# Legacy tech hinders AI projects across the Asia Pacific

- Link: https://www.thailand-business-news.com/generative-ai/301120-legacy-tech-blocks-ai-projects-across-the-asia-pacific
- Published: 2026-04-17T02:43:00+07:00
- Author: J. Allan

Asia Pacific’s AI ambitions are colliding with the past. Outdated infrastructure
is quietly sabotaging the region’s artificial intelligence race, and a widening 
revenue gap is exposing who is falling behind.

## Key takeaways

 * Legacy infrastructure is the single biggest barrier to AI adoption across Asia
   Pacific, with nearly half of organisations unable to build new applications without
   major modernisation first.
 * The revenue gap between digital leaders and the rest is not theoretical: leaders
   generate 71% of revenue from digital products while mainstream peers manage just
   23%.
 * Companies that ignore their technical debt are on borrowed time, with IDC forecasting
   a 50% higher AI failure rate for laggards by 2027.

Across the Asia Pacific, boardrooms are buzzing with AI ambitions. But beneath the
optimism, a stubborn obstacle is stalling progress: the creaking weight of legacy
technology that companies have long deferred modernising.

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New [research](https://cfotech.com.au/story/legacy-tech-blocks-ai-projects-across-asia-pacific)
commissioned by MongoDB and conducted by IDC paints a sobering picture. A survey
of 1,400 organisations across eight markets found that 43% reported their existing
architecture makes it impossible to build new applications without extensive modernisation,
systems that their own staff describe as too rigid, too costly, and too slow for
what the AI era demands.

The findings land at a pivotal moment. Companies across the region have moved from
experimenting with AI pilots to attempting full-scale production deployments. That
transition is brutally exposing the gap between ambition and infrastructure.

## The data problem no one wants to talk about

At the root of the crisis is data quality. The most commonly cited software development
challenge was data management and poor-quality data, named by 32% of organisations.
Close behind were outdated database technology that cannot support AI workloads 
and the difficulty of embedding security into development without sacrificing speed,
each cited by 31% of respondents.

In plain terms, many companies are attempting to build next-generation AI systems
on platforms designed for a previous era of computing.

Supporting new AI initiatives was the main reason for modernising databases and 
applications, with 46% of organisations naming it as their top driver. Yet the path
to modernisation is proving treacherous. Nine in ten organisations surveyed reported
having experienced failed modernisation initiatives, with siloed and poor-quality
data identified as the main obstacle.

## A revenue divide is opening up

The research does more than catalogue frustration. It identifies a consequence that
finance leaders cannot ignore: a measurable and growing commercial gap between companies
that have modernised and those that have not.

A smaller group of companies described as leaders are pulling away from their peers,
generating 71% of revenue from digital products and services, compared with just
23% among mainstream peers. Those leading organisations share a common trait. 58%
are running multiple programmes to reduce legacy constraints and build cloud-ready
foundations for AI systems in production, treating modernisation not as a project
with an end date but as a permanent discipline.

The cost of standing still is rising sharply. IDC has forecast that organisations
that do not address technical debt will face a 50% higher failure rate and rising
costs for AI initiatives by 2027.

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## What the experts are saying

IDC’s Senior Research Director for Asia Pacific, Dr William Lee, was direct about
what the data reveals. He described high-quality, integrated data as the essential
fuel that determines the accuracy and performance of an AI application, and said
many organisations are being held back by rigid legacy architectures that lack the
flexibility and scalability to handle the high volume of unstructured data required
for AI.

MongoDB’s Managing Director of CXO Advisory, Thorsten Walther, framed the issue 
in board-level terms, arguing that AI has made technical debt an urgent priority
for senior leadership and that the research shows strategic modernisation unlocks
AI opportunities and supports significant revenue growth.

## A real-world example

The study points to Bendigo Bank as a concrete illustration of what modernisation
can achieve. The bank moved a core banking application away from legacy relational
database technology to MongoDB Atlas and used AI-assisted tools to break the work
into smaller releases, reducing development time by up to 90% and cutting costs 
to one-tenth of a traditional migration, all without service outages.

## The path forward

IDC outlined a set of steps for organisations seeking to improve their AI readiness,
including stronger data quality and governance, modernising architectures that slow
application development, building cloud-ready hybrid operating models, and investing
in skills and change management.

The survey covered organisations with at least 100 employees across Australia, China,
Hong Kong, India, Indonesia, Singapore, South Korea, and Thailand, spanning developers,
IT decision-makers, and senior executives.

The picture that emerges is of a region at a crossroads. Those who treat modernisation
as a strategic priority are pulling ahead commercially. Those who continue to defer
it are not simply falling behind on technology benchmarks. They are falling behind
on revenue, resilience, and their ability to compete in an economy that AI is rapidly
reshaping.
