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:
- Leaders, who modernise core systems and enable stronger AI adoption
- Mainstream organisations, which remain constrained by legacy infrastructure
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AI has become a board-level priority for APAC growth, but most organisations are still held back by technical debt and outdated systems.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.
AI Readiness Gap and Structural Impact of Legacy Systems
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.
Key findings:
- The Leaders cohort generates nearly 3x the regional average in digital revenue
- By 2029, Leaders expect digital revenue to reach 77%, compared to 60% for Mainstream organisations
- Organisations failing to close the gap risk falling behind in AI-driven transformation
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.
Data Debt, Technical Debt, and AI Failure Risk
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:
- Data debt threatens APAC’s US$47.2B big data market (by 2028)
- 89% of organisations say technical debt is a major obstacle
- 34% have not started any modernisation initiatives
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.
Additional findings:
- 95% of organisations report project delays
- 90% have experienced failed modernisation initiatives
- Poor data quality is a consistent root cause of failure
Key Reasons Why Modernisation Fails
Modernisation programs face recurring structural issues across enterprises:
- Inadequate funding (24%)
- Lack of clear business objectives (23%)
- Technology mismatch (22%)
- Resistance to change (22%)
- Data quality issues (21%)
Security concerns, migration complexity, and high upfront costs further slow transformation efforts.
Cloud and Data Strategy Shift
Enterprises are shifting toward hybrid and cloud-centric data platforms to support AI workloads.
Key investment priorities:
- Cloud-centric data platforms (38% of organisations)
- Code security tools (30%)
- CI/CD pipelines (29%)
- API management (28%)
This shift reflects the need for scalable, flexible infrastructure capable of supporting AI and data-intensive workloads.
Leadership Strategy and Investment Trends
IDC highlights a clear difference in strategic priorities between Leaders and Mainstream organisations:
Leaders prioritise:
- AI enablement
- Security and compliance
- Flexible data structures
Mainstream organisations prioritise:
- Performance
- Scalability
Leaders are also significantly increasing investment in modernisation:
- Spending expected to rise from US$3.4M to US$5.6M over three years (~65% increase)
Turning Modernisation into Business Value
Modernisation outcomes show a clear financial impact:
- Up to 3x higher revenue impact from modern application replatforming compared to lift-and-shift
- 10–20% productivity gains after AI deployment
- Up to US$800,000 in operational savings
Hybrid cloud adoption (on-premises + cloud) is highlighted as a key enabler for unlocking data value and improving scalability.
India Focus: AI Readiness Gap and Revenue Leadership
India reflects a similar but sharper divide between Leaders and Mainstream organisations.
Key findings:
- 46% report legacy architecture limits new application development
- Leaders generate 73% digital revenue, compared to 20% in mainstream organisations
- 98% have experienced failed modernisation initiatives
Key challenges in India:
- Security integration without slowing innovation (35%)
- Poor data management and quality (34%)
- Weak alignment between business and engineering teams (28%)
Additional insights:
- 57% of Leaders run multiple modernisation programs simultaneously
- Failures are largely linked to siloed and inconsistent data
- Companies such as IntellectAI, Zepto, SonyLIV, and Cars24 are actively modernising data foundations for AI
Outlook
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:
- Higher AI failure rates
- Slower transformation cycles
- Increased operational inefficiencies
- Reduced competitiveness in AI-driven markets
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.