Artificial intelligence has become one of the few technologies that organizations simultaneously overestimate and underprepare for. Across Asia-Pacific, investment levels suggest extraordinary confidence in AI's ability to reshape enterprise performance. The organizational foundations required to realize that value tell a rather different story.
KPMG's inaugural Global AI Pulse survey, published in March 2026 and drawing on more than 2,100 senior leaders across 20 countries, found that organizations across Asia-Pacific plan to invest an average of US$245 million in AI over the next twelve months. That figure exceeds the Americas (US$178 million), EMEA (US$157 million), and the global weighted average (US$186 million).
The commitment is real, and so is the gap. IDC forecasts that by the end of 2026, 45% of AI-fuelled digital use cases across Asia-Pacific and Japan will fail to meet their return-on-investment targets. More interestingly, IDC attributes those failures primarily to poor data foundations and unclear value realization rather than shortcomings in the technology itself.
That distinction matters because it suggests the region does not have an AI investment problem. It has an AI execution problem. And those are solved in very different ways. Pursuing AI for its own sake is rarely transformative. More often, it creates the appearance of progress without addressing the underlying conditions required to sustain it.
The Proof Gap
The instinct when AI initiatives disappoint is to look for technical explanations. The model was inadequate. The implementation was rushed. The vendor overpromised. The platform failed to scale. Each of these explanations contains some truth. That said, Grant Thornton's AI Impact Survey 2026 points to a more fundamental issue. In many organizations, approval arrived before accountability did.
Boards approved investment without establishing governance expectations. Leadership teams deployed AI capabilities without clearly defining ownership. Programs scaled before anyone agreed on how success would be measured beyond activity metrics and pilot-stage enthusiasm. The result is what the survey describes as a growing proof gap. The uncomfortable implication is that the gap between AI investment and AI outcomes is not primarily a technology problem and therefore cannot be solved by technology choices alone.
IDC's data makes this challenge visible at scale. An estimated 75% of organizations across Asia-Pacific and Japan have already deployed agentic AI in some form. And 61% of CEOs identify agentic and generative AI as their highest-priority investment area. Yet more than a third of organizations remain stuck in experimentation and isolated point solutions, unable to transition into enterprise-scale deployment.
Investment has moved quickly. Governance, on the other hand, appears to have taken a more reflective approach. Those who have spent time leading creative, marketing and production operations across regulated, matrixed, multi-market environments will recognize the pattern immediately.
What Works
The organizations generating meaningful returns from AI are rarely those that moved first or fastest. Nor are they necessarily those with the most sophisticated tools. More often, they are the organizations that established clarity before scale. They defined success at the workflow level rather than at the strategy-slide level. They built measurement frameworks before widespread deployment. They invested in data discipline before investing in AI ambition. And they assigned ownership before assigning budgets.
None of this tends to headline keynote presentations. What it is, however, is remarkably effective. This is why conversations about AI increasingly need to shift from capability toward operating models. The real question is no longer whether organizations can access AI – most already do. The more important question is whether they can integrate AI coherently into how decisions are made, work is executed, and value is measured. That is a fundamentally different challenge.
Gartner's forecast of $2.59 trillion in global AI spending during 2026, representing a 47% increase year-on-year, arrives just as the technology sits firmly in the so-called Trough of Disillusionment. The juxtaposition is revealing. Capital continues to flow aggressively into AI while many organizations still struggle to demonstrate value at scale. Asia-Pacific now finds itself at the center of that tension.
The region's opportunity is significant. IDC projects that by 2030, half of all new economic value generated by digital businesses in Asia-Pacific will come from organizations investing in AI today. But that future will not be determined solely by investment levels. It will be determined by execution. The organizations that succeed will not necessarily be those that spend the most or move the fastest.
Enterprise history is littered with examples of organizations confusing early adoption with durable advantage. They will be the ones that treat governance, measurement, accountability and data discipline as prerequisites rather than afterthoughts. They will also recognize that the first wave of productivity gains is merely the entry fee. Competitive advantage emerges when AI reshapes how work is organized, measured and scaled.
The organizations seeing genuine impact treat AI as an operating-model shift rather than a technology deployment, redesigning how people and machines work together rather than removing expert judgment from the loop. The point holds well beyond the legal operations in which he framed it. The first wave of productivity gains is merely the entry fee. Once automation becomes the baseline, advantage moves to the last mile, the point at which client value is actually created, and to how well an organization orchestrates everything around it.
The gap between AI investment and AI outcomes is not a reason to moderate ambition. It is a reminder that ambition, without the organizational infrastructure to support it, risks becoming expensive disappointment. For Asia-Pacific, the path forward lies not in spending more, but in building the governance and execution frameworks that turn investment into impact.
