Through the Code Maze: AI vs Manual Refactoring

Introduction

For years, manual refactoring has been the only real strategy — slow, expensive, dependent on people who knew every hidden corner of the codebase. It was never elegant — but it got the job done safely. Then AI arrived, promising to change the rules.

Chances are, your team is already experimenting with it. And yet, if you’re leading that transformation, you’ve likely felt the quiet question beneath the excitement: not “can AI do it?”, but “can it do it responsibly?”. Having led modernization projects for years, I’ve seen both sides of the journey — the classic discipline of manual refactoring and the uncanny acceleration that AI now brings. But I found myself asking the same question every CTO eventually does: Can we really trust it? Spoiler: what I’ve learned is that the promise of AI refactoring isn’t about removing humans from the loop — it’s about giving them a loop that finally closes.

Let’s see how AI code refactoring really stacks up against manual refactoring — and how to take the right path to get the most business value out of it.

Assurance in the Age of Intelligent Automation

In recent years, intelligent tools have entered the picture with a compelling proposition: automation that speaks the language of developers, understands architecture, and accelerates structural change. Refactoring, once viewed as a time-intensive cleanup operation, is evolving into a strategic lever — and code optimization services powered by automation are reshaping its economics.

And that has a direct impact on ROI. However, as HBR notes, even as AI’s promise grows, most enterprises still struggle to build the proper infrastructure to realize it. The real challenge isn’t training a model — it’s designing the environment where AI can operate responsibly. In other words, the difference lies in how it’s implemented and governed.

In fact, large-scale platforms run on millions of code lines. In this setting, manual refactoring takes time and can span multiple sprints with test creation and architecture sessions. The choice between manual and AI-assisted refactoring is of importance at the executive level. It shapes hiring plans, budget allocation, sprint velocity, and even platform reliability.

Legacy complexity is quietly eating away at enterprise innovation, slowing modernization and dulling competitiveness. Gartner’s 2025 platform engineering brief estimates that more than a third of engineering effort now goes to managing outdated systems. Stack Overflow’s global survey mirrors this: seven in ten developers wrestle with unclear or obsolete logic every week.

Meanwhile, AI-powered assistants are closing the time gap. They can analyze entire repositories, track dependencies, summarize old logic, and suggest changes that follow clean code principles. With the incredible rise of AI in software development, what used to need extensive code digging is now part of a developer’s daily routine.

CTOs who embed AI refactoring capabilities gain more than tooling. They establish a system where modernization no longer competes with innovation, but becomes the enabler. Sound refactoring modernizes internal systems without altering their surface-level behavior. And whatever the matter, the business logic remains. What improves is the code’s adaptability, clarity, and alignment with current engineering standards.

The real value emerges as systems scale and products evolve. Engineering leads using AI for refactoring get faster upgrades. They also gain greater confidence in impact analysis and see more consistent results in regression testing. This change is reshaping how product teams plan modernization, making the initial stages less complex. Finally, refactoring moves from technical hygiene to business priority and creates the conditions for faster iteration.

The Bottom Line: How does code refactoring with AI reshape development economics and business competitiveness?

Automation makes modernization from a necessary evil to a strategic advantage. By handling complex analysis, optimization, and code maintenance, AI reduces technical debt. Refactoring becomes technical hygiene that directly boosts ROI and scalability.

Modernizing Legacy Code — While Not Bringing Down Prod — Joel Tosi — NDC Oslo 2025

ROI Reality Check: What AI Refactoring Delivers in Numbers

Every refactoring decision lands somewhere between a cost center and a competitive advantage. Multiply that across legacy heavy platforms, and the math gets ugly.

A new generation of AI-assisted refactoring tools enters with weighty claims — velocity without compromise, automation with context, transformation on demand. At the surface level, the pitch is compelling. Beneath that, the operational, financial, and architectural implications carry substance worth dissecting.

ROI & Computational Symbiosis

From my own field experience, the most successful transformations start small — a single service, a measured pilot, a clear feedback loop. Once teams see measurable ROI and accuracy improvements, adoption stops being a question and becomes an operational habit.

