At GIC Insights 2025, senior business and technology leaders discussed how artificial intelligence (AI) is transforming customer experience, data strategy, infrastructure, talent needs, and leadership. The panel addressed practical challenges such as data sovereignty, physical and regulatory constraints, the rise of inference computing, and the importance of adaptability in an AI-driven world.
Held on 18 November 2025 and moderated by Mark Ong, Chief Investment Officer, Public Equities, GIC, the panel featured:
- Ewout Steenbergen, Executive Vice President and Chief Financial Officer, Booking Holdings
- Adaire Fox-Martin, CEO and President, Equinix
- Rick Wallace, President and CEO, KLA Corporation
This article presents the key takeaways from the discussion.
Note: In accordance with the Chatham House Rule, none of the insights reflect the views of or should be attributed to any single organisation or individual. Views expressed in this article may not be shared by all panellists.
Transactional to relationship-based customer service
The panel observed that AI is fundamentally transforming customer experience, moving businesses away from transactional, low-frequency interactions towards relationship-driven, high-frequency engagement. In sectors such as travel, AI is being deployed to simplify complex logistics and anticipate customer needs, resulting in higher satisfaction and loyalty. For example, travel platforms are leveraging AI to personalise itineraries, proactively manage bookings, and offer tailored recommendations, thereby increasing customer lifetime value and direct engagement.
This shift is not limited to consumer-facing industries—a poll of over 400 C-suite guests at GIC Insights identified healthcare, finance, and software as the sectors where AI will create the most disruptive value. AI is enabling granular personalisation, allowing organisations to respond to individual preferences and behaviours in real time. Such capabilities are driving increased engagement and retention, with economic benefits accruing from greater customer loyalty and reduced operational costs.
Data sovereignty and competitive advantage
While data is foundational to AI, the panel highlighted that it is the quality and uniqueness of proprietary data that truly confers a sustainable edge. Increasingly, organisations are prioritising data sovereignty and privacy, moving beyond reliance on public cloud infrastructure towards more complex multi-cloud strategies and private colocation facilities. This approach allows firms to retain control over their intellectual property (IP) and ensure compliance with regulatory requirements.
Harnessing proprietary data, especially for high-frequency, high-value decisions such as dynamic pricing, was cited as a key factor in delivering differentiated value to clients and in building and sustaining competitive advantage. As AI models become more sophisticated, the importance of data privacy and security is only set to grow, with companies investing in infrastructure that supports both innovation and data protection. Audience sentiment echoed these concerns, with data, adoption, and talent identified as the greatest long-term risks to their companies’ AI strategies.
Inference computing: The next frontier
Looking ahead, the panel discussed the shift from training- to inference-focused computing in AI. While emphasis has been placed on the resources needed to train large models, one of the panellists highlighted that inference—using pre-trained models to generate predictions from new data—will soon dominate data centre demand, reshaping infrastructure requirements and creating opportunities for new computing architectures. This transition has significant implications for both technology providers and end users, requiring organisations to adapt their infrastructure strategies to balance performance, cost, and energy efficiency.
Physical and regulatory constraints
Despite the optimism surrounding AI’s potential, the panel acknowledged significant physical and regulatory constraints that are likely to moderate the pace of adoption. Semiconductor supply bottlenecks, material shortages, and data centre construction timelines present tangible challenges. For instance, current production capacity for advanced chips is struggling to keep pace with the ambitious AI deployment plans announced globally, leading to significant supply constraints.
Regulatory regimes also play a crucial role in shaping the AI landscape. Markets with robust rule of law and intellectual property protection are more conducive to innovation, while those with weaker frameworks pose risks of IP theft and diminished competitive advantage. Singapore’s robust legal environment and skilled workforce were referenced as key factors in attracting high-value AI manufacturing and research operations.
The underrated role of skilled trades
While much of the AI conversation centres on the risk of widespread job displacement and the impact on knowledge workers, the panel drew attention to a less recognised but increasingly critical issue—the growing scarcity of skilled trades supporting AI infrastructure.
As data centres and high-performance computing facilities proliferate, demand for engineers, technicians, and construction teams is rising. These roles are critical to maintaining the physical backbone of the digital economy, and their shortage could become a bottleneck for AI deployment.
Adaptability: The new leadership currency
The panel argued that the most critical transformation in the AI era is not technological but human. Human willingness to cede control to AI agents may prove the ultimate barrier to adoption. The acceptance of autonomous digital assistants, capable of managing personal and professional tasks, remains uneven, with trust and human agency at the forefront of concerns. Organisations must be mindful of varying user comfort and preferences as they design future experiences.
Leadership, the panellists suggested, will require a shift from traditional intelligence metrics to adaptability.
The concept of the 'adaptability quotient’ (AQ) was discussed as a key metric for future leaders, with the ability to pivot and respond to uncertainty becoming more valuable than IQ or EQ alone. Managers must evolve from overseeing tools to collaborating with AI colleagues, ensuring human judgement remains central to decision-making.
Pragmatism and long-term value
The panel concluded that AI delivers the greatest impact when applied to areas with clear, measurable value, rather than through indiscriminate adoption across the enterprise. Instead of pursuing short-term gains, organisations should focus on integrating AI into core products and processes, recognising that productivity improvements will take time to materialise. Those able to remain agile in the face of short-term regulatory or supply chain disruptions, while retaining control over their data and models, are best positioned to capture AI’s long-term benefits.
