An AI readiness gap is emerging across the Asia-Pacific region, with legacy architecture cited as the primary barrier to AI success, according to new IDC research commissioned by MongoDB.
Results are based on an online survey of 1,400 organizations with at least 100 employees across Australia, China, Hong Kong, India, Indonesia, Singapore, South Korea, and Thailand, conducted in late 2025.
Half of respondents were developers and half were IT decision-makers, which included primary decision-makers and members of decision-making units.
IDC has predicted that organizations who fail to address technical debt will face 50% higher failure rates and rising costs for their AI initiatives by 2027.
The research found that nearly half (43%) of the region’s organisations say their existing architecture makes it impossible to build new applications without extensive modernization because it is too rigid, costly, and slow for today’s requirements.
However, there is a cohort of leaders who are generating three times more digital revenue (71%) than their mainstream peers (23%) by successfully investing in strategic modernization programmes to escape their legacy architecture.
“The stakes for modernization are now critical. 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,” said William Lee, senior research director at IDC Asia Pacific.
“But research shows that many organizations 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,” said Lee.
The gap between AI ambition and reality is most visible at the data layer. The top three challenges in software development identified in the research were data management and poor quality data (32%), outdated database technology that does not support the demands of AI workloads (31%), and embedding security into the development process without impacting speed or innovation (31%).
Support for new AI initiatives was the number one driver for modernizing databases and applications in the Asia Pacific region, cited by 46% of organizations.
However, almost all organizations (90%) have experienced failed modernization initiatives, with siloed and poor-quality data cited as the major obstacle.
By contrast, the cohort of companies the research identified as ‘Leaders’ treat modernization as an ongoing discipline and long term investment. Among these, 58% are running multiple programs to continually address legacy constraints and build cloud-ready foundations that can support production AI.
“AI has made technical debt an urgent board-level priority,” said Thorsten Walther, managing director, CXO Advisory at MongoDB. “The research is clear, strategic modernisation unlocks AI opportunities and supports a significant increase in revenue.”
One example of an organization leading the way in AI and modernization is Bendigo Bank in Australia. The bank modernized a mission-critical banking system by moving off rigid legacy technology onto MongoDB, using AI-assisted tooling to break work into smaller, safer releases without outages.
The bank reduced the development time required to migrate a core banking application off of a legacy relational database to MongoDB Atlas by up to 90% at one-tenth of the cost of a traditional migration.
