Richard Scott, Senior Vice President, Asia Pacific & Japan, Informatica, on what more the APAC region needs to know – and do – about AI as the technology continues to evolve.
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.
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, 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.
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 and resources required to maintain AI systems. This issue is heightened in organisations that still rely on legacy systems and manual data processes.
APAC’s regulatory landscape is also ever evolving, with stricter data privacy laws and AI guardrails emerging in markets such as Australia and Singapore. AI systems that are not built on transparent and well-governed data could become vulnerable to compliance risks and legal penalties.
Ultimately, a robust data strategy should precede any AI strategy. Without a clear data governance framework and a strong foundation for reliable data, APAC organisations risk investing in AI technologies that fall short of their potential. The priority should be about getting data AI-ready.
How do organizations get their data AI-ready?
Preparing your data for AI is foundational for any organisation in APAC aiming to leverage advanced analytics and machine learning effectively. Here are some practical strategies to transition your data into AI-readiness:
- Evaluate and cleanse your data: Start by conducting a comprehensive evaluation of existing datasets to identify and correct inconsistencies, missing values, duplicates and inaccuracies. Clean, quality data is essential for the success of AI models.
- Build a robust data governance framework: Establish a strong data governance framework that aligns and remains current with regulatory standards and regulatory shifts in the region such as the Privacy Act in Australia or the Personal Data Protection Act in Singapore. A well-defined governance model will ensure data integrity, compliance and security. This can serve as a roadmap for AI initiatives, aligning them with business objectives and ethical standards.
- Invest in scalable infrastructure: As AI projects evolve from pilots to full-scale implementations, scalable infrastructure becomes paramount. Migrating workloads to the cloud enables efficient data processing and storage, but also promotes business continuity and reduces the cost of ownership associated with current management practices.
- Promote a culture of data literacy: Foster a culture of data literacy at all organisational levels. Provide continuous learning opportunities to enable employees to effectively understand and utilise data in their roles, while democratising data across organisations for quicker decision making and insights.
For APAC organisations, the challenge is clear: business leaders need to shift their focus from implementing AI technologies to building a solid data foundation that supports AI. This means investing in data management, improving data quality and fostering a data-centric culture. Organisations can then drive transformative change and adopt an AI-ready innovation culture to drive business value.
