Singapore – A growing divide in AI readiness is emerging across the Asia Pacific, with legacy technology identified as a primary constraint on progress. The latest IDC report commissioned by MongoDB indicates that 43% of organisations in the region are unable to develop new applications without significant upgrades to their existing systems, which are often considered inflexible, expensive to maintain, and too slow to meet current demands.
The report highlights a clear disparity between organisations that have prioritised modernisation and those that have not. A smaller group of more advanced adopters is generating substantially higher digital income, with digital revenue accounting for 71% of total earnings compared with 23% among their peers. This performance gap is linked to sustained investment in updating core systems and reducing reliance on outdated infrastructure.
“High-quality, integrated data is the essential fuel that determines the accuracy and performance of an AI application, making modern data architecture a foundational element of any AI strategy,” Dr William Lee, Senior Research Director, Service Provider and Core Infrastructure Research at IDC Asia Pacific, stated.
“But research shows that many organisations are being held back by their existing rigid legacy architectures that do not have the flexibility and scalability to handle the high volume of unstructured data required for AI.”
Challenges related to data remain central to the issue. Many organisations report difficulties with managing and maintaining high-quality data, while others cite limitations in existing database technologies that are not suited to AI workloads. Integrating security measures into development processes without slowing innovation also continues to present difficulties.
The report further suggests that modernisation efforts are frequently unsuccessful. Approximately 90% of organisations have experienced failed initiatives, with fragmented and low-quality data identified as a leading cause. Broader findings from the study reinforce that poor data management contributes significantly to delays and underperformance in technology projects, often resulting in higher costs and inefficiencies.
“AI has made technical debt an urgent board-level priority. “The research is clear, strategic modernisation unlocks AI opportunities and supports a significant increase in revenue,” Thorsten Walther, Managing Director, CXO Advisory at MongoDB, commented.
“The leaders across the region are showing what’s possible when organisations ditch rigid, siloed legacy systems and move to AI-ready data platforms like MongoDB.”
Despite these challenges, demand for AI capabilities is accelerating efforts to modernise. Nearly half of organisations identify support for AI initiatives as a key motivation for updating applications and databases. However, persistent technical debt and legacy constraints continue to slow progress, creating a gap between strategic ambition and operational readiness.
Looking ahead, organisations that do not address these issues are expected to face increased risks. Projections indicate that failure to tackle technical debt could lead to significantly higher failure rates in AI initiatives, alongside rising implementation costs. In contrast, those adopting a long-term approach to modernisation, supported by stronger data practices and cloud-ready infrastructure, are better positioned to realise the commercial benefits of AI.
