AI Tools for Accelerating Legacy Refactoring: Copilot, Claude, Cursor

Most organisations carry more legacy code than they’d like to admit. Some of it still works reliably. Some of it only works because nobody touches it. Either way, it slows down decision-making and absorbs engineering time that should be spent elsewhere.

AI didn’t change that overnight, but it did shift the balance of effort in AI code refactoring tasks. In 2025, tools like Copilot, Claude, and Cursor matured enough to be useful not just for writing new code, but for helping teams understand and reshape the old parts of their systems. They don’t remove the need for engineers, and they don’t remove the need for judgment. They do reduce the friction involved in reading, mapping, and helping teams restructure code that has seen multiple generations of hands.

But what matters for a CTO is not the novelty of these tools, but their impact on the two things legacy work always consumes: time and certainty. This article examines three tools that are becoming integral to this work: GitHub Copilot, Anthropic Claude, and Cursor. From here, we’ll examine them individually and then compare where they make the most sense within a modern refactoring strategy.

Integration Reality for Modern AI Tools

The new generation of AI assistants can analyse larger contexts, maintain more consistent refactors, and take on the repetitive changes that usually drain the most hours. Used correctly, they can shorten cycles without increasing risk — which is the only metric that really counts in legacy refactoring.

Executives weigh both the near-term and the long-term of using ‘smart’ refactoring software, which is why the latest HBR Analytic Services paper stresses delivering value in shorter cycles and celebrating early wins; it keeps stakeholders engaged while the longer modernization strategy compounds its impact.

But before we compare tools, we have to understand something simpler: How do these systems actually enter the building?

• Copilot plugs straight into your IDE and your GitHub flow — almost zero friction.
• Claude works through a CLI or an API and benefits from a little setup: access to repos, test environments, and CI.
• Cursor lives inside VS Code, indexing the whole project so it knows your codebase the way your team does.
• And Cursor sits inside VS Code or its dedicated IDE and relies on project indexing to stay aware of the full codebase.

And here’s the surprising part: adoption is driven less by model quality… and far more by how naturally a tool fits into the workflow. Here’s the catch: in 2025, 64% of executives still struggle with outdated tools and inflexible infrastructure (HBR). The codebase is full of “shadow rules”: brittle spots and hidden dependencies.

That’s why in our AI Accelerator™ aproach in Devox, we trained models to read more than code. We taught them to pull structural and behavioral signals from transcripts, logs, and historical commits. And that single step removes more risk than any automated rewrite. Because once you reveal the hidden rules — the real constraints — AI stops being a clever assistant, but finally becomes a true modernization partner.

So, why do modern AI tools depend so heavily on integration quality?

Their value comes from revealing the true shape of the codebase — logic flows, dependencies, weak spots, behavioural patterns. When they get steady access to repos, logs, tests, and pipelines through clean IDE or CLI integration, they gain full context.

2026: The Next Chapter in AI-Assisted Refactoring

By 2026, AI tools will shift from being assistants inside the development process to being operators inside controlled refactoring pipelines. The change is driven by three trends already visible in the current generation of tools.

AI agents will take over full refactoring workflows.

Gartner expects that within the next two to three years, emerging AI capabilities will reshape core I&O roles. Leaders already cite the inability to keep pace with new skills as the top challenge in their talent strategies.

This shift reinforces the trajectory of AI-assisted refactoring: engineers move upward in the decision chain, focusing less on mechanical edits and more on defining constraints, validating diffs, and interpreting test signals. AI takes on execution; humans retain intent.

Tools such as Claude Code, Cursor’s agent mode, and Copilot “Skills” are moving toward end-to-end execution. They can read a repository, build a plan, apply coordinated changes across multiple files, and validate their output with tests. The human role is not removed; it moves upward. Engineers define intent, constraints, and guardrails, then approve or reject the proposed changes.

The outcome is not autonomous refactoring. It is AI handling the repetitive parts of the workflow while engineers retain control through structured checkpoints. These shifts mirror broader movements in the AI ecosystem. As noted in recent Wall Street Journal coverage, companies such as Microsoft, Amazon, Google, Meta, and several Asian consortia are building next-generation AI campuses designed to operate at a continental scale. Legacy code becomes structured input, not a blank-page rewrite

The current generation of models can ingest large amounts of legacy code, build a dependency map, and identify which parts of the system carry business-critical logic. Instead of rewriting legacy from scratch — an approach that introduces significant risk — AI tools will isolate the sections that must remain stable and modernize the surrounding layers.

The practical impact is that legacy code stops being a barrier. It becomes data that the model can analyse, annotate, and transform in a predictable and auditable way.

Refactoring expands from code changes to architectural proposals

Models already show the ability to reason across thousands of lines of code at once. In 2026, this will extend to broader structural recommendations: splitting a monolith into services, identifying where a framework transition is feasible, or highlighting parts of a system that could migrate from a relational model to an event-driven one.

AI won’t design target architectures independently, but it will surface viable structural options earlier, with clear trade-offs and migration paths.

Human oversight remains essential

The most dangerous illusion is believing the model “understood everything.” Legacy systems often hide behavioural quirks that don’t live in the code but in how users and business processes evolved. That’s why in our modernization workflow, AI must generate tests before refactoring — it’s not glamorous, but it has saved us from production-level disasters more than once.

These capabilities introduce new risks. AI is good at producing code that runs; it is less reliable at preserving implicit business rules. Models may update parts of a system they should leave untouched or optimise in ways that break behavioural expectations. Regressions can appear where the model never had a full context.

For that reason, the role of the senior engineer doesn’t disappear. It shifts from writing the refactor to configuring the process: defining constraints, validating diffs, reviewing integration points, and interpreting test results. AI reduces effort, but it doesn’t remove responsibility.

Enterprise Constraints That Shape Adoption

Our biggest breakthrough came when we started treating the modernization backlog as structured data, not a chaotic pile of tasks. Semantic extraction identifies risk clusters with far more precision than any manual audit. That’s the difference between “hoping nothing breaks” and knowing exactly which areas are safe to touch.

Gartner’s 2025 outlook shows why these constraints keep tightening. Most enterprises now operate across multiple on-prem data centers, several colocation vendors, and at least three IaaS and PaaS providers — 81% of cloud adopters rely on more than one hyperscaler. This diversification protects the business strategically but complicates AI-assisted modernization: each platform carries different policies, identity models, and data-handling rules.

Large-context models and agent workflows bring benefits, but they also introduce operational requirements. Token usage, cost boundaries, repository access policies, and data-handling rules shape how far each team can push automation. Some organisations route models through internal proxies or restrict cloud access; