, and also because it brought me right back to my own high school basketball exhilaration as a player. The strategizing and knowing the opponent; the intensity and the teamwork; the split-second setting up teammates to their superpowers and working, sweating and persevering to bring home the win. Or, if not getting the win, leaving everything all out on the court.
The recent March Madness and the NBA playoffs offer a useful lens for thinking about what effective leadership and competent coaching actually looks like. The data is clear: Every single college and NBA coach has played the sport extensively themselves before ever being a coach in college or a professional team. They deeply understand the game from being “on the floor.” They have felt the pressure of a last-second shot, understood the rhythm and culture of a locker room, and navigated the realities of competition firsthand. That lived experience shapes better decisions, sharper instincts, and more credible leadership and competency coaching. Boardrooms are no different, especially in the explosive realities of AI and how it is transforming industries, businesses, as well as talent direction and needs.
Yet, in boardrooms, especially around AI, we often accept the opposite: directors who advise on transformation without ever having built, scaled or operated an AI-driven business themselves.
According to Ashif Mawji, managing director of ScaleGood Fund and chair of Ben Stelter Foundation, “For decades, I’ve watched boards mistake ‘watching the horizon’ for ‘navigating the ship.’ In the AI era, governance is no longer a spectator sport — it’s a full-contact discipline. If your board matrix doesn’t include an operator who has actually felt the friction of deploying AI at scale, you aren’t overseeing a strategy; you’re overseeing a blind spot.”
Mawji continues, “We must stop asking if AI is on the agenda and start asking if the people around this table are equipped to pressure-test the answers management gives us. Data is the oil, AI is the pipeline and insight is the heat. If, as a director, you are not insisting on seeing at least a 20% improvement in operational efficiency, you simply aren’t doing your job!”
Having the AI operator competency around the board table to ask the right questions is key to best-in-class board governance. According to the Protiviti study, “Many boards struggle with insufficient expertise in the boardroom, a lack of strategic clarity and unclear AI ROI.” They do not have AI on every board agenda with an in-depth discussion and they also do not have the AI operator and board competency around the table to ask the right questions.
It is critical that the board also views the effort from the lens of enterprise transformation, not purely AI.
According to Laura Peterson, director of MicroVision and former CEO of Palladyne AI, “AI represents a tectonic shift, both in scale and speed, within an ongoing digital transformation that has been unfolding for decades. The organizations getting this right treat AI as inseparable from operational excellence and people — indeed, an enterprise transformation. When directors combine deep AI and digital transformation fluency with operational experience leading disciplined, data-driven enterprise-scale transformation, they become an invaluable strategic thought partner to the management team, bringing perspective to strategy, rigor to planning and execution, and discipline to oversight, while thoughtfully balancing opportunity and risk against an ever-evolving competitive and technological landscape.”
For private companies, the lesson is straightforward: AI risk without board proficiency and discipline is a reputational and fiduciary vulnerability.
Recent data underscores the urgency. Gartner reports that 80% of nonexecutive directors believe current board practices and structures are inadequate to oversee AI. Additional research finds that 66% of directors report limited-to-no AI knowledge or experience and nearly one-third say AI does not regularly appear on their board agendas. Even among the S&P 500, only around one-third of directors disclose formal AI oversight at the board level, according to ISS-Corporate.
All of this data is concerning given the rapid advances in AI and the impending effects of quantum. The implications of AI and quantum computing to company strategy, combined with the recent data showing board refreshment and turnover is low and slow, with only 8.6% of board seats turning over (newly elected directors) in Russell 3000 (down from 13.3% in 2022), are too great to ignore. Boards are changing more slowly, but more deliberately, even as executive turnover (CEOs) is accelerating.
“AI is like electricity. It is transforming every industry in efficiency, creativity and, ultimately, strategy. Boards should insist that the incorporation of AI in these three foundational areas of the business be included in every board meeting,” says Brett Hurt, co-owner of Hurt Family Investments; co-founder and former CEO of data.world, Bazaarvoice and Coremetrics; and author of “Love Conquers Fear: Humanity, AI and the Age of Abundance for All.” For private company CEOs and boards, AI is a strategy accelerator, a risk amplifier and increasingly, a valuation factor. The question is not whether AI should be discussed in the boardroom. The question is whether boards are structurally and compositionally prepared to govern it.