Because engineering budgets remain finite. In environments with 2M+ lines of legacy code, manual refactoring typically stretches across multiple quarters. Teams often allocate senior talent and pause feature velocity, while refactoring legacy code services driven by AI can deliver measurable results before the burn rate becomes a problem.

By contrast, AI-led workflows introduce a compression effect. Code models, pretrained on open and proprietary datasets, parse dependency trees, locate anti-patterns, and suggest structured improvements inside the IDE. After all, code generation conforms with defined best practices.

For instance, in a 2025 internal AWS study on some migration of legacy billing services, using AI to help with refactoring ended up cutting costs by a whopping 60% — a major win considering they managed to finish a 9-month rewrite in just 16 weeks flat.

On the flip side, though, those top-tier AI development tools come with a hefty price tag. Then there’s also the setup cost, getting them up and running, which involves executive buy-in, implementing access controls, and making sure everything is compliant. That said, the upside is that larger teams often recoup their costs within 3 to 6 months, especially if they’re using automated testing and CI pipelines, plus architectural governance to keep everything on track.

Getting the Accuracy

One of the most impressive things is how effectively AI untangles messy code. In projects where nobody’s bothered to document what they’ve done, that’s a huge bonus. Engineers end up getting access to tools like summarization, impact analysis, and some decent suggestions for how to rewrite things — all without having to leave their IDE.

For instance, in Microsoft’s 2024 study of GitHub Copilot, teams saw their time to merge cut by almost 55% when they started using AI to suggest code as they refactored.

When they do things right and stick to some best practices at the language level, accuracy really starts to add up. One of the things AI does consistently well is to help out with the low-level stuff: things like extracting conditions, collapsing branches, taking out redundant input validation, or getting function signatures in line.

However, domain-specific logic introduces constraints. During an internal refactor of a lift ticket pricing engine (Rubberduck project), the AI streamlined conditional logic based on age and date parameters. In doing so, it minimized a segment responsible for holiday-specific pricing overrides. The issue emerged only during test execution. Business logic remained unchanged in documentation, but had shifted behavior during control flow simplification.

LLMs handle code syntax and structure with sophistication. However, the interpretation of business rules encoded in control logic remains probabilistic. This creates the necessity for a hybrid governance model — with test coverage, manual review, and CI observability working in tandem.

Coverage grows rapidly. Still, integrity relies on structured supervision.

Risk in the Era of Autonomous Runtime

Every codebase carries implicit assumptions — architectural, behavioral, and security-bound. AI tools surface structure. What lies beneath that structure requires domain understanding, contractual awareness, and operational history.

In financial platforms, a misplaced rewrite of a fund settlement gateway can trigger reconciliation mismatches. In healthcare, a change to asynchronous error handling can affect data consistency across audit trails. Both scenarios emerge from localized edits that appear clean — syntactically valid, test-passing, code-review-approved — yet materially shift operational behavior.

Three risk vectors frequently surface:

• Contextual blind spots. LLMs operate within token windows. Decisions that depend on distributed logic — split across multiple files, services, or commits — may bypass critical context. Suggested changes then reflect partial truths.
• Dependency exposure. AI-assisted changes can invoke third-party libraries or code constructs that introduce unexpected side effects. These integrations, if insufficiently sandboxed, create security review backlogs and compliance gaps.
• Change attribution. When AI suggests code, its provenance becomes unclear. In regulated industries, engineering leaders require full traceability — from commit author to model prompt. This remains an evolving concern in enterprises using shared LLM layers across teams.

Scenarios such as missed exception handling, dropped side-effect logic, or abstracted error messages generate real financial impact. Internal cost modeling from Tier-1 SaaS providers in 2024 estimated single-instance outages from AI-generated code failures at $850K-$1.2M, factoring SLA violations, customer remediation, and delayed feature rollouts.

Mitigation involves discipline:

• Regression and chaos tests post-refactor
• Domain-specific AI model fine-tuning
• Feature flag gating for all AI-suggested code
• Audit trails from model inference to merge

Well-instrumented pipelines deliver transformation with visibility. The architecture supports speed, but precision defines resilience.

