Boomi Finds APAC Data Gaps Threaten AI ROI

Boomi announced new research, commissioned to Omdia, that surveyed more than 1,100 senior technology and business decision-makers across Australia, New Zealand, Singapore, Malaysia, and the Philippines, reporting that 74% of organisations are running active AI initiatives (Boomi press release / Business Wire). The survey found only 46% currently use a platform-led approach to integration and that nearly a quarter of respondents said they are unable to effectively measure the success of AI initiatives (Omdia via Boomi). The study also reports 89% are seeking to reduce tool and technology sprawl and 92% are consolidating across data, process integration, API management, and automation (Boomi press release).

Omdia respondents flagged data integration, access, and governance as priorities, with 94% viewing those areas as key and 93% saying AI initiatives will increase focus on data quality and governance policies, yet only 50% reported formal AI-specific data governance policies in place (Omdia via Boomi). The survey further reports 81% of organisations see unmanaged shadow integrations as disrupting data quality and confidence, and 76% view data residency as a concern even though 24% say residency materially affects strategy (Melbourne-Insider summary of the Omdia data).

"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 (press release). Michael Barnes, Chief Analyst, Enterprise IT Asia at Omdia, is quoted warning about model development on poorly controlled data, saying teams lack visibility into data lineage and its business impact (Omdia quote in press release).

Editorial analysis: Companies and practitioners building production AI pipelines commonly encounter friction when integration is fragmented, governance is immature, and shadow IT proliferates. In comparable enterprise surveys, those gaps correlate with longer model deployment cycles, higher costs to remediate data issues, and weaker ability to measure downstream ROI.

Editorial analysis: The Omdia findings, as distributed by Boomi, underscore a recurring pattern where rapid AI adoption outpaces investment in data engineering and governance. For organisations across APAC, the combination of high AI project incidence (74%) with low formal governance coverage (50%) raises the risk that many projects will deliver limited, hard-to-measure business value despite heavy tooling and automation efforts.

Editorial analysis: Observers and practitioners should track three indicators: uptake of platform-led integration and API management solutions across APAC, the share of organisations publishing AI-specific data governance policies, and metrics teams publish to demonstrate measurable AI outcomes. Public vendor procurement announcements and follow-up industry surveys will show whether consolidation efforts reported by 89%-92% of respondents translate into durable architecture and measurement improvements.

Editorial analysis: Teams evaluating or running AI initiatives should prioritise observable metrics for data quality, lineage, and integration health. Industry experience shows that establishing those telemetry and governance primitives earlier reduces rework during model production and simplifies ROI measurement across stakeholders.