KPMG: APAC AI Spending Is Rising Faster Than ROI Proof

Asia-Pacific companies are preparing to spend heavily on AI, but many still cannot prove what that spending is returning.

A new KPMG survey found that 70% of companies in the region plan to invest more than US$50 million in AI over the next 12 months. At the same time, only 5% said they have established ROI with demonstrated business outcomes.

According to KPMG, 81% of Asia-Pacific companies surveyed said AI is already delivering meaningful business value through productivity gains, cost savings, or revenue growth. That figure rose from 69% three months earlier.

Those numbers point to a measurement problem. Many executives see benefits from AI, but far fewer can prove return in a way finance teams can measure and defend.

KPMG surveyed more than 2,100 senior executives globally, including 521 respondents across Australia, China, India, Japan, South Korea, and Singapore. India reported the highest level of business value from AI at 89%, followed by Australia at 86%.

The region is also uneven on cost visibility. KPMG found that about 80% of APAC respondents have full or partial visibility into AI operating costs. Australia is further ahead, with 40% of companies monitoring AI costs fully and in real time, compared with 12% in South Korea.

AI value is easier to claim than prove

Singapore’s own AI adoption data shows how early the process still is for many firms. The Singapore Ministry of Manpower reported in April that 71.5% of private-sector establishments with at least 10 employees had not adopted AI. Only 3.8% had integrated AI into core business processes.

Adoption was much higher among larger firms. MOM found AI adoption rose from 23.9% among firms with fewer than 25 employees to 76.4% among the largest firms.

For companies already using AI, productivity remains the clearest reported gain. MOM said 70.7% of AI-adopting Singapore firms reported improved worker productivity, while fewer cited better decision-making or innovation.

Those gains still need to be tied to business results. KPMG said 55% of APAC companies had delayed or scaled back AI agent rollouts because operating costs began to exceed the value generated. That cost pressure becomes more important as companies move from simple chatbots to more expensive agentic systems.

AI investment is also still going into the basics. KPMG said APAC companies are putting AI budgets toward IT infrastructure, cyber and data security, operations, and transformation. Those investments may be necessary, but they do not always create quick, measurable ROI.

AI costs need workflow-level tracking

Accenture argues that AI cost management now needs a more specific discipline. In its tokenomics report, the firm defines tokenomics as the practice of connecting what AI consumes to the value it returns.

Token costs can concentrate quickly. Accenture said that in nearly every deployment it examined, fewer than 10% of users and workflows drove most of the AI bill. The firm also found that if token prices fell 25%, only 15% of organizations would actually bank the savings, with most reinvesting the difference into more AI use.

BCG makes a similar point in its work on return on AI, or RoAI. The firm says companies should track AI costs at the workflow level, including token use and human review time, then compare that cost with the business outcome produced.

For IT and finance leaders, the first move does not need to be complicated. Pick one expensive or high-value AI workflow, record the pre-AI baseline, and track token costs, human review time, quality, and business output over a set period. The review should end with a clear decision: scale it, redesign it, or stop it.

That discipline will become more important as OpenAI’s business adoption and enterprise AI use continue to grow. It also matters in markets where regulators are paying closer attention to Australia’s AI rules and how autonomous systems operate.

KPMG’s findings do not mean AI is failing in APAC. They show that enthusiasm, adoption, and reported productivity gains are ahead of formal measurement. Companies that close that measurement gap will have a clearer view of which AI projects deserve more budget and which ones should be cut before costs keep rising.

Also read: Rising AI agent cloud costs are making enterprise budgets harder to predict as companies move beyond simple chatbot deployments.