Interpreting the Return Through Data-Centric Engineering

AI-assisted refactoring changes the engineering rhythm. When paired with rigorous oversight and measured deployment, it shifts platform modernization from long-term aspiration into tactical motion.

The opportunity lies in volume: millions of lines optimized, dependencies clarified, and architectures simplified. The responsibility lies in alignment — between model output, domain standards, and operational thresholds.

Where those align, AI ceases to be speculative. It becomes a structural advantage. Figures refer to enterprise-scale legacy projects drawn from your internal case work and research assets.

Every modernization pitch sounds the same until the numbers hit the table. The promise of AI-driven refactoring — faster delivery, lower cost, higher code quality — only matters when it shows up in the budget and the quarterly report.

In large-scale environments, the economics shift fast. Once the first pipelines stabilize, AI doesn’t just accelerate code work; it reshapes how engineering time converts into business value. Manual bottlenecks disappear, feedback loops shorten, and technical debt stops piling up at the same pace.

Across the projects we’ve audited and executed — from financial platforms and SaaS products to enterprise billing systems — the ROI pattern stays consistent: cost compression in the first quarter, measurable quality gains in the second, and velocity translating into revenue by the third.

The figures below reflect what happens when AI-assisted engineering meets disciplined DevOps governance — real, verified outcomes from live modernization programs.

This is what speed looks like when built into the architecture.

Value lever	Quantified outcome	Representative case/note
Direct engineering cost	-60 % total refactor spend during a legacy-billing migration completed in 16 weeks instead of the planned nine-month rewrite	Internal AWS programme (2 M LOC, Java → micro-services)
Manual effort avoided	Up to -87 % developer hours on repetitive code conversion when AI agents handle syntax, pattern, and test scaffolding	Morgan Stanley automation study, S&P-500 baseline
Delivery lead-time	Nine-month plan compressed to < 4 months (same AWS case) — enables earlier API monetisation, earlier cash flow	“Time value” often exceeds pure cost savings
Pay-back window	Typical enterprise recovery 3-6 months once AI pipelines stabilise and automated tests are in place	Capex shifts to fast amortisation
Tooling investment	$10k — $40k per engineer/year for secure, enterprise-licensed AI assistants & scanning toolchain	Budget line typically < 8 % of total programme OPEX
Cycle-time & quality	-30 % backend development time and -20 % post-release defects after AI-driven test generation and early QA start	Devox SaaS modernisation, 400k LOC
Revenue acceleration	Finance platform projects +16 % realised and +19 % projected annual gross revenue once modernised features are shipped faster	VP Engineering testimonial, 2025

Interpreting the Return

The savings come from precision: fewer idle cycles, fewer rework loops, faster validation, and cleaner code paths that scale without friction.

For a CTO, the signal inside these numbers is clear. AI refactoring moves value creation from maintenance to momentum. Every sprint that used to pay back technical debt now contributes directly to product delivery and revenue capacity.

The organizations that win this transition treat ROI as a managed metric — measured, reviewed, and reinvested into automation maturity. They align AI capability with engineering governance, allowing technology to evolve without creating a new generation of hidden risk.

That’s when returns start to compound — modernization that pays for itself and continues to drive growth.

The Devox Modernization Doctrine: Human-Machine Coevolution

Modernization at the enterprise level is never about technology alone. For CTOs facing high-stakes transformation, the true challenge is creating a change process that unlocks business value, manages risk at scale, and equips the organization for continuous evolution. At Devox, our doctrine goes beyond tools and automation; it is a discipline anchored in transparency, measurable progress, and accountable delivery.