According to William Hartman, vice chairman at CBRE, “Three years ago, AI tenants were effectively nonexistent in the New York office market. Today, across AI fintech, AI legal and AI-driven life sciences R&D, we’re seeing a new class of occupier routinely taking 100,000 square feet at a time — at a pace and scale that’s materially reshaping demand. Rents that averaged in the low-$70s per square foot a mere three years ago are now commanding North of $120 for high-quality space, driven by these firms’ willingness to pay for talent density, infrastructure and location.”
Hartman continues, “Likewise in San Francisco: the San Francisco market, which was approaching 40% vacancy in 2025 from a 5% vacancy in 2019, has stabilized and is in the process of being saved singularly by the AI industry with millions of square feet of leasing.”
“Markets don’t expand this quickly without displacement,” Hartman adds. “The reality is straightforward: This isn’t just incremental growth — AI is reallocating demand. In plain terms, someone else’s lunch is being eaten and being eaten at warp speed.”
Companies anchored to long-standing directors and traditional governance competencies may believe they’re ensuring stability, but in reality, they risk unintentionally directing the business toward irrelevance.
According to Jennifer Ewbank, director of DarkOwl and former deputy director of the Central Intelligence Agency for Digital Innovation, “In the world of intelligence, the most consequential failures rarely stemmed from a lack of information. They stemmed from information that existed but never reached the right decision-maker in time. Boards without AI fluency risk replicating a similar failure mode. The signals are already there in the data your company collects, in the competitive trends your industry is experiencing, and in the threats you and your vendors are exposed to. But without directors who understand how AI systems actually work, and how they interact with other aspects of broader digital transformation, those signals may never get translated into questions that really matter. I spent decades watching what happens when decision-making structures can’t keep pace with the environment they’re supposed to govern. The cost in national security was measured in lives. In corporate board governance, it will be measured in relevance and competitiveness.”
The AI Issues Keeping CEOs and Directors Up at Night
Across private company boardrooms, the top three concerns consistently surface:
Strategic value creation. Boards should also pressure-test a more uncomfortable question: “Could AI make our core offering obsolete within our planning horizon? Or worse, could an AI-native competitor replace us entirely before we’ve had time to respond?” This isn’t hypothetical. In health care, AI is already compressing the value of services that were once highly specialized — diagnostic reads, clinical documentation, prior authorization workflows. In financial services, AI-native competitors are undercutting incumbents on cost and speed, rewriting the rules faster than established players can adapt. The board’s job isn’t to have all the answers, but to ensure management is asking the question with real discipline — and that the answer is reflected in capital allocation decisions.
A key question to be raised often is where does AI meaningfully change our economics and create new competition or devalue current and future value propositions?
Questions boards should be asking for clarity include:
- What new competitive threats (known competitors and unknown competitors) exist? How does this impact the company?
- Which workflows or products are AI-enabled? What does this enable in terms of efficiency?
- Where will AI create margin expansion or compression?
- What capital allocation is required, what’s the investment road map and what is the expected return?
AI is not a technology initiative. It is a business model lever. Boards that treat it as an operational update risk missing its enterprise-wide implications.
Enterprise risk amplification. Where does AI create new exposure? AI brings risks that are both novel and accelerated:
- Competitive realities among known and unknown competitors
- Data privacy and sensitive data leakage
- Hallucinations or errors in customer-facing systems
- Intellectual property infringement
- Expanding regulatory scrutiny
Accountability and operating model clarity. Who owns AI decisions and what rises to the board? High-performing boards insist on clarity on:
- Who is accountable for AI strategy?
- What is the process internally for aligning all AI implementation around the full customer experience end-to-end and the operational flow that supports it?
- When is this reviewed by the board?
- Who owns model validation and monitoring?
- What decisions require board review?
- What triggers escalation?
Absent clear accountability, AI initiatives diffuse and are one-off across the enterprise — precisely the environment where governance breakdowns occur. When governance breaks down, risk rises for negative surprise impacts.
Where Does AI Oversight Live?
Boards are approaching structure in several ways:
- Full board oversight. AI becomes a recurring specific strategic agenda item with quarterly dashboards.
- Strategy committee oversight. Focused on growth implications and competitive positioning.
- Audit/risk committee oversight. Focused on controls, data governance and model risk.
- Dedicated Technology/AI committee. More common in AI-native or AI-intensive companies.
