Three years on since the introduction of ChatGPT and the continuing commoditization of artificial intelligence, Asia’s CIOs and CAIOs continue to face intensified AI pressures. Yet 79% of firms globally report inadequate GPU capacity for AI workloads – signalling critical infrastructure strain. Beyond hardware, fragmented data ecosystems threaten regional AI scalability.

Ben Canning, chief product officer at Alteryx, highlights the disconnect between AI ambition and operational data realities, urging APAC enterprises to prioritise foundational data integration over algorithmic hype to unlock sustainable AI value.

Given the importance of data in the function of AI systems, we have asked Canning to share with us his perspective on how data integration can spell the success or failure of AI adoption.

1. Briefly give us a state of AI adoption in Asia in 2025. What has worked and what has not worked? (second wave of AI adoption now)

2. What is the understanding of CIOs and CAIOs/Chief Data Officers when it comes to the relationship between data and AI? What about the rest of the C-suite leadership?

3. What makes APAC’s data-AI gap distinct from global counterparts, and how can it be bridged?

4. What metrics have proven data integration’s ROI in accelerating AI deployment?

5. How do regulatory variances across Asian economies impact integrated data strategies for AI?

6. How can APAC leaders better align their executive AI vision with legacy data infrastructure constraints?

7. Our topic is data integration as an enabler of successful AI adoption, given AI adoption trends, data sprawl, AI sprawl, governance issues, etc., what is your guidance for CIOs and CAIOs to ensure organisations are able to better data integration when it comes to AI adoption?