• Every engagement begins not with a search for quick wins, but with a complete, economic diagnosis of the existing architecture. Our audits map out technical dependencies, identify zones of concentrated risk, and tie every finding to a real business consequence — whether that’s hidden cost, operational drag, or exposure to critical incidents. The result is a modernization ledger that allows the CTO to quantify technical debt in terms of margin, agility, and resilience.
• Transformation moves forward in contained phases. Instead of big-bang rewrites, we approach legacy framework transformation in controlled waves — each a self-contained stream with its own release pipeline and clear boundaries. That setup keeps every change isolated, measurable, and reversible, so performance shifts or regressions stay contained and business continuity stays intact. Every wave lands with visible gains: shorter lead times, fewer incidents, faster feature flow.
• AI augmentation, for us, is a multiplier. Our AI Solution Accelerator™ automates everything that can be standardized: dependency mapping, pattern recognition, automated test generation, and initial code rewrite. Yet at every handover, domain engineers still need to step in to double-check that the business logic makes sense, keep the technical direction on track, and only sign off on what truly meets the standards set by both engineering and product leaders. That’s how you can scale automation without watering down who’s accountable or what’s going on.
• Governance and auditability are native. Governance and auditability are built in by design. You get security rules, privacy checks, and the ability to track what’s going on, all wrapped into every single auto-build/ deploy cycle. In industries where being compliant is make-or-break – think finance or healthcare, or your average SaaS provider — this framework lets the CTOs move at full speed without losing their grip or being left with nothing to show for it. Every change, whether it’s something the AI suggested or you’ve finally merged it to production, has the whole story behind it, so audits aren’t a bottleneck or a burden anymore, but rather just another part of business as usual.
• Business value must be demonstrable. You need to be able to measure the value you’re getting. That’s why at Devox, every big modernization project isn’t measured by how much code you write or how many tasks you tick off, but in real operational numbers that actually resonate with the board — reduced support costs, accelerated time to market, improved customer-facing reliability, and actual cash coming in faster. These are the real metrics that count – the data that proves, no question, that upgrading your platform is actually driving growth.

The end result isn’t just a modernised codebase – it’s a living, breathing system. Your pipelines, automation, and shared knowledge get left with the client team, so they can keep evolving and improving long after we’ve done our thing and walked out the door. The platform itself becomes an adaptive asset — built to absorb change, scale with demand, and turn innovation into habit. The Devox doctrine treats modernization as leverage: for control, visibility, and lasting value creation. That’s how enterprise tech becomes a true advantage, when change is systematic, transparent, and aligned with the business agenda.

Every CTO eventually hits the same wall: systems that once carried the company start slowing it down. The decision isn’t just how to modernize — it’s how to do it without losing momentum, talent, or trust.

The Bottom Line: What does AI code refactoring really deliver in ROI?

AI refactoring saves up to 60% on engineering hours by automating boilerplate cleanup and untangling legacy dependencies — the kind of work that drains time without pushing the product forward. More than cost, it accelerates the delivery of revenue-generating features and frees senior devs from grunt work. But without strong DevOps — test coverage, CI discipline, and financial visibility, the gains stall. When those are in place, every sprint delivers cleaner code and clearer business value.

Conclusion

Drawing on my time spent leading the charge on large-scale modernization, I’ve come to realize that the real key to AI’s potential isn’t just how fast it is, but how much leverage it can give us, turning that one crucial insight into a snowball effect of momentum that never falters under the constraints of governance and the bigger picture.

Every new wave of technology shifts where real value lies — for a long time, it was firmly in the control of skilled engineers — people who were happy to spend their days digging in deep, being methodical and exacting in their approach. But these days, that value is expanding as intelligent automation matures and proves its worth — able to scan a codebase, understand the way it’s put together, and come up with suggestions for change that can be scaled up in a heartbeat. Even so, progress still comes down to the quality of the leadership. AI can certainly give a boost to every line of transformation, but it’s the judgment and guidance that humans bring to it — the discipline, governance, and intent behind it all — that ultimately decides whether that momentum compounds or crashes to the floor.

Manual refactoring remains the kind of craftsmanship that keeps meaning and continuity intact; AI refactoring, on the other hand, brings a reach and speed that the needs of growth these days demand. Together, they’re inventing a new beat for the engineers — one that’s continuous, driven by data, and accountable for results. At Devox, we don’t see modernization as just about tweaking code to make it better. We see it as more of an exercise in getting everyone in the company on the same page, tying together the pace of technical change with the performance of the business, and turning those old, stuck systems into platforms that can keep up with the ambition of the company.

That’s the true payoff of intelligent refactoring — a technology landscape that actually learns and adapts to the business, and helps it grow — not holds it back.