The structure matters less than the clarity, says Donnie Hampton, co-founder and CEO of Roz. “AI oversight becomes real when ownership gets specific on what data is being used, who is accountable, and what triggers escalation.”
According to Orlando Ashford Sr., chair of Perrigo Company PLC, director of Array Technologies and interim CEO of the National Black MBA Association, oversight without ownership is oversight in name only. “Integrating AI into a legacy business model is going to create tension — what I often call ‘lumpiness.’ As a board, we have to lean into that lumpiness,” says Ashford. “We need to create the space for constructive conflict and debate around the risks and opportunities of AI. If the conversation around our AI strategy is too smooth, we are likely missing critical blind spots or failing to push the organization toward true strategic transformation and not maximizing impact.”
The Board Matrix Question
Board business rhythm alone cannot compensate for composition gaps of actual AI operator knowledge and competency. There is a critical distinction between AI fluency and AI operator-level expertise:
- Fluency allows directors to ask informed questions.
- Operator-level expertise allows directors to challenge assumptions, anticipate second-order consequences and serve as strategic thought partners in all facets of the business (opportunity, competitive advantages and risks).
Many boards have deep financial and operational experience but limited advanced technology depth of any kind. And, without deep technology expertise, the bar for competency of the matrix evaluation and upskilling will be “watered-down.” As a result, the company will be at risk.
According to Maureen Hurd, principal of HurdMcNally and CEO and founder of Core Business Solutions, “As board oversight of enterprise technology risk has rapidly evolved, so, too, has the board skills matrix and board refreshment strategy. The matrix is only as useful as the distinctions it forces. Boards must assess whether they need literacy or deep expertise across each category. Most boards need a blend with one or two deep experts and broader literacy across the rest. That distinction makes gaps far easier to identify, and it is those gaps that should be driving the right recruitment and refreshment strategy.”
“With AI governance now at the center of board responsibility, the next generation of directors must bring the same strategic fluency to digital risk that has long been expected of financial oversight. The right directors need to be experienced and technically informed, but also need to know how to listen well, assess and challenge. Organizations that evaluate and refresh their boards with this lens won’t just be better governed, they will be better positioned.”
As private equity firms increasingly embed AI operating partners across portfolios to drive value creation, the market and realities signal that AI capability is now a competitive differentiator.
An AI-ready board matrix evaluates:
- AI strategy experience.
- Data governance expertise.
- Cyber and third-party risk oversight.
- Regulatory and IP exposure familiarity.
- Change management capability.
Boards that identify gaps have three primary levers:
- Recruit directors with relevant AI operator expertise (not consulting, but actual P&L AI operators/founders).
- Add formal AI advisors.
- Invest in structured director upskilling across the entire board.
In a highly complex and rapidly evolving market, composition refresh is not optional — it is strategic and it’s critical.
A Practical Upskilling Road Map
AI governance is governance competence. In today’s environment, every board should include at least one director with current AI operator experience — someone who has led the deployment of AI within a scaled operating business with full ownership as an AI CEO/founder and technologists, not simply advised on it.
AI is now shaping strategy, capital allocation and enterprise risk at a pace that exceeds traditional governance learning curves. Without operator-level perspective in the room, boards risk overseeing a domain they do not fully understand. Nom/gov committees should evaluate the board matrix accordingly. If this capability is absent, the board should prioritize recruiting an AI operator director in short order. Composition refresh is not a technology preference; it is a governance requirement in an AI-defined era.
According to Julie Weatherford, former Central Intelligence Agency senior executive cyber leader and current partner manager at Flashpoint, “After 14 years in CIA technical surveillance, I watched the ground shift. The digital tide was turning toward cyber, and the missions I’d built my career on were fast becoming relics. In 2012, I had to decide whether to cling to my status as an expert or humbly become a student again. I pivoted to the Center for Cyber Intelligence, trading a decade of seniority for the grueling uncertainty of a high-velocity mission. It was scary, exhausting and a massive blow to my ego. However, it was the only way to stay relevant. Today, some boards are making the same fatal mistake. They are clinging to legacy governance while AI transforms the landscape at warp speed. The real threat isn’t a lack of technical knowledge; it’s the pride that prevents directors from admitting they’re behind the curve. If you aren’t willing to be humbled by this technology today, your business could be silenced by it tomorrow.”
What AI Oversight Should Include
The six oversight domains boards should insist on are:
- Strategy and capital allocation (AI portfolio , investment guardrails, return on investment measurement).
