An AI readiness gap is emerging in India, with legacy architecture cited as the primary barrier to AI success, according to new IDC research commissioned by MongoDB.
IDC has predicted that organisations who fail to address technical debt will face 50% higher failure rates and rising costs for their AI initiatives by 2027.
The research paper, Modernising Legacy: Winning in the Age of AI, found that nearly half of Indian organisations (46%) 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, there is a cohort of leaders in India who are generating more than three times more digital revenue (73%) than their mainstream peers (20%) by successfully investing in strategic modernisation programmes to escape their legacy architecture.
“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. In India, the top three challenges in software development identified in the research were: embedding security into the development process without impacting speed or innovation (35%), data management and poor quality data (34%), and gaps in engagement between software teams and the business (28%) .
Support for new AI initiatives was the number one driver for modernising databases and applications in India (50%) However, almost all organisations (98%) have experienced failed modernisation initiatives, with siloed and poor-quality data cited as the major obstacle.
By contrast, the cohort of Indian companies the research identified as ‘Leaders’ treat modernisation as an ongoing discipline and long term investment, with 57% 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. 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 demonstrating how to become a leader in AI and modernisation is IntellectAI, India’s leading enterprise AI fintech. IntellectAI used MongoDB to modernise how it handles large volumes of messy, real-world business information so its AI systems can find and use the right data when it matters. That foundation helped it scale ESG and compliance analysis from 100–150 companies manually to 8,000+ companies globally, delivering more than 90% accuracy and enabling faster, higher-confidence decisions at enterprise scale.
“In a fast-growing economy like India, legacy architecture acts as a costly anchor, limiting the ability to translate AI ambition into real-world outcomes. However, I’m very optimistic about the path forward. We’re already seeing leaders like IntellectAI, Zepto, SonyLIV, and Cars24 prove that by addressing technical debt and moving to modern data foundations, organizations can unlock AI more effectively and build a durable competitive advantage in the AI era,” said Aamir Sait, Vice President India, MongoDB.
IDC: How to Bridge the AI Readiness Gap
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 modernisation and AI delivery can move faster without breaking compliance and reliability.