ASEAN enterprises are leading in data intelligence with 74% training their large language models (LLMs) with enterprise data according to an Economist Impact report from Databricks.
In contrast, nearly half of data scientists (49%) across all of APAC still rely on general-purpose LLMs without contextual enterprise data. These general-purpose models often lack the quality, governance, and evaluative capabilities that enterprise-specific data can bring.
The report surveyed 1,100 technical executives and technologists from 19 countries across Asia, Europe and the Americas and included insights from 100 respondents from ASEAN countries — Malaysia, the Philippines, Singapore and Thailand.
Among the organisations represented are Accenture, CJ CheilJedang, Condé Nast, Dream Sports, Fanatics Betting & Gaming, Flo Health, Frontier, General Motors, HP, JetBlue, Mahindra Group, Mastercard, Molson Coors, Novartis, NTT Docomo, Opendoor, Providence, Rakuten Group, Repsol, Rivian, Seven West Media, Shell, Siam Commercial Bank, TD Bank Group, Thermo Fisher Scientific, Unilever, UPS and the United States Army.
Databricks said that while more companies are investing in AI than ever before, struggles related to delivering business-specific, highly accurate, and well-governed results at a reasonable cost prevent organizations from scaling their AI efforts and achieving more transformational results.
“It’s clear that AI is becoming an integral part of every business, and the technology is emerging as a critical driver of business growth. Yet enterprises remain cautious, balancing ambition with concerns around quality, cost, and implementation,” said Cecily Ng, Databricks VP and general manager in ASEAN.
Ng said ASEAN organisations need AI platforms that prioritise data privacy, centralise governance, and deliver a sustainable total cost of ownership (TCO) at scale.
Findings also show that the vast majority of ASEAN enterprises (91%) are using generative AI in at least one function. However, only one in three (32%) believe their generative AI applications are production-ready. Respondents across the Asia Pacific cite key hurdles, including cost (40%), skills (38%), governance (38%) and quality (33%).
Only 18% of ASEAN respondents believe AI is overhyped, but 77% see the technology as crucial to their long-term goals. Despite the momentum, 37% believe investment across technical and non-technical domains is insufficient.
By 2027, 99% of all ASEAN respondents expect generative AI adoption across both internal and external use cases.
ASEAN organisations expect to mix and match different models and tools in their Agent Systems, spanning open source and proprietary technologies, to drive better performance. By 2027, 94% plan to deploy open source AI models.
Just 18% of ASEAN respondents are confident their organisation can secure enough AI talent.
Only 20% of ASEAN respondents strongly agree that their organisation’s data and AI governance are sufficient.Enterprises face challenges with fragmented data estates, complicating discovery, access permissions, data usage, audits and sharing. Governing AI models and tools is also vital to meet evolving AI regulations. To succeed, enterprises need a unified and open governance approach.
“Our findings show that, for many organisations, the real value (of AI) comes when the technology is unleashed on their own proprietary data to develop data intelligence,” said Tamzin Booth, editorial director of Economist Impact.
