Governing AI in the Boardroom

AI is reshaping how boards operate—but most leaders aren’t ready. In this episode, we outline ten strategic actions every board must take to govern AI effectively and responsibly. From embedding AI into board agendas to modernising risk oversight and leadership structures, this is essential listening for executives navigating AI transformation. Learn how to align AI with business value, scale responsibly, and strengthen decision-making at the top. 

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Transcript

The Boardroom Is Changing — Are You Ready?

AI isn’t creeping into the enterprise. It’s storming in.

From automating contracts to making hiring decisions, this technology is now shaping strategy, operations, and risk—whether the board is ready or not.

But most boards aren’t. In fact, Deloitte found that 66% of directors say they have limited or no experience with AI oversight .

That’s not a technology issue. It’s a governance issue.

So let’s walk through 10 practical steps to bring your board up to speed and in control—before AI moves faster than you do.

Step One: Put AI on the Agenda—Permanently

First things first—AI has to stop being a one-off discussion.

Right now, 31% of boards don’t even have AI on their regular agenda . That’s a serious blind spot.

You need recurring updates—not just from the CIO, but across the executive team. What new AI systems are being deployed? What risks are emerging? What strategic opportunities are on the table?

Make AI a standing item, not a seasonal topic. That sends a clear message to the business: this matters.

Step Two: Build Board-Level AI Literacy

You don’t need your board coding in Python. But you do need them fluent in what AI is—and isn’t.

That includes understanding model bias, data quality, explainability, and the regulatory context. PwC and Deloitte both highlight this as foundational  .

Some firms are already acting. Munich Re, for instance, runs regular AI simulations and strategic foresight sessions for its board .

Invest in learning. Bring in experts. Run workshops. Fluency builds foresight—and foresight protects the business.

Step Three: Assign Ownership—Then Build a Governance Rhythm

Oversight without ownership fails fast.

Boards must clearly assign responsibility for AI—not just within management, but also among directors. That might mean formalising it under an existing risk committee, or setting up a dedicated AI and Technology Committee.

But more importantly, it means ensuring there’s an enterprise-wide AI governance structure beneath it.

Look at Unilever’s approach. They created cross-functional steering groups that bring IT, legal, HR, and operations into one AI oversight stream. That ensures governance reflects the real-world complexity of AI decisions.

The board should ask: Who owns the register of AI use cases? Who escalates high-risk models? What gets reported and when?

Governance isn’t just a structure. It’s a rhythm. Make sure the business has one.

Step Four: Align AI Strategy to Business Value

Most boards hear about AI in terms of pilots and prototypes. That’s not strategy.

Boards need to ask: where is AI creating measurable business value?

Ping An used AI to cut insurance claims processing time by 50% . GE Healthcare embedded AI across product lines by investing in shared data and modelling infrastructure.

Step four is all about this: tie AI efforts directly to revenue growth, cost efficiency, and strategic differentiation.

If it’s not adding value, it’s not ready for scale.

Step Five: Appoint a CAIO—or Empower Equivalent Leadership

AI success doesn’t come from scattered efforts. It comes from orchestration.

That’s why more firms are appointing a Chief AI Officer—or at least designating an executive with cross-cutting AI responsibility.

L’Oréal’s CAIO, for example, has a direct line to the CEO and oversees AI strategy, compliance, and enablement across the group.

As a board, you should ask: who owns the AI roadmap? Do they have the budget, visibility, and authority to drive adoption responsibly?

If no one’s empowered to lead across silos, the organisation isn’t ready to scale safely.

Step Six: Deepen Risk and Compliance Oversight

Here’s where many boards fall short.

AI doesn’t just bring operational risk. It introduces systemic risk—bias, privacy breaches, IP theft, lack of explainability, and reputational exposure.

Amazon’s AI hiring tool ended up favouring male candidates and had to be scrapped. Clearview AI’s facial recognition platform led to lawsuits across multiple countries.

Boards must ensure management maintains a full inventory of AI use, scores risk levels, and applies governance proportional to harm.

This means pushing for model validation protocols, bias audits, and scenario testing—especially for high-impact decisions.

Step Seven: Engage the Full C-Suite—Not Just Tech Leaders

AI isn’t an IT issue. It’s an enterprise transformation issue.

Yet Deloitte found that most board-level AI discussions are limited to the CIO or CTO . That’s a red flag.

Finance, HR, Marketing, Operations—all are now impacted.

Goldman Sachs, for example, uses AI in asset management, HR screening, legal review, and client insight. It’s systemic.

Boards should ask: What’s the CFO doing with AI in forecasting? What’s the CHRO doing about role redesign? If these questions draw blanks, you’ve got work to do.

Step Eight: Modernise Board Composition and Advisory Structures

According to Deloitte, 40% of boards are now rethinking composition due to AI .

And for good reason. If no one in the boardroom has governed AI at scale, how will you challenge assumptions or see around corners?

That doesn’t mean replacing your entire board. But it does mean bringing in new perspectives—either as full directors or rotating advisors.

And look beyond tech. Some of the best AI governance insight is coming from risk officers, ethics experts, and global regulatory veterans.

Diversity of thought isn’t just good ethics. It’s good risk mitigation.

Step Nine: Shift from Pilot Thinking to Platform Thinking

Here’s a pattern I see all the time—ten AI pilots running in silos, none of them reaching scale.

What’s missing? Shared data pipelines. Enterprise tooling. Change management.

GE Healthcare avoided this by building a scalable AI platform that could be used across multiple products and business lines .

Boards must ask: How many use cases are in production? What percentage of employees are actually using AI tools? How are we measuring adoption and value realisation?

Without scale, AI remains a sideshow.

Step Ten: Use AI to Strengthen the Board Itself

Here’s a powerful idea: the board doesn’t just oversee AI—it can use it.

Stanford’s research highlights how boards are using AI to analyse governance patterns, evaluate strategic options, and improve decision-making .

Imagine AI tools that flag when board discussions skew toward operations instead of strategy. Or that benchmark your governance against peers.

Step ten is this: turn the mirror inward. Use AI to make your board sharper, faster, and more informed.

Closing: One Step at a Time, But Don’t Wait

AI governance isn’t about mastering everything at once. It’s about knowing where to start—and having the courage to act before you’re completely comfortable.

So take that first step. Make AI a standing agenda item. Build literacy. Appoint ownership. Push for integrated oversight.

Because in this space, waiting too long is the greatest risk of all.