AI in Action: Lessons from a Tech Leadership Roundtable in Hong Kong

At a recent AI roundtable hosted by Russell Reynolds Associates, CTOs and board-level tech leaders across sectors in Hong Kong shared hard-won insights on what’s working—and what’s not—when it comes to implementing AI in real organizations. These seven takeaways highlight how companies are moving from experimentation to enterprise impact.

1. Workforce transformation: New roles (not just reductions)

While some companies are preparing for AI-driven restructuring, others are creating entirely new functions. A CTO from a services company shared how AI is creating new roles; for example, “AI Directors”—senior roles responsible for overseeing AI agents, approving deployments, and ensuring accountable use.

Outside the room, Moderna offers a public example of rethinking workforce structure. The company recently merged its HR and technology functions, appointing its chief people and digital officer to oversee 3,000 GPT-powered tools. This shift was intended to redesign workflows at scale and align people strategy with digital transformation.

2. Discovering internal hidden champions for AI

Several participants shared how traditional hiring processes often overlook those driving the most AI value. A board director from a consumer company described how internal usage data led them to uncover a high-performing AI user, who was later promoted to lead the company’s AI task force. These champions are often self-taught, identified and pulled into leadership roles not by title but by the impact they drive.

3. Rethinking AI leadership and structure

There is no one-size-fits-all solution now. Participants shared multiple operating models—from CIOs reporting directly to CEOs to cross-functional AI steering committees. In terms of structure, there are countless models, as illustrated in Figure 1.  In many cases, models are built around existing talent or talent availability.

Figure 1. Major Organizational Structures for AI

_{Source:  Russell Reynolds Associates analysis}

4. Organizational friction is the real bottleneck

AI strategy ownership is usually nested with the tech function (Figure 2). “When things get stuck, it's often not the technology,” said one tech board leader we spoke to. Often, it’s the human factors. Tech leaders usually find themselves navigating internal legal, compliance, data infrastructure, and finance—domains that lie well outside their day-to-day scope. It’s a time when boards should play a critical role in balancing long-term transformation with short-term performance.

Figure 2. GenAI strategy ownership (H1 2024)

_{Source: RRA H1 2024 Global Leadership Monitor, N=900 leaders}

5. Making the case for AI investment

Justifying AI investment remains difficult in organizations with fee-for-service models. One board member from a professional services firm described how their AI task force—comprised of the CEO, digital team, and business unit leaders—struggled to gain traction when success wasn’t credited toward utilization targets.

When it comes to measures of success of AI, leaders can demonstrate early wins that build confidence and momentum across the organization, with special focus on initiatives that directly impact customers, solve pressing business challenges, or tangibly contributes to the organizations strategic goals.

6. Governance starts with the board

Participants agreed: AI transformation demands a new level of collaboration and adaptability from leadership teams. CEOs must align their C-suite to adopt an enterprise-wide perspective, breaking down siloes to foster seamless cross-functional collaboration.

Having an enterprise mindset means prioritizing foundational AI literacy across all levels, using principles like “the 30% Rule,” which emphasizes sufficient understanding without requiring technical mastery (Figure 3). This includes tailored training for all levels of the organizations starting with the board of directors and leadership teams.

One company trained its board using real-time exercises, including a 30-minute generative AI pitch simulation, to help directors shift from curiosity to strategic understanding. But governance doesn’t end at the top. Several participants in highly regulated sectors—especially financial services—shared the importance of engaging audit and compliance teams early.

Figure 3. Best-in-Class Practice: Enterprise Mindset in Action

_{Source: Decoding the Future: The RRA Systems View on Leading Through AI Transformation | Russell Reynolds Associates}

7. Integration and iteration drive momentum

Participants emphasized that successful AI programs don’t wait for perfection—they launch, learn, and refine. A telecom executive described how their company established an internal AI studio that allowed employees to experiment, track adoption, and scale what works. Deployment is tied to specific business outcomes, like EBITDA growth or customer service productivity, with feedback loops connecting frontline users to technical teams. AI becomes a tool embedded in day-to-day workflows, not a parallel R&D stream.

Authors

Harry Lin is a member of Russell Reynolds Associates’ Technology Practice. He is based in Hong Kong.
Patricia Tan is a member of Russell Reynolds Associates’ Technology Practice. She is based in Singapore.
Justine Qin is a member of Russell Reynolds Associates’ Knowledge Management team in Asia Pacific. She is based in Beijing.
Caris Wong is a member of Russell Reynolds Associates’ CEO and Board Advisory Practice. She is based in Hong Kong.