Enterprises are facing a practical dilemma with AI. Capability is moving fast. But many organizations are still constrained by the systems that run their core operations.
The result is a dual imperative: AI can’t scale on legacy foundations — and modernization can’t scale at today’s speed without AI.
This article helps CIOs, CTOs, and engineering leaders make modernization decisions that unblock AI adoption while reducing delivery risk — not just ship more code.
The Urgency Is Operational, Not Theoretical
At the heart of most large organizations is tech debt embedded in legacy systems, often built decades ago. These systems slow innovation, resist real-time integration, increase resiliency risk, and make compliance change harder and more expensive.
McKinsey notes modernization can no longer be treated as an “IT problem” to be deferred. Modern technology foundations are increasingly a CEO priority because technology underpins value creation in business transformations.
Legacy Systems Don’t Just Slow AI — They Stop It
Many enterprises invest in AI strategies, talent, and tooling. Then they hit a wall when AI needs to be governed, and when real-time access to the applications that actually execute business processes is required. Legacy systems typically struggle with modern channels, advanced connectivity, and automation-heavy testing and release practices.
This becomes even more visible as teams experiment with agents and automation. AI can speed up certain tasks. But if the underlying system is brittle, the organization may simply move faster into instability. DORA‘s research on AI-assisted software development reinforces this “amplifier” effect — AI tends to magnify an organization’s existing strengths and weaknesses rather than fix foundational issues on its own.
AI Is Also the Tool That Modernizes the Foundation
The constraint is real — but so is the opportunity. McKinsey’s research describes how generative AI can reduce manual modernization work, accelerate modernization timelines by 40–50%, and achieve a 40% reduction in technology debt costs — while also improving output quality.
The economics are already shifting. A transaction processing system at a leading financial institution that would have cost more than $100M to modernize three years ago now costs well under half that with gen AI. In another case, a top 15 global insurer used an agent-led approach to improve code modernization efficiency and testing by more than 50%.
Deloitte describes three emerging approaches enterprises are converging on: rethinking tech processes, reengineering the digital core, and reimagining business capabilities with AI at the center. Deloitte also cautions against repeating the early “lift-and-shift” cloud era — where migrating without refactoring delayed ROI and created downstream cost and pain.
A real-world signal of this shift: Deloitte notes Amazon’s use of Amazon Q to upgrade applications to Java 17, reducing upgrade time from 50 developer days to a few hours and contributing to an estimated $260M in annualized efficiency gains.
Three Moves That Separate Action From Experimentation
- Modernize for value, not volume. Converting old code line by line migrates tech debt — not business outcomes. This is a “code and load” trap. Use AI to identify what matters, then modernize what drives outcomes.
- Reengineer the digital core for AI consumption. Modern architectures, containers, and integration patterns aren’t just outputs of modernization — they’re the foundation AI needs to act reliably at scale. Deloitte’s view is that this reengineering phase may be the best opportunity for enterprises to make their data and systems truly consumable by AI.
- Make quality engineering the control plane. Faster delivery without stronger testing and governance creates operational fragility. McKinsey highlights resiliency risks in legacy environments — especially where understanding is limited, and testing remains manual. DORA’s amplifier framing makes it clear: without strong engineering foundations, AI will surface issues faster, not remove them.
At Scalence, Application Engineering & QA sits at this intersection — supporting modernization programs while strengthening the quality engineering practices that keep AI-assisted delivery production-grade. For a related Scalence perspective on AI in production, see our view on operationalizing agentic AI for enterprises.
Continue This Conversation at Anthropic Partner Summit 2026
Such conversations are happening now. I’ll be at the Anthropic Partner Summit in Carlsbad on March 11–12, and if your enterprise is navigating modernization, AI-assisted development, or quality at scale, I’d welcome the opportunity to discuss. Reach out before then, or find me there.
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Ashish Bahl is the Associate Vice President and Global Practice Leader for Application Engineering & QA at Scalence. He specializes in helping enterprises modernize their application portfolios while building the engineering practices and quality frameworks needed for AI-native development.
