A global survey from the AICPA, CIMA, and NC State’s ERM Initiative crystallises a stubborn truth: AI’s promise is matched by a widening readiness gap.
The study, drawing responses from 1,735 executives from across the executive rung, confirms that strategic gains from early AI adoption are accelerating for those who invest in people, processes, and governance; yet the majority of organisations remain underprepared to translate AI potential into reliable, scalable operations.
In Asia specifically, the picture is nuanced. Elevated regional momentum in sectors such as mining, professional services, and transportation reflects a strong appetite for data-driven automation and analytics.
Yet this momentum coexists with fragmented capabilities: only around a quarter of organisations report adequate AI-skilled talent, IT readiness, or regulatory preparedness.
Smaller firms are notably behind, suggesting a risk of widening competitive gaps within the region. By contrast, AI‑transformed entities — those that have already embedded AI into core operations — show markedly higher readiness across talent, technology, and regulatory domains, signalling a path for peers to emulate.
Leaders should view readiness as a revenue and risk management lever, not a compliance obligation. The report makes clear that AI risk and AI reward rise in tandem: boards are increasingly prioritising AI risk, and executives recognise that governance, talent pipelines, and robust infrastructure are no longer optional.
For Asia, this means aligning automation strategies with regional data-privacy regimes, cross-border data flows, and multi-cloud architectures. A deliberate plan—spanning data governance, model risk management, and operational resilience—can convert AI from a pilot project into a trusted, scalable capability.
Geography and industry divergence offer practical lessons. Regions in emerging markets within Asia report substantial strategic impact from AI, underscoring the urgency for managers to accelerate capability-building in data literacy, responsible AI use, and vendor risk management.
Across industries, those with dense data dependencies and complex workflows are leading the AI charge, reinforcing the necessity for integrated governance and end-to-end risk oversight. As AI risks evolve—from model drift to regulatory shifts—COOs in Asia must institutionalise cross-functional governance and continuous improvement to safeguard performance and resilience.
