96% of APAC Organisations Are Betting Their Revenue Growth on AI This Year

96% of APAC Organisations Are Betting Their Revenue Growth on AI This Year

Something notable has shifted in the Asia-Pacific enterprise AI landscape in 2026: the conversation has moved decisively from "should we invest in AI?" to "how quickly can we scale what we've already started?" According to the latest data, 96% of organisations across the region are prioritising revenue growth through AI transformation this year, with a planned 15% average increase in AI investment across the board. What is driving this, and what does it mean for the companies that are still catching up?

The Infrastructure Race Is Reshaping Corporate Strategy

The context for this surge is enormous. Big Tech has committed over $650 billion to AI infrastructure in Asia-Pacific, creating a foundation of compute^✦ capacity, data centre real estate, and model availability that did not exist two years ago. Enterprises are now building on top of this base; and the investment decisions they are making reflect a fundamentally different understanding of AI's role in the business.

Forrester projects Asia-Pacific technology spending will grow 9.3% in 2026, with China's AI infrastructure alone exceeding $70 billion. That figure is not abstract: it represents data centres, GPU^✦ clusters, network infrastructure, and the human talent needed to operate them. For enterprises, it means that the cost of entry for serious AI deployment is falling while the potential upside is rising.

Sany's Transformation: A Manufacturing Bellwether

Perhaps the most striking example of enterprise AI commitment in the region is Chinese construction equipment giant Sany, which has announced a $4.2 billion investment to reposition itself as an AI-native construction technology company. The strategy involves a RMB 30 billion R&D commitment, fully integrated AI across its manufacturing and operations, and a shift towards electrification. In 2025, Sany recorded RMB 8.64 billion in new energy equipment sales; a signal that the transition is already generating revenue, not just strategic intent.

Sany's move is emblematic of how Asia's heavy industry is approaching AI: not as a software layer on top of existing processes, but as a fundamental redesign of how physical manufacturing works. The ADB's analysis of Asia's AI readiness divide noted that this kind of full-stack AI integration is most advanced among larger industrial firms with significant R&D capacity, and remains out of reach for many smaller manufacturers across the region.

Revenue growth through AI transformation is no longer a long-term aspiration for Asia-Pacific organisations — it is the dominant strategic priority for 2026.

AI-powered^✦ agentic^✦ workflows are transforming how organisations in Singapore approach customer service, with leaders prioritising skills development and automation at scale^✦.

Singapore's Agentic Customer Service Shift

In Singapore, the numbers are particularly striking for customer-facing AI applications. Salesforce reports that AI is projected to resolve 41% of customer service cases in Singapore by 2027; a dramatic increase that reflects the move from simple chatbot deflection to genuinely capable agentic AI. Singlife, the Singapore insurance company, has already deployed agentic AI as part of its customer service strategy, with leaders citing both cost efficiency and improved resolution rates.

This pattern, deploying AI agents to handle structured customer interactions while routing complex cases to humans, is becoming the dominant enterprise AI use case across the region. It is less glamorous than the frontier AI research headlines, but it represents where the actual business value is being realised today.

The Hybrid Infrastructure Imperative

One of the underreported constraints on enterprise AI adoption in APAC is data sovereignty. As AI models become central to business operations, the question of where training data is stored and processed becomes a board-level concern rather than an IT consideration. The record Q1 2026 venture funding figures partly reflect investor bets on companies building the hybrid infrastructure layer that lets enterprises keep sensitive data onshore while accessing global model capabilities.

For enterprise leaders, the practical implication is that AI infrastructure planning now requires simultaneous consideration of performance, cost, and regulatory compliance; a complexity that is driving demand for specialised advisory services and local cloud providers who can navigate jurisdiction-specific data rules.