October 29, 2025
Meeting | Agenda | Members | Format
Digital Transformation in Asia
Asia’s digital economy rides on public rails (identity, payment, consent-based data sharing) — and more. It is propelled by fast‑cycling consumer markets (super‑apps, social commerce, real‑time payments), AI‑at‑scale innovation hubs (e.g., China’s applied AI in retail, logistics, fintech), dense supply‑chain ecosystems, and policy that moves quickly. The mix shifts by market, but the common thread is velocity — shorter build‑measure‑learn loops and a higher bar on convenience.
Questions
- Beyond public infrastructure, which demand‑side forces (consumer behavior, competitive intensity) are pushing AI adoption in our businesses?
- Where is AI real (delivering visible P&L impact in the last 6–12 months)?
- What Asia‑specific constraints or enablers (distribution, partnerships, regulators) materially drive our approach?
Architecting for Data & AI
Production-grade AI depends on clear data ownership, right‑sized models, and trustworthy data products. Teams are converging on lightweight governance like an AI Bill of Materials (AIBOM) — an inventory of the models, data sources, prompts, tools, and guardrails in use—so leaders know what runs where and who is accountable. Open Question: one model or many? Some enterprises standardize on a primary model for scale and cost, others diversify for resilience or domain performance. There isn’t a single answer — what matters is knowing why and managing trade‑offs (accuracy, latency, cost, compliance, vendor leverage).
Questions
- What’s our default stance today: one primary model with exceptions, or a portfolio strategy? What pushed us there?
- Which reference‑architecture elements are non‑negotiable (orchestration, retrieval/memory, tool‑use, eval/observability, cost controls, safety)?
- How are we meeting data‑localization/sovereignty rules without losing accuracy and speed (federated patterns, synthetic data, edge, multi‑cloud)?
AI and the Talent Imperative
AI is compressing entry‑level tasks across the enterprise — operations, finance, HR, risk, customer service, engineering (in addition to IT operations). Many are evolving from a pyramid to a diamond‑shaped workforce while redesigning jobs so juniors add distinctive value (judgment, customer interaction, exception handling) and mid‑career talent scales impact with AI. New roles emerge (AI product owners, evaluators, safety leads), but the center of gravity remains business accountability for outcomes.
Questions
- How are we adjusting entry‑level hiring and apprenticeship in each function without hollowing out the leadership pipeline — and where have we redesigned roles so junior talent contributes leverage (not just capacity); how are we measuring that?
- Which skills pathways (internal academies, certifications, rotations) are working, and how are we tracking AI fluency (usage, quality, impact) at the enterprise level?
- What governance and incentives align product, data, platform, and business while avoiding shadow IT and preserving speed?
Singapore Innovation Ecosystem — Lessons Learned
Singapore has run national‑scale programs on talent (training, apprenticeships), applied research (industry projects), and enterprise collaboration. We will briefly frame (with an external guest for this part of the discussion) what they do and how the journey unfolded, then focus on transferable lessons — what to emulate, where it struggled, and what doesn’t travel.
Questions
- From Singapore’s trajectory, what 3–5 learnings do we take away?
- How have training and apprenticeships changed enterprise readiness? Which elements are portable?
- Which policy or procurement patterns (templates, sandboxing, security reviews) actually reduced time‑to‑pilot and time‑to‑scale?
Dinner: Reflections in Conversation
At a more relaxed setting for dinner, we’ll take the opportunity to connect deeper. In addition, we will run a single round: what’s the one concrete actionable takeaway we each take away, captured for follow‑up at the next board meeting.
Executive Technology Board (c)
Board Meeting Logistics
Singapore Meeting PlannerSingapore Meeting FormatSingapore Meeting AgendaSingapore Meeting Members
Pre Read: Attending Members
Pre Read: AI and the Three Pillars of Success/w=3840,quality=90,fit=scale-down)
Pre Read: AI Agents & Agentic AI