Tech Debt Holds Back AI Growth but Leaders Unlock 73% Digital Revenue, IDC Study Finds

Tech Debt Holds Back AI Growth but Leaders Unlock 3x Digital Revenue, IDC Study Finds

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."

India AI readiness gap widens as leaders earn 73% digital revenue: IDC

IDC's research paper Modernising Legacy: Winning in the Age of AI surveys 1,400 Asia/Pacific IT leaders and examines the widening AI readiness gap across the region. It explains why the Leaders cohort generates 3x digital revenue and how organisations can reduce technical and data debt to improve AI outcomes. The paper identifies two distinct cohorts shaping the digital economy: The findings highlight that AI has become a board-level priority for APAC growth, but most organisations are still held back by technical debt and outdated systems. As legacy risks increase, modernisation of core infrastructure is becoming a key factor in capturing AI-driven value. IDC highlights that organisations modernising legacy environments and resolving data debt are achieving significantly higher digital revenue compared to mainstream peers. The gap between the two cohorts is expected to widen further, shaping market competition across the region. AI performance is strongly linked to the quality of operational data and the ability to use modern, cloud-ready architectures. However, legacy systems, technical debt, and fragmented data continue to slow AI adoption and increase failure rates. A major barrier identified is data debt, where siloed and poor-quality data limits AI performance. Legacy relational databases are described as rigid, costly, and inefficient for AI workloads. Regional challenges: IDC also predicts that CIOs who fail to address data debt will face a 50% higher AI failure rate by 2027, driven by persistent model underperformance. Modernisation programs face recurring structural issues across enterprises: Security concerns, migration complexity, and high upfront costs further slow transformation efforts. Enterprises are shifting toward hybrid and cloud-centric data platforms to support AI workloads. Key investment priorities: This shift reflects the need for scalable, flexible infrastructure capable of supporting AI and data-intensive workloads. IDC highlights a clear difference in strategic priorities between Leaders and Mainstream organisations: Leaders are also significantly increasing investment in modernisation: Modernisation outcomes show a clear financial impact: Hybrid cloud adoption (on-premises + cloud) is highlighted as a key enabler for unlocking data value and improving scalability. India reflects a similar but sharper divide between Leaders and Mainstream organisations. IDC indicates that the AI performance gap between Leaders and Mainstream organisations will continue to widen across Asia/Pacific. Organisations that delay modernisation are likely to face: In contrast, organisations investing in continuous modernisation, cloud-native architectures, and AI-ready data systems are expected to strengthen digital revenue contribution and improve AI performance outcomes over time.