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APAC Data Leaders Push Ahead with Generative AI Investments Despite ROI Challenges

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A majority of data leaders across the Asia-Pacific (APAC) region are ramping up investments in Generative AI (GenAI) and data management in 2025, despite ongoing challenges in demonstrating business value and return on investment (ROI), according to Informatica’s CDO Insights 2025 survey.

The annual study, which surveyed global data leaders across Europe, North America, and APAC, found that while APAC organisations are at the forefront of GenAI adoption, they face hurdles related to data privacy, AI ethics, and data reliability.

Key Findings: APAC Leads in GenAI Adoption

Balancing AI Innovation with Governance

The push for GenAI adoption comes amid mounting pressure from the C-suite, with 92% of APAC data leaders facing expectations to deliver quick ROI on AI initiatives. However, challenges related to data readiness, security, and compliance remain significant.

“One particular concern is attaining the readiness of their data that is required to achieve the transformative impact of AI,” said Richard Scott, SVP for Asia Pacific & Japan at Informatica. “A strong data foundation and strategy will be imperative to embedding trust in any company’s GenAI efforts.”

For sectors like insurance and finance, where GenAI offers opportunities for automation and digital transformation, responsible AI governance is crucial.

“At Income Insurance, our approach to data and AI integrates data governance and responsible AI frameworks, achieving a strategic balance between innovation and measured oversight,” said David Tan, Chief Data Officer at Income Insurance, Singapore. “A strong foundation fosters confidence in effective cross-functional collaboration, enabling capability development and delivering measurable impact through a risk-calibrated approach.”

The Complexity of GenAI Integration

While APAC data leaders aim to transition fully to the cloud within a year, nearly a third (32%) expect to use 15 or more tools to manage their data. This fragmentation could make GenAI implementation more complex, requiring organisations to streamline their data ecosystems and vendor relationships to avoid inefficiencies.

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