- Data governance (data quality, ownership, access controls, retention, third-party data).
- Model governance (testing and validation, monitoring drift, explainability where needed, documentation).
- Cyber and vendor risk (third-party model risk, security reviews, incident response).
- Legal/regulatory and ethics (privacy, IP, sector regs, “responsible AI” rules)
- Workforce and change management (recruiting, job redesign, training, productivity measures, adoption).
Kathy Tune, managing partner of Capita3, says “Thirty years in healthcare technology has shown me what happens when governance lags innovation. That lag is no longer measured in years — it’s measured in months. The health care boards that will lead in this environment are the ones asking not just how to oversee AI, but whether their core business is being reshaped by it. The companies that fall behind won’t do so because they lacked information — They’ll do so because boards weren’t designed to move at the speed AI demands. The window to get ahead of it is shorter than most boards realize.”
A Higher Bar for Health Care Company Boards
Health care boards face a materially more complex AI governance environment than most industries. In the United States, Health Insurance Portability and Accountability Act (HIPAA) obligations extend directly to AI vendors. Boards should confirm that every AI tool touching patient data is covered by a business associate agreement and vetted for HIPAA-compliant security. State laws can be even more demanding — Washington’s My Health My Data Act and California’s Confidentiality of Medical Information Act are two notable examples.
For U.S.-based companies selling digital health products internationally, domestic compliance is not enough. The European Union’s General Data Protection Regulation applies the moment a company processes health data of E.U. residents, regardless of where it’s headquartered. The EU AI Act classifies most clinical health care AI applications as high-risk, triggering mandatory conformity assessments — with full compliance obligations phasing in through 2027. Where your data goes, your regulatory obligations follow — regardless of where you’re headquartered. That fact belongs explicitly on the board’s agenda.
AI trained on historical health data can encode existing disparities across race, gender and socioeconomic status — exposure regulators on both sides of the Atlantic are scrutinizing closely. And when an AI-assisted clinical recommendation contributes to an adverse patient outcome, liability remains unsettled in most jurisdictions. Boards need to know whether their company has a position on it.
The governance question isn’t just “Do we have AI oversight?” It’s “Does our oversight account for every market our products touch?”
Why does it matter for private companies? Buyers and lenders increasingly evaluate operational resilience and compliance maturity. AI governance becomes part of “institutionalizing the business.”
Actionable Board Next Steps
In the next 90 days, your board should:
- Conduct board matrix review specifically identifying gaps in AI operator and competency across the board.
- Conduct a structured AI strategy briefing.
- Draft and approve a concise AI governance charter with key strategic review points at each board meeting.
- Request a full inventory of AI use cases and vendors.
- Define escalation thresholds for AI-related incidents.
- Set a clear expectation that all non-AI-operator board members complete an external AI governance course or credentialing program within three to six months and present their learnings to the board.
Over the next six-to-12 months, your board should:
- Add an AI operator to the governance committee to be “in the room” to evaluate competency, the board matrix and ongoing upskilling.
- Ensure ongoing tracking on the progress of each non-AI operator on the board to complete their initial AI upskilling and credentialing.
- Run an AI incident simulation exercise.
- Institutionalize AI performance and risk dashboards.
- Integrate AI operator and capability into ongoing succession and refreshment planning.
What “Great” Looks Like
AI-ready private company boards can confidently state:
- AI strategy is explicitly linked to enterprise value creation.
- AI deployments and data touchpoints are known and monitored.
- Guardrails and escalation triggers are defined.
- Oversight accountability is clear.
- The board matrix includes AI fluency and competency and operator-level expertise.
- The board can articulate its AI governance framework to customers, lenders, regulators and potential buyers.
The most effective private boards are not asking whether AI matters. They are asking whether their governance model and regular agenda — and their own composition — is strong enough to lead and be growth-relevant in an AI-defined era.
According to Jim Forbes, independent director and member of the technology committee of Internet Sciences Inc., “Given the rapid developments across the AI landscape, directors should adopt General Colin Powell’s ‘40-70 rule’ to balance speed and accuracy in decision-making.” In today’s complex and accelerated market, AI oversight is not a future agenda item. It is a present fiduciary responsibility in every board agenda. Attempting to play the game without board members who have been AI operators — given it is the most consequential technological shift in history — is risking the company’s short and long-term survival.