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

Tech Debt Creates Widening AI Readiness Gap

A new IDC study titled "Modernising Legacy: Winning in the Age of AI" exposes a critical divide shaping the future of artificial intelligence across Asia Pacific . Surveying 1,400 IT leaders across the region, the research identifies two distinct cohorts: a group of leaders who have successfully escaped their legacy architectures and mainstream organizations struggling under the weight of tech debt . The gap between these groups is stark and growing, with profound implications for AI growth and competitive positioning.

The study reveals that leaders are generating 73% digital revenue, more than three times the 20% achieved by their mainstream peers in India 1

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. This dramatic difference stems from strategic investments in modernizing legacy systems that enable these organizations to leverage AI effectively . The AI readiness gap is expected to widen further, fundamentally reshaping market competition across the region .

Legacy Architectures Block AI Adoption

Dr. William Lee, Senior Research Director for Service Provider and Core Infrastructure Research at IDC Asia Pacific, emphasizes the urgency of the situation. "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," he states 1

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. Yet many organizations remain trapped by rigid legacy architectures that lack the flexibility and scalability to handle the high volume of unstructured data required for AI workloads 1

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Data debt emerges as a major barrier to AI adoption, with siloed and poor-quality data severely limiting AI performance . Legacy relational databases prove rigid, costly, and inefficient for modern AI workloads . IDC predicts that CIOs who fail to address data debt will face a 50% higher AI failure rate by 2027, driven by persistent model underperformance . This forecast underscores why robust data architecture has become non-negotiable for organizations seeking to compete in the age of AI.

Modernization Unlocks AI Outcomes

Leaders distinguish themselves through fundamentally different strategic priorities and investment patterns . These organizations are significantly increasing investment in modernization initiatives that resolve technical debt and create cloud-ready architectures capable of supporting AI outcomes . Enterprises are shifting toward hybrid and cloud-centric data platforms to support AI workloads, with hybrid cloud adoption emerging as a key enabler for unlocking data value and improving scalability .

India reflects a similar but sharper divide between leaders and mainstream organizations . Organizations that delay modernization face mounting risks including higher AI failure rates, reduced digital revenue contribution, and weakened competitive positioning . Conversely, organizations investing in continuous modernization, cloud-native architectures, and AI-ready data systems are expected to strengthen digital revenue contribution and improve AI performance outcomes over time . The research makes clear that AI has become a board-level priority for growth across Asia Pacific, but success requires addressing the foundational issues of legacy environments before organizations can capture AI-driven value .