AI company Databricks has released a new Economist Impact report, Unlocking Enterprise AI: Opportunities and Strategies, which examines the challenges businesses face in adopting and scaling AI and the techniques they are using to drive greater value from these investments.
The report found ASEAN-based enterprises are leading in data intelligence with 74% training their large language models with enterprise data. In contrast, nearly half of data scientists (49%) across all of Asia-Pacific still rely on general-purpose large language models without contextual enterprise data.
These general-purpose models often lack the quality, governance, and evaluative capabilities that enterprise-specific data can bring.
As demand for data intelligence grows worldwide, AI remains a major focus area for companies. According to Goldman Sachs, global AI spend is expected to reach USD1 trillion in the next few years.
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 organisations from scaling their AI efforts and achieving more transformational results.
The Economist Impact 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, namely Malaysia, Philippines, Singapore and Thailand.
“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,” said Databricks’ Cecily Ng. “Yet enterprises remain cautious, balancing ambition with concerns around quality, cost, and implementation.”
“ASEAN organisations need AI platforms that prioritise data privacy, centralise governance, and deliver a sustainable total cost of ownership (TCO) at scale,” she added. “At Databricks, we’re bringing together data, analytics and GenAI that understands our customers’ unique businesses to deliver data intelligence. This report from Economist Impact showcases why data intelligence is essential, and why the winners in each industry will be those who take a holistic approach that encompasses data management, governance and domain-specific expertise.”
Additional key findings include:
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The vast majority of ASEAN enterprises (91%) are using GenAI in at least one function. However, only one in three (32%) believe their GenAI applications are production-ready. Respondents across the Asia Pacific cite key hurdles, including cost (40%), skills (38%), governance (38%) and quality (33%).
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Only 18% of ASEAN respondents believe AI is overhyped. In fact, 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.
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By 2027, 99% of all ASEAN respondents expect GenAI adoption across both internal and external use cases.
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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.
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Just 18% of ASEAN respondents are confident their organisation can secure enough AI talent.
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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.
“From classic machine learning to generative AI, the business world’s obsession with AI isn’t letting up,” said Tamzin Booth, editorial director of Economist Impact. “But our findings show that, for many organisations, the real value comes when the technology is unleashed on their own proprietary data to develop data intelligence.”
“That data intelligence is even more valuable in an increasingly unpredictable world,” she said. “To drive the algorithm advantage they’re seeking, it’s clear enterprises must address significant challenges with producing high-quality outputs, identify ways to evaluate performance and governance with large AI models, and work out how to effectively connect AI to the workforce.”
