Governance delays AI projects as APAC enterprises move workloads out of public cloud
Cloudera survey reveals Asia Pacific firms are shifting AI workloads to private infrastructure as security, compliance, and rising costs delay key deployments.
Enterprises across the Asia Pacific region are actively changing where their artificial intelligence workloads run as governance, compliance, and infrastructure constraints complicate deployment, according to research commissioned by Cloudera. The study found that 92% of APAC organisations have delayed or cancelled at least one AI project over the past year due to governance, compliance, or regulatory challenges, prompting 64% to move AI workloads from public cloud environments to private cloud or on-premises infrastructure.
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Cloudera’s research surveyed 1,500 enterprise architects, cloud infrastructure leads, and data architects across nine global markets. Globally, 77% of respondents reported that their organisations were actively using AI. However, 95% indicated that they had delayed or cancelled AI initiatives because of governance, compliance, or regulatory issues.
Infrastructure constraints are placing additional pressure on these deployment decisions. Globally, 84% of respondents reported higher costs arising from AI workloads, while 72% noted that their existing data architecture required significant changes to meet future AI demands, a figure that reached 68% across APAC.
Governance problems are slowing deployment
Managing data grows increasingly complex when AI workloads draw on information spread across multiple computing environments. Globally, 73% of respondents stated that AI had made data governance more complex, compared with 61% across the APAC region. These governance difficulties are directly slowing deployment timelines, with 55% of organisations globally delaying or cancelling more than six AI projects during the past 12 months because of governance, compliance, or regulatory challenges.
Across APAC, 46% of respondents identified data security, governance, and compliance requirements as the primary reason for changing their AI infrastructure. Data movement introduces further complexity into this operational environment.
A striking 97% of respondents globally reported that their organisations moved data between environments at least once a month, spanning public cloud, private cloud, on-premises systems, and edge infrastructure. Applying uniform governance and security controls across these varied setups becomes increasingly critical as AI systems rely on data stored in different physical locations.
More AI workloads are moving out of public cloud
The survey highlights a broader trend of enterprises reconsidering where individual AI workloads should run. Globally, 66% of respondents moved AI workloads from public cloud environments to private cloud or on-premises infrastructure over the past year, with APAC close behind at 64%.
Investment is now being distributed across cloud, on-premises, edge, and hybrid environments. One-quarter of respondents globally said their organisations plan to prioritise a hybrid-first architecture over the next two years. AI is also reshaping broader infrastructure strategies, with 75% of respondents stating that AI integrations had altered their organisation’s data storage and architecture practices as higher infrastructure costs influence workload placement.
Singapore enterprises reassess workload placement
Singapore respondents reported similar shifts in their operational strategies. Some 43% stated that their organisations had moved AI workloads out of the public cloud, while another 36% were actively evaluating such a move. Edge infrastructure is also attracting greater spending in the market, with 35% expecting investment to increase over the next two years and 41% identifying data security, governance, and compliance as the main reasons for modifying their AI setup.
“Across the Asia Pacific region, the conversation is transitioning from merely selecting an AI host to determining which environment yields the superior business results for specific workloads,” observed Remus Lim, Senior Vice President, Asia Pacific & Japan at Cloudera. “Singapore reflects this evolution as enterprises collectively evaluate latency, performance, expenditure, and governance when defining their AI deployment strategies. To scale AI breakthroughs effectively, businesses must possess the agility to transition data and AI seamlessly among cloud, edge, and on-premises setups while maintaining strict governance and operational command.”
