The Future of the Boardroom: AI Is Changing How Boards Govern

At Nasdaq’s 2026 Future of the Boardroom Forum, leaders across governance, finance, technology, and investing confronted a shared reality: the challenge is no longer understanding AI; it is adapting governance models to keep pace with it. Boards must rethink how they prepare, oversee, and make decisions in an environment defined by speed, data, and continuous change.

"AI is no longer a distant concept, it's a present force. It's influencing how we evaluate risk, how we make decisions, and how we uphold accountability. And when it's applied with care and clarity, it's enhancing boardrooms around the world," noted Gabriella Halasz-Clarke, Head of Governance Solutions at Nasdaq.

This shift was reflected across the forum, where several clear themes emerged for boards navigating this new operating reality.

1. Governance Is Becoming Continuous, Not Periodic

Boards have long operated on a quarterly cadence of meetings, reports, and committee updates. That model was built for a slower, more predictable environment. For AI-driven organizations, decisions are made faster, risks emerge in real time, and the information landscape shifts between meetings in ways that static dashboards cannot capture.

This tension is compounded by the rise of shadow AI, where employees use AI tools or features at work that are not formally approved by the organization. Florin Rotar, Group Chief Technology Officer at Atos, framed this as a signal of a broader issue, sharing “There is a risk that people are using unvetted, unapproved versions of consumer-grade AI. To me, that’s more of a signal that something is not right in the organization around the tools or training they’re being given.”

With that context, boards can shift from reactive enforcement to proactive enablement. That may mean, for example, updating reporting cadences to reflect the speed of AI-driven decision-making, building escalation models that surface emerging risks between meetings, and treating governance as a continuous operating rhythm rather than a periodic checkpoint.

2. AI Is Evolving from a Tool to a Workforce

In recent years, governing AI meant governing a piece of software, considering what data it touches, how that data is secured, and who has access to it. That framing is now outdated. AI agents today can analyze data, draft contracts, initiate procurement, and make operational recommendations, often without direct human intervention. In some organizations, AI agents already outnumber the humans managing them.

The nature of board oversight changes as a result. When AI systems begin to operate like employees—trained, deployed, and tasked with real responsibilities—boards must start thinking about governance differently. Rotar quantified the shift, stating “We have about 10,000 AI agents, AI employees, and I suspect many of you probably have hundreds or thousands as well, if not tens of thousands.” He challenged boards to apply the same rigor by asking, “Who made the decision to hire or commission those employees? How did they get trained? Who manages their performance? What are they allowed to do? How much do they cost?”

For boards, governing AI systems that operate like employees is no longer a theoretical exercise. Organizations are already operating with large-scale AI agent deployments, making workforce-level AI governance a present-day requirement.

3. AI Is Closing the Information Gap Between Boards and Management

One of the most persistent challenges in corporate governance has been the information gap between boards and management. Directors have traditionally relied on management to frame the narrative, determining which data is surfaced, how risks are contextualized, and where attention is focused—but the dynamic is beginning to shift.

With access to AI-powered tools, directors can now analyze materials independently, benchmark performance, and test assumptions in ways that were not previously possible. This creates the potential for more informed oversight and more productive boardroom dialogue. “AI basically mitigates the information gap between management and the board. Now because they have AI tools and they can benchmark, they can run their own analysis. They can presumably challenge management assumptions in more concrete ways,” described Elena Hera, Partner at Goodwin.

Hera also cautioned, “Is there a danger of the board stepping outside its oversized lane and potentially into management’s remit?” The opportunity is not for boards to take on operational roles, but to strengthen governance by asking sharper questions, identifying blind spots, and holding management to a higher standard of transparency. 

4. AI Investment Requires Real Discipline 

AI is no longer experimental. Organizations are committing significant capital across hiring, platforms, and infrastructure, but, in many cases, boards are still working to apply the same level of discipline to measuring returns.

The same rigor boards apply to any capital investment should apply to AI, even as AI introduces new complexity. Some investments are foundational and may take years to deliver value, while others resemble venture portfolios where a few high-impact use cases justify smaller experiments. Unlike traditional IT projects, AI carries ongoing operational costs, particularly token usage, that can rival or exceed initial implementation.

John Bartholdson, Board Chair at Lincoln Tech, commented, “Ultimately, AI investments are being valued on the same basis as other investments. What is the return to the shareholders? Cash flow, margin, improved operations efficiencies—those should apply to AI as well. Just because AI is new and exciting, it shouldn’t get a free pass.”

Sarah Youngwood, Chief Financial Officer at Nasdaq, reinforced the importance of governance and accountability, stating, “It’s not just about measuring AI. It’s also about having a clear RACI and accountability around how AI investments are evaluated and governed.”

Investors are raising expectations, and broad claims about using AI across the enterprise no longer meet that bar. Instead, boards must push for greater precision and accountability, including:

• Specific AI use cases tied to measurable business outcomes
• Clear AI frameworks to evaluate short- and long-term investments
• Transparent AI cost tracking, including token and compute expenses

This level of discipline is reinforced externally as well. John Roe, Global Co-Head of BlackRock Investment Stewardship, noted that the primary readers of disclosures are no longer just institutional investors, but AI systems are now interpreting them at scale and raising the stakes on precision and clarity.

Looking Ahead: Enabling the Always-On Boardroom

Beyond building governance frameworks, boards are increasingly focused on how AI can support their work in practice. Throughout the forum, a consistent theme emerged: directors need faster access to relevant information and more continuous visibility into risk and performance as governance shifts toward an always-on model.

Nasdaq’s board portal software, Nasdaq Boardvantage®, is built to meet that need. Nasdaq Boardvantage’s AI Assistant is designed specifically for boards and governance teams, helping directors surface meeting-specific insights across governance, risk, financial, and strategic materials, ask questions in natural language, and quickly access the information that matters most. All AI capabilities are delivered within a closed, controlled environment, supporting informed decision-making while maintaining the privacy, control, and responsible AI practices boards require. Learn more about Nasdaq Boardvantage AI.