Reimagining brownfield application modernization: Combining deterministic data and agentic AI for technical debt reduction

Addressing technical debt effectively has emerged as one of the most complex challenges for CIOs and CTOs. Years of accumulated complexity, structural inefficiencies, dead code, security gaps, poor resource allocation, performance bottlenecks and architectural decay now threaten stability, security and innovation across enterprises.

Yet, despite its criticality, technical debt remediation is routinely deprioritized. Competing modernization and migration programs consume budgets, and remediation has historically required separate funding, separate teams and timelines.

Worldwide, technical debt surged in 2025, reaching the equivalent of 61 billion days of repair time, according to a study by CAST. In the US alone, repairing tech debt requires 2.07 workdays per 1,000 lines of code (LOC).

The report also found that a staggering 45% of the world’s code is deemed fragile, impacting consumers daily. Some languages carry more technical debt than others. Python requires an average of 4.5 workdays per 1,000 LOC, while Objective C/C++ calls for 2.87 per 1,000 LOC.

New research from the IBM Institute for Business Value shows that enterprises that fully account for the cost of addressing technical debt in their AI and modernization business cases project higher ROI. In fact, they anticipate up to 29% greater returns than those enterprises that overlook these costs.

Conversely, organizations that overlook technical debt risk losing 18% to 29% of expected returns, turning strong investment cases into marginal outcomes. The study also reveals that 81% of executives believe that technical debt is already constraining AI success. Meanwhile, 69% say it can render some initiatives financially untenable by adding 15% to 22% to delivery timelines.

It’s clear that technical debt is a material business variable that directly shapes the success or failure of digital transformation programs. With agentic AI, technical debt can be addressed within migration and modernization programs without increasing cost and often accelerating overall delivery.

Over time, legacy applications tend to accumulate significant volumes of non‑value‑adding code, which inflates modernization scope, costs and timelines unnecessarily. This issue typically happens due to three recurring patterns:

-      Dead or inactive code

As applications evolve, functionality is retired, replaced or bypassed—but the underlying code is rarely removed. This issue results in large portions of the codebase that are no longer executed in production yet continue to exist and undergo scanning, testing, securing and modernizing. Dead code adds noise, increases perceived complexity and inflates effort without delivering business value.

-      Poorly structured legacy code from multiple handovers

Many legacy applications have passed through multiple development teams over years or decades. Each handover introduces inconsistencies in coding standards, design patterns and architectural decisions. In the absence of strong governance, teams inevitably produce fragmented, tightly coupled, hard‑to‑maintain code that increases technical debt and slows modernization.

-      Duplicate code due to inadequate documentation and transitions

When documentation is missing or outdated, new teams often reimplement existing functionality rather than reusing it. This issue results in duplicated logic scattered across the application, increasing maintenance burden, defect risk and modernization effort—while providing no incremental business capability.

For a typical legacy application with approximately 1 million lines of code, deterministic structural analysis with CAST can identify and quantify the impact of dead, duplicated and low‑value code. This insight enables teams to surface these issues early in the journey.

Often, the first iteration of analysis can reduce the effective modernization scope by as much as ~30% by eliminating dead code, highlighting redundant components and isolating business‑critical logic from complexity. Based on estimates by CAST, these early insights can significantly streamline the overall effort. This outcome means that modernization efforts need to focus only on the remaining ~70% of the codebase that truly delivers business value.

This right‑sizing of the codebase upfront directly translates into lower modernization cost, shorter timelines, reduced risk and improved ROI. This approach helps ensure that clients are investing effort where it matters most instead of modernizing waste.

Deterministic structural analysis for application modernization is a precise, rules-based approach to analyzing legacy codebases (such as COBOL, Java™ or RPG) without relying on guesswork. It transforms source code into a structured, machine-readable format (typically Abstract Syntax Trees or loss-less semantic trees) to understand, visualize and automatically transform code with 100% accuracy.

Deterministic data is what grounds modernization in truth. Tools like CAST Highlight and CAST Imaging deliver the structural facts that AI alone cannot infer. CAST describes these facts as “absolutely critical” for both assessing legacy systems and validating the transformed state.

• Deterministic intelligence gives us:
• Accurate system baselines
• Complete dependency visibility
• High-fidelity quality and security insights
• A way to confirm transformation outcomes
• A check against AI-driven hallucinations and errors

And when paired with IBM’s deep modernization, hundreds of migration patterns and rewrite recipes, it forms a modernization backbone competitors cannot replicate.

To move modernization beyond incremental improvement, IBM designed a way to unite deterministic data with agentic AI automation. The outcome is a powerful hybrid architecture that blends accuracy, intelligence and automation at scale.

Deterministic data includes:

• CAST for deep structural analysis
• IBM Code Transporter for established migration pathways
• IBM modernization blueprints and engineering accelerators

Agentic AI Automation includes:

• MCP-based multi-agent coordination
• Autonomous fixing of compilation errors and dependency conflicts
• Smart prioritization of technical debt hotspots
• Automated refactoring driven by structural findings

This fusion creates a closed-loop modernization engine, where AI operates with precision because deterministic facts continually guide it.

IBM Consulting® helped a large Brazilian bank address their migration delays by treating technical debt as a core modernization issue, not a separate cleanup task. Deterministic analysis that applies CAST made technical debt visible and measurable, identifying dead, duplicated and low-value code early. Internal IBM data showed that this analysis right-sized the modernization scope by approximately 30% and helped ensure that the bank did not modernize non-value-adding code.

IBM industrialized remediation through Code Transporter, automating recurring debt patterns such as Java upgrades, framework migrations and standards alignment. Reusable automation recipes replaced repeated manual fixes, sharply reducing rework. Agentic AI handled residual complexity, resolving dependency conflicts and compilation issues with precision. Internal IBM data shows that:

• Migration effort dropped from nearly 90 to 56 hours per application
• Throughput increased from 40–55 or more applications per month
• Technical debt reduction became an embedded, continuous outcome of migration delivering cleaner, more secure and future-ready applications

IBM’s differentiation is clear and compelling, rooted in a combination few competitors can match. These factors include:

• Industry-leading modernization expertise
• Deterministic insights that provide accuracy and trust
• Agentic AI that delivers automation and scale
• Technical debt reduction integrated directly into modernization delivery

With this approach, clients no longer need separate funding streams or stand-alone initiatives for structural debt remediation; it becomes an inherent, value-driving component of the modernization lifecycle.

The powerful intersection of deterministic insight, agentic AI and IBM’s established engineering patterns is redefining the modernization of brownfield applications. Over the last year, IBM has built a new modernization model that transforms applications, improves software quality, shrinks technical debt and establishes repeatable modernization pipelines. In their PEAK Matrix Assessment, the Everest Group recognized IBM’s successes in application transformations, especially for AI-enablement.

What started as exploratory demos and engineering pilots has quickly matured into a clear IBM differentiator: a modernization strategy that clients now actively seek as they confront aging systems, modernization pressures and escalating technical debt.

Reimagine application modernization with IBM Consulting