New Research Reveals Overcoming Legacy Tech Issues Key to AI Success
This guest post comes from IDC’s Dr. William Lee, Senior Research Director, Service Provider and Core Infrastructure Research. MongoDB commissioned IDC to explore the connection between legacy infrastructure, data challenges, and AI across Asia Pacific, and today we’re happy to share that work. For more, see the full MongoDB-sponsored IDC InfoBrief, Modernizing Legacy: Winning in the Age of AI, Doc #AP242555-IB, April 2026.
AI ambition is everywhere across Asia/Pacific. But ambition alone does not determine success. Organizations are discovering that AI outcomes are directly tied to the quality, accessibility, and modernity of their underlying technology stack and associated data technology foundations.
Organizations that have managed to stay abreast of technical and data management changes across the application and infrastructure stacks, by embedding modernization into their organizational DNA, are experiencing 3x more digital revenue growth than those that are bound up in technical and data debt.
To better understand this connection, IDC surveyed 1,400 organizations across eight Asia/Pacific markets. The findings reveal that modernization is no longer a side initiative. It is the core of a sustainable AI strategy.
The AI readiness divide: Leaders versus mainstream
IDC’s latest Asia/Pacific Modernization Survey, sponsored by MongoDB, identifies two distinct groups:
The Mainstream Cohort: organizations still burdened by technical debt, siloed data, and skills gaps
The Leaders Cohort: organizations that have embedded modernization into their strategy and experience the business results to match
This divide is not theoretical. It is measurable in business performance. Organizations in the Leaders Cohort generate nearly three times more digital revenue than their peers.
The difference is not simply higher AI spending. Leaders modernize core infrastructure, align executive support with transformation goals, and invest in skills development alongside technology. They treat AI readiness as an enterprise capability—not a standalone initiative.
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The rigidity trap: Technical debt and AI failure risk
A significant portion of Asia/Pacific organizations remain constrained by legacy architectures. According to IDC’s research, 43% report that their existing architecture is a major obstacle, making it difficult to build new applications without extensive modernization.
This rigidity creates what IDC refers to as data debt—siloed, redundant, outdated, and poor-quality data that undermines AI performance and increases operational cost, and is in addition to the growing levels of technical debt that are being accumulated by organizations due to the slow modernization of older applications.
When AI systems are trained on fragmented or inconsistent data:
Outcomes become unreliable
Bias risks increase
Operational costs rise
Business trust erodes
IDC predicts that CIOs who fail to launch data debt remediation initiatives will face 50% higher AI failure rates and rising costs by 2027.
Yet one-third of all enterprises continue to rely on legacy relational databases.
Many such databases have been implemented in support of a wide array of business applications where business leaders expect they can use AI. Yet the legacy RDBMS-type databases are not capable of delivering on the dynamic, rapidly evolving, high-volume real-time demands that AI requires.
Organizations that are unable to move to AI-ready application stacks are being left behind by those that have already made the switch.
The gap between AI investment and infrastructure readiness is widening.
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Legacy drag: The real business impact
The consequences of technical debt are already visible.
95% of organizations report project delays
90% have experienced failed modernization initiatives
89% acknowledge technical debt as a major modernization obstacle
In addition, organizations cite weak security integration, limited engagement with the business users, and outdated workflows as compounding challenges.
Modernization failures are rarely just technical. They are organizational and structural.
What sets leaders apart
The Leaders Cohort does not operate in a constraint-free environment. Instead, they respond differently.
IDC defines Leaders as organizations—across both digital-native and traditional industries—that have broken free from legacy rigidity and embedded modernization into ongoing operations. Their distinguishing characteristics include:
Continuous, multi-pronged approaches to addressing legacy systems
Alignment between executive leadership, funding, and AI outcomes
Investment in modernization as a long-term capability, not a one-off project
Strong focus on AI and modern application development skills
The result is not just better IT performance. Leaders grow digital revenue faster and are positioned to extract value from AI initiatives earlier and more consistently than their peers.
Cloud-centric data management: A strategic enabler
Modern data platforms are central to this shift.
In IDC’s research, 38% of Asia/Pacific organizations identify cloud-centric data management platforms as their top modernization investment priority for 2026. The motivation is clear: support hybrid architectures and AI workloads without introducing additional complexity.
While AI enablement is a universal requirement, Leaders distinguish themselves by prioritizing:
Security and compliance
Flexibility across structured and unstructured data
Scalable architectures aligned to modern AI toolchains
This capability is increasingly critical. Much of today’s AI-relevant data—including content, sensor outputs, and customer interactions—resides in unstructured formats that traditional architectures struggle to integrate effectively.
Handling both structured and unstructured data seamlessly has become a competitive differentiator.
Modernization as a continuous strategy
IDC’s perspective is clear: modernization is no longer a technology refresh cycle. It is a strategic operating model.
Successful organizations approach modernization across three dimensions:
People
Leaders invest deliberately in AI and modern application development skills. They recognize resistance to change as a strategic risk and actively manage it.
Process
They adopt cloud-native approaches rather than repeating short-term “lift-and-shift” migrations that simply relocate technical debt. They use structured prioritization frameworks to embed modernization into business-as-usual operations.
Technology
They modernize to data platforms that support scalability, diverse data types, rapid feature development, and alignment with contemporary AI ecosystems.
The ROI equation: Risk of action versus risk of inaction
Modernization is often perceived as expensive and risky. However, IDC’s analysis suggests that the risk of inaction is frequently underestimated, and this study affirms that those who invest effectively, and continuously, into their application modernization program are experiencing both better ROI and higher digital revenues!
Organizations that modernize report:
Significant reductions in reporting time
Double-digit productivity improvements
Meaningful cost savings
Hundreds of thousands of dollars in quantified cost reductions
While full application rewrites and database modernization demand greater upfront investment than lift-and-shift migrations, they can deliver up to three times the long-term benefit.
For CIOs and business leaders, the decision should not be framed as modernization cost versus status quo stability. It is modernization investment versus escalating AI failure risk.
The Path forward: Legacy systems are not permanent
Overcoming legacy is often perceived to be as significant a risk as taking on new technologies. IDC notes that many CIOs across the region focus on risk avoidance as a priority. In contrast, business leaders are seeking innovative solutions that drive new business opportunities, and so CIOs must balance their risk aversion concerns with the business demands. IDC’s research shows that legacy migrations that are under-funded pose significantly higher risk, and deliver lower returns, than those that are sufficiently funded from the outset.
Organizations that proactively address technical debt, modernize systems, and align leadership and funding around AI-enabled outcomes will increasingly separate themselves from the pack.
Those that delay will face structural disadvantages:
Growing technical debt
Escalating modernization costs
Underperforming AI systems
Slower digital revenue growth
IDC’s research shows that the next wave of AI advantage in Asia/Pacific will not be determined solely by model sophistication. It will be determined by architectural foundations.
Ultimately, without modernization, there can be no sustainable AI strategy.
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