Australia, New Zealand AI boom lacks data foundations

Boomi has released research showing many organisations in Australia and New Zealand lack the data architecture needed to generate measurable returns from artificial intelligence investments, despite strong adoption across both markets.

An Omdia survey of more than 1,100 senior technology and business decision-makers across Asia-Pacific found that 72 per cent of Australian organisations and 65 per cent of New Zealand organisations are already running active AI initiatives. Yet only 46 per cent in Australia and 39 per cent in New Zealand have a platform-led approach to integration, which the research identifies as a central requirement for applying AI across operations.

The results point to a gap between board-level expectations for AI and the systems needed to support them. In Australia, 93 per cent of respondents said AI-enabled automation would significantly reshape business processes within two to three years, while 86 per cent in New Zealand said the same.

At the same time, 28 per cent of Australian organisations and 34 per cent of New Zealand organisations said they were unable to measure the success of their AI initiatives effectively. That leaves a substantial share of companies without a clear way to assess whether spending is producing commercial results.

Budgeting also remains uneven. The research found that 39 per cent of Australian organisations and 43 per cent of New Zealand organisations do not have a dedicated AI budget, even though most respondents said they plan to maintain or increase investment over the next 18 to 24 months.

Those plans were strongest in core data and control functions. In Australia and New Zealand respectively, 92 per cent and 93 per cent said they planned to increase or maintain spending on data integration, preparation and orchestration. The equivalent figures were 90 per cent and 88 per cent for AI governance, risk and compliance, and 94 per cent and 91 per cent for data quality, security and privacy.

Integration gap

The findings suggest many organisations are pushing ahead with AI projects while still dealing with fragmented technology estates. In Australia, 85 per cent of respondents said they were actively seeking to reduce tool and technology sprawl, while 84 per cent said the same in New Zealand.

Consolidation efforts are already under way. In Australia, 90 per cent said they were consolidating across data, process integration, application programming interface management and automation, compared with 84 per cent in New Zealand.

That clean-up effort appears closely tied to AI deployment. As organisations add models, automation tools and data services, weak links between systems can make performance harder to track and governance harder to enforce.

David Irecki, Chief Technology Officer, APJ, at Boomi, said the main problem was not enthusiasm for AI but the state of the data behind it.

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

"The gap between adoption and ROI realisation stems from one fundamental issue: weak data foundations. Without unified integration, governance and data quality frameworks, each new AI initiative adds complexity rather than value."

Governance pressure

Data governance emerged as one of the clearest pressure points in the survey. In Australia, 94 per cent of organisations said data integration, access and governance were a key priority, while 89 per cent of New Zealand organisations said the same.

Most respondents also expect AI to sharpen that focus. The survey found that 90 per cent in Australia and 86 per cent in New Zealand believe AI initiatives will increase attention on data quality and governance policies.

Even so, formal policy frameworks remain limited. Only 49 per cent of Australian respondents and 38 per cent of New Zealand respondents said they had AI-specific data governance policies in place.

Unmanaged shadow integrations were also identified as a problem. Some 76 per cent of respondents in Australia and 72 per cent in New Zealand said those hidden or informal links between systems were disrupting data quality and confidence.

Michael Barnes, Chief Analyst, Enterprise IT Asia, at Omdia, linked that weakness directly to operational risk.

"Around nine out of 10 organisations we've surveyed cite governance as a priority, but less than half have formal policies in place," Barnes said.

"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."

Sovereignty concerns

The survey also highlighted a difference between the two markets on data residency. In Australia, 76 per cent of organisations said they had concerns about data sovereignty requirements, compared with 59 per cent in New Zealand.

Relatively few, however, said those concerns were having a significant effect on integration or AI strategy. The figure was 18 per cent in Australia and 14 per cent in New Zealand, suggesting many companies are still working through the operational implications rather than changing plans immediately.

Irecki said senior technology leaders were focusing on simpler environments and more reliable data as AI use moved beyond the trial phase.

"Scaling AI successfully depends on trusted, connected and governed data. CIOs and senior IT leaders are increasingly focused on simplifying fragmented environments, improving data quality and building the operational foundations required to support enterprise-scale AI," he said.

"The strong pace of AI adoption across Australia and New Zealand, and the significant investment plans for the next two years, show that organisations are moving beyond experimentation and into implementation. But they now need the right data foundations, integration capabilities and governance structures."

"Without this shift, organisations risk creating isolated AI activity without delivering measurable business outcomes. Governance, data quality and clear performance measurement are what transform AI deployments into sustainable business value, enabling organisations to translate adoption into productivity gains, operational efficiency and competitive advantage."