The Executive AI Blueprint: Balancing Speed, Scale, Sovereignty and Spend
Overview
Organised by Avtica (f.k.a. Nmblr) in partnership with AWS, A*STAR, SGH and 1982 Ventures, the panel is a closed-room conversation for enterprise leaders who have moved past the question of whether to adopt AI and are now navigating the far harder questions of how to do so responsibly, economically, and at scale.
Attendance is curated and strictly capped. Applications are subject to confirmation. Participation is limited to C-Suite executives, Vice Presidents, and Enterprise Technology Leaders.
Panellists
The panel brings together practitioners from infrastructure, applied research, enterprise deployment, and international development — organisations that are building, procuring, and governing AI systems at scale across Asia and beyond.
Panellists
Cheryl Abundo (Principal Solutions Architect, Amazon Web Services)
Edward Khant Tun (R&D Group Manager, A*STAR)
Prof Adam Chee (Director of AI Development Office, Singapore General Hospital)
Winston Damarillo (CEO, Avtica)
Moderated by: Scott Krivokopich (Managing Partner, 1982 Ventures)
Orchestrated by: Wayne Wee (VP of Corp Dev & Strategy, Avtica)
Agenda
Reception: 6:00 PM to 6:45 PM
Panel Discussion: 6:45 PM to 7:45 PM
Networking: 7:45 PM to 9:00 PM
Light refreshments and drinks will be provided.
Attendance
This session is invitation-based and attendance is capped to preserve the quality of discussion. It is designed for C-Suite executives, Vice Presidents, and senior enterprise technology leaders operating at the intersection of AI strategy and enterprise accountability. Applications are subject to confirmation by the organising team.
The Conversation
Enterprise AI deployments are entering a new phase. Proof-of-concept fatigue is real. The organisations now pulling ahead are not those with the most ambitious AI strategies on paper — they are those that have made clear-eyed structural decisions about autonomy, cost, architecture, and jurisdiction. This session surfaces four of the most consequential trade-offs in that process.
Autonomy versus Accountability. As agentic AI systems move from experimental to operational, they introduce new questions about traceability and governance that existing enterprise frameworks were not built to answer. When an autonomous system acts — recommending, executing, or escalating — who is accountable, and how is that accountability demonstrated? The panel will examine what meaningful oversight architecture looks like in practice: agent registries, audit trails, and the organisational structures required to make accountability more than a policy statement.
Performance versus Total Cost of Ownership. Token pricing is the entry point of the conversation, not the conclusion. The real cost of enterprise AI includes inference infrastructure, fine-tuning and retraining cycles, integration overhead, human-in-the-loop operations, and the compounding cost of model drift over time. Leaders who evaluate AI investments against compute costs alone are systematically underestimating their exposure. This part of the discussion will address how to build a TCO framework that reflects operational reality rather than vendor benchmarks.
Speed Now versus Flexibility Later. The pressure to deploy quickly is genuine. Turnkey platforms and managed AI services reduce time-to-value, but they also introduce architectural lock-in that becomes structurally expensive to undo as requirements evolve. Modular, composable architectures demand more from engineering teams upfront and deliver optionality in return. The panel will examine how organisations are making this call — and what they have learned when they got it wrong.
Sovereignty versus Regional Scale. Southeast Asia is not a single regulatory environment. Data residency obligations, cross-border transfer restrictions, and localisation requirements vary materially across markets, and the pace of regulatory change is accelerating. Organisations seeking to operate at regional scale must reconcile that ambition with the compliance posture of each jurisdiction they touch. Panellists with direct operational experience across the region will address where the practical tensions lie and how leading enterprises are resolving them.
About
Amazon Web Services (AWS) began offering IT infrastructure services to businesses as web services—now commonly known as cloud computing. One of the key benefits of cloud computing is the opportunity to replace upfront capital infrastructure expenses with low variable costs that scale with your business. With the cloud, businesses no longer need to plan for and procure servers and other IT infrastructure weeks or months in advance. Instead, they can instantly spin up hundreds or thousands of servers in minutes and deliver results faster. Today, AWS provides a highly reliable, scalable, low-cost infrastructure platform in the cloud that powers hundreds of thousands of businesses in 190 countries around the world.
The Agency for Science, Technology and Research (A*STAR) is Singapore's lead public sector agency that spearheads economic oriented research to advance scientific discovery and develop innovative technology. Through open innovation, we collaborate with our partners in both the public and private sectors to benefit society. As a Science and Technology Organisation, A*STAR bridges the gap between academia and industry. Our research creates economic growth and jobs for Singapore, and enhances lives by contributing to societal benefits such as improving outcomes in healthcare, urban living, and sustainability.
Singapore General Hospital (SGH) is Singapore’s flagship hospital. SGH operates as a restructured hospital and is a not-for-profit institution with a long tradition of providing affordable tertiary healthcare. As the bedrock of medical education, SGH continues to play a key role in nurturing doctors, nurses and allied health professionals, and is committed to innovative translational and clinical research.
Avtica is an enterprise artificial intelligence (AI) orchestration platform that helps businesses connect multiple AI models, data systems, and workflows into a single, secure control plane.
1982 Ventures is a venture capital firm investing in early-stage opportunities across global enterprise AI and fintech. We are backing the boldest founders building the AI architecture, fintech rails, and automated workflows defining the future business.