Three-quarters of senior technology leaders across Asia-Pacific are already running active artificial intelligence (AI) initiatives, but fewer than half have built the unified data platforms needed to make those investments pay off. A new research study commissioned by Boomi and conducted by Omdia surveyed more than 1,100 senior technology and business decision-makers across Australia, New Zealand, Singapore, Malaysia, and the Philippines, revealing a critical gap between AI adoption speed and the foundational infrastructure required for success.

What's Driving the AI Adoption Rush in APAC?

The appetite for AI transformation in Asia-Pacific is undeniable. The Omdia research found that 74% of organizations across the region are already running active AI initiatives. This reflects the global urgency many companies feel to stay competitive in a rapidly evolving technology landscape. However, this speed comes with a hidden cost: most organizations are treating AI as a technology spending category rather than a strategic business transformation that requires careful planning and infrastructure investment.

"APAC organisations are moving quickly on AI, but the research suggests that many organisations still appear to treat AI as an extension of broader technology spending rather than a strategic business transformation initiative," said David Irecki, Chief Technology Officer, APJ, Boomi.

David Irecki, Chief Technology Officer, APJ, Boomi

The disconnect between adoption and actual return on investment (ROI) stems from one fundamental problem: weak data foundations. Without unified integration, governance, and data quality frameworks, each new AI initiative adds complexity rather than value.

Why Are Data Foundations the Real Bottleneck?

The research uncovered a troubling pattern. While 94% of APAC organizations view data integration, access, and governance as a key priority, and 93% believe AI initiatives will increase focus on data quality and governance policies, only 46% currently have a platform-led approach to integration. This means nearly half of organizations are building AI systems on fragmented, uncontrolled data sources.

The consequences are severe. Most organizations, 81%, reported that unmanaged shadow integrations (unauthorized data systems operating outside official IT oversight) are disrupting data quality and confidence. When teams build AI models on data they don't fully control or orchestrate across systems, they lose visibility into what's feeding the models and whether that data is reliable.

"When teams are building AI models on data they don't fully control or orchestrate across systems, they lack visibility into what's feeding what. That gap becomes a real business risk," explained Michael Barnes, Chief Analyst, Enterprise IT Asia at Omdia.

Michael Barnes, Chief Analyst, Enterprise IT Asia at Omdia

How to Build a Sustainable AI Strategy in Your Organization

• Establish Formal Data Governance Policies: Only half of APAC organizations currently have formal AI-specific data governance policies in place. Creating clear rules about who can access data, how it's used, and how quality is maintained is essential before scaling AI initiatives.
• Unify Data Integration Across Systems: Moving beyond fragmented, siloed data sources to a platform-led approach to integration allows teams to see the full picture of their data landscape and build AI models on trusted, connected information.
• Address Shadow Integrations: Identify and manage unauthorized data systems that operate outside official IT oversight. These unmanaged integrations are a primary driver of data quality problems and should be brought under formal governance.
• Prioritize Data Quality Before Scaling AI: Before expanding AI initiatives across the business, invest in improving data quality and building the operational foundations required to support enterprise-scale AI deployment.

The research makes clear that scaling AI successfully depends on trusted, connected, and governed data. CIOs and senior IT leaders across the region are increasingly focused on simplifying fragmented environments and improving data quality as a prerequisite for AI success.

For organizations in APAC, the message is straightforward: slow down the AI initiative rollout just enough to build the data infrastructure first. The companies that take this approach will see better ROI, faster time to value, and fewer operational headaches down the road. Those that continue to treat AI as a standalone technology investment without addressing data foundations risk wasting significant resources on initiatives that never deliver the promised business impact.