Based on in-depth interviews with 1,200 C-suite executives, Asia-Pacific Link reveals the critical pathways for generative AI to move from pilot to production-scale value.
Based on in-depth interviews with 1,200 C-suite executives, Asia-Pacific Link reveals the critical pathways for generative AI to move from pilot to production-scale value.
In 2024-2025, most enterprises began experimenting with generative AI, yet fewer than 12% achieved cross-functional scale. Our research reveals three critical differences between leaders and laggards: how they positioned AI at the strategic level, the maturity of their data assets, and the speed of organizational change.
81% of leading enterprises have elevated AI to a board-level agenda, directly sponsored by the CEO or a dedicated CDAO (Chief Data & AI Officer). By contrast, 62% of laggards still leave AI to IT or digital departments — a structural problem that prevents AI programs from getting the cross-BU priority and resources they require.
Generative AI is the largest technology inflection point in 20 years. If the CEO doesn't lead it personally, the company's future is left to chance. — Global AI Chair, Asia-Pacific Link Consulting
Leading enterprises allocate 60%+ of AI budget to data infrastructure — data platforms, feature engineering, governance and privacy computing. Laggards fall into the trap of buying models while neglecting the moat: data assets.
Scaled AI adopters spend 1.8x more on talent, structure and process redesign than on pure technology. The bottleneck is not compute — it is organization.
In banking, top institutions have deployed AI across 300+ use cases spanning credit, anti-fraud, service and knowledge management, saving 8-12% annually. In pharma, AI-driven target discovery has shortened preclinical R&D by 30-40%. In manufacturing, AI visual quality inspection systems achieve 15-25% yield improvement.