EDITOR ’ S QUESTION
Richard Scott , Senior Vice President , Asia Pacific
& Japan , Informatica
AI is here to stay . While many organisations in the Asia-Pacific ( APAC ) region are already embracing AI , few are unlocking its full potential . The reality is that most organisations lack the foundational data infrastructure and strategy needed to support AI at scale . The gap between AI integration and reality is widening , and unless businesses address the foundational issue – data quality – AI projects will continue to underperform or even fail entirely .
A key issue is that for many organisations their data isn ’ t ready for AI . This is important because AI-ready data accelerates decision-making with real-time insights and predictive analytics , boosts operational efficiency and enhances competitiveness through AI innovations . It also enables easy integration with future technologies while improving data governance , reducing risk and maximising the value of data investments .
Are APAC organizations as prepared as they think they are ?
A leading analyst from Gartner said that at least 30 % of generative AI ( GenAI ) projects will be abandoned after proof of concept by the end of 2025 . The culprits ? Poor data quality , inadequate risk controls , escalating costs and unclear business value .
with 56 % of APAC data leaders admitting they struggle to balance over 1,000 data sources within their organisation .
Additionally , respondents reported facing other significant roadblocks , such as AI ethics ( 42 %) and data privacy and protection ( 42 %).
This disconnect is a significant concern in APAC , where data has become a vital asset that not only powers organisations but entire industries whether it ’ s finance , retail , healthcare , manufacturing or the public sector . Unfortunately , the surge in data generation has not been matched by corresponding and necessary investments in data management and governance by many .
A fundamental problem for organisations is that they approach AI readiness from a technology-first perspective , emphasising on acquiring the latest AI tools rather than building a solid data foundation . The fact is , AI models are inherently data hungry and require data from various sources , with high quality and transparency .
If the underlying data is inconsistent , inaccurate or poorly managed , even the most sophisticated AI tools can lead to critical pitfalls .
This highlights a fundamental truth : AI-readiness is only made possible with a high-quality , governed and accessible data foundation .
Yet , many organisations in APAC are struggling to meet this standard . In fact , recent research conducted by Informatica reveals that data fragmentation and complexity are major hurdles ,
AI models are only as good as the data they are trained on . Poor data quality can skew AI models , so if the data is incomplete , incorrect or scattered , this can propagate and amplify biases . As a result , models may produce flawed or misleading outcomes .
Fragmented data environments can lead to operational inefficiencies that can significantly increase the time
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