Asia-Pacific organisations are expected to boost spending on AI and generative AI fivefold, from US$73 billion in 2024 to reach US$370 billion by 2029, with a compound annual growth rate (CAGR) of 38.4 per cent, according to research firm IDC.
Generative AI is expected to be the fastest-growing segment, expanding at a 68.2 per cent CAGR to reach US$175 billion by 2029. By then, it is expected to comprise 47.4 per cent of total AI spending in the region.
This surge, revealed in an IDC report this week, marks a broader shift in the market, as organisations move from early experimentation to adopting AI across their operations.
According to the research firm, the region’s AI growth is driven by enterprise demand for scalable infrastructure, operational efficiency, and AI-enabled applications. However, this adoption will be affected by challenges around governance, cost, and integration.
The leading use case is expected to be AI infrastructure provisioning, contributing 39 per cent of total spending.
Vinayaka Venkatesh, senior market analyst for data an analytics at IDC Asia-Pacific, said the region’s AI market has moved beyond infrastructure-building. It is now in the phase of platform consolidation and building operational depth.
He added that organisations are prioritising AI platforms that are able to generate content, predict what may happen, and prescribe what action to take. The focus is on using AI agents and coordinating AI tools to scale enterprise-wide adoption.
IDC said a convergence of business priorities is fuelling the demand for AI. Companies are investing in AI to handle more complex workloads, improve efficiency and deliver more personalised customer experiences.
Rising demand for real-time analytics and security intelligence is also reinforcing AI’s role as a core business capability rather than an optional investment.
Agentic AI is emerging as another major force shaping the market. Enterprises are embedding more autonomous capabilities into applications and platforms, allowing AI systems to move beyond assisted decision-making toward executing tasks across workflows with less human intervention.
IDC expects the region’s AI market to continue expanding as organisations move from isolated use cases to more integrated, enterprise-wide ecosystems.
Spending is likely to shift further toward platforms that support orchestration, governance and scalability, though adoption in some markets may be tempered by cost pressures, regulatory requirements and talent shortage.
Industry AI adoption
By industry, software and information services are expected to remain the largest contributor, accounting for more than 47 per cent of AI spending in 2026. Growth in the sector is being driven by investments in development platforms, training infrastructure and intelligent applications.
Financial services firms are also broadening their use of AI beyond traditional fraud detection and risk management into areas such as autonomous advisory services, compliance automation and real-time decision-making.
Telecom and retail companies are increasingly embedding AI into their core operations, including predictive network management, intelligent customer routing, demand forecasting, dynamic pricing, and personalised commerce.
In the public sector, AI is increasingly used in national security and emergency response. Governments in the region are deploying AI for surveillance, predictive threat detection and real-time data fusion to improve situational awareness and crisis response.
IDC said infrastructure remains the largest area of investment because AI applications demand substantial computing power, data processing capability and scalable environments.
It added that software and information services, financial services, telecommunications and retail are among the sectors leading adoption, as firms look to improve decision-making, customer experience and efficiency.
Even as adoption accelerates, organisations still face risks around security and oversight. In Singapore, there remain significant security concerns, that have resulted in gaps between AI goals and oversight, while AI remains a top data security risk.
