Asia-Pacific businesses are taking up AI in a faster pace than global rivals, though core issues such as data quality still bug some of these early adopters, according to a Hitachi Vantara study released recently.
In advanced markets such as Singapore, up to 57 per cent of organisations consider AI critical to their operations, compared to the global average of 37 per cent, revealed the enterprise storage system vendor.
However, data quality remains an issue. Only 30 per cent of data is structured, and AI models produce accurate outputs just 32 per cent of the time. Plus, messy or siloed data remains a key issue in the region.
While AI has enjoyed initial wins in operational optimisation and enhanced customer engagement, other “low hanging fruit” projects that involve maintenance, demand forecasting, intelligent automation, and conversational AI will need better data quality to succeed, says Lawrence Yeo, solutions director for Asean for Hitachi Vantara.
“Organisations that clean and govern their data more effectively will see faster returns,” he tells Techgoondu in this month’s Q&A. “The priority should be on scalable, measurable use cases rather than moonshot AI ambitions.”
NOTE: Responses have been edited for style.
Q: How have businesses in Asia-Pacific done so far in terms of getting infrastructure ready for AI?
A: Businesses in Asia-Pacific are outpacing global counterparts when it comes to embedding AI into core operations. According to Hitachi Vantara’s latest research, 42 per cent of organisations in Asia now consider AI critical to their operations; above the global average of 37 per cent. In markets like Singapore and China, this figure rises to 57 per cent and 53 per cent respectively.
However, many organisations are still struggling with foundational challenges. Only 30 per cent of data is structured, and AI models produce accurate outputs just 32 per cent of the time.
These figures highlight that while adoption is high, the infrastructure and data maturity required for effective AI are still evolving. Data quality, availability, and security remain key barriers.
Q: This is said to be the year that AI will bring real results. Are we seeing some already?
A: Yes, early signs of real impact are visible, especially among leading adopters in Asia who are pairing AI efforts with strong data practices. In fact, 40 per cent of successful AI adopters in the region attribute their results to high-quality data, while others credit partnerships with external experts and robust project governance.
That said, widespread success is not yet the norm. Many organisations remain stuck in transition, with data quality and model reliability still limiting outcomes.
The good news is that the groundwork is being laid. More than 70 per cent of enterprises in Asia are hiring for AI-relevant roles, and 68 per cent are working with external experts to accelerate maturity. We expect to see more measurable value emerging as these investments take root.
Q: What types of AI successes can we expect in the next 12 to 18 months? The low-hanging fruit?
In the near term, AI gains will come from operational optimisation and enhanced customer engagement; areas where small improvements can deliver meaningful ROI.
Expect to see growth in predictive maintenance, demand forecasting, intelligent automation, and conversational AI in sectors like manufacturing, retail, logistics, and financial services.
These “low-hanging fruit” projects rely heavily on improving data availability and reducing noise in input data. Organisations that clean and govern their data more effectively will see faster returns. The priority should be on scalable, measurable use cases rather than moonshot AI ambitions.
Q: Given how fast AI has evolved in a short time – DeepSeek is a good example – how should businesses invest their resources in getting AI to enhance workflow and their products and services?
A: To keep pace with AI’s rapid evolution, businesses must invest in three critical areas – data readiness, skills development, and trusted partnerships.
First, prioritise cleaning, structuring, and governing data to ensure it’s usable by AI systems – messy or siloed data remains one of the biggest blockers in Asia.
Second, build internal expertise while also tapping into external knowledge: 71 per cent of Asian firms are already hiring AI talent, and 68 per cent are bringing in external specialists.
Finally, align AI investments with business outcomes. Focus on augmenting workflows, not replacing them, such as streamlining decision-making or automating repetitive processes. Enterprises should also ensure infrastructure choices – such as storage, compute, software – are scalable and secure to support evolving AI workloads.
