MongoDB: Solve tech debt to unlock 3x digital revenue boost

- Poor data foundations are derailing AI ambitions: 90% have of organisations have seen modernisation failures, with data quality emerging as a consistent root cause

- The cost of tech debt is rising: Organisations that fail to address tech debt will face 50% higher failure rates and rising costs for AI initiatives by 2027

- The Asia Pacific region's (APAC’s) AI leaders are achieving better business outcomes: AI leaders are generating 3x more digital revenue (71%) than mainstream peers (23%) by building on modern, AI-ready data platforms

 

An AI readiness gap is emerging across Asia Pacific, with legacy architecture cited as the primary barrier to AI success, according to IDC insights commissioned by MongoDB. According to IDC, 89% of APAC organisations acknowledge that technical debt is a major obstacle to modernisation. 

The consultancy has predicted that organisations which fail to address technical debt will face 50% higher failure rates and rising costs for their AI initiatives by 2027. This is particularly urgent for the 34% of organisations which have yet to begin any modernisation initiatives.

Modernizing Legacy: Winning in the Age of AI found that nearly half of the region's organisations (43%) say their existing architecture makes it impossible to build new applications without extensive modernisation because it is too rigid, costly, and slow for today’s requirements.

However, leaders are generating 3x more digital revenue (71%) than their mainstream peers (23%) by successfully investing in strategic modernisation programmes to escape their legacy architecture. IDC predicted that leaders can expect digital revenue to hit 77% of total revenues by 2029, while the rest, termed the 'mainstream', are expected to see a 60% contribution to total revenues.

"The stakes for modernisation 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 Dr William Lee, Senior Research Director, Service Provider and Core Infrastructure Research, IDC Asia Pacific.

“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."

The gap between AI ambition and reality is most visible at the data layer, MongoDB said. 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%)

- Embedding security into the development process without impacting speed or innovation (31%)

Support for new AI initiatives was the No. 1 driver for modernising databases and applications in the Asia Pacific region, cited by 46% of organisations. However, about 95% of organisations reported project delays, and 90% encountered failed modernisation initiatives, with siloed and poor-quality data cited as the major obstacle.

In contrast, the companies identified as leaders treat modernisation as an ongoing discipline and long term investment, with 58% running multiple programmes 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, MD, CXO Advisory at MongoDB.

“The research is clear, strategic modernisation unlocks AI opportunities and supports a significant increase in revenue. 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."

One example of an organisation leading the way in AI and modernisation is Bendigo Bank in Australia. The bank modernised 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.

Source: MongoDB landing page. AI readiness is discussed in Modernizing Legacy: Winning in the Age of AI.

To pay down data debt and improve AI readiness, IDC recommends that Asia Pacific organisations:

- Make data quality and governance non-negotiable, so AI systems are fed consistent, trusted operational and vector data.

- Modernise outdated architectures that block change, enabling rapid development of new applications without the risks and costs associated with legacy systems.

- Build cloud-ready, hybrid operating models that reduce data sprawl and make data usable across environments.

- Invest in skills and change management, so modernization and AI delivery can move faster without breaking compliance and reliability.

Explore

Find out how Asia Pacific organisations can replicate the success of the Leaders and close the AI readiness gap

*The IDC InfoBrief, commissioned by MongoDB, Modernizing Legacy: Winning in the Age of AI, Doc #AP242555-IB, April 2026, is based on an online survey of 1,400 organisations with software development and delivery capabilities (100+ employees) across Australia, mainland China, Hong Kong, India, Indonesia, Singapore, South Korea, and Thailand, conducted in late 2025. Respondents were developers and IT decision-makers, which included primary decision-makers and members of decision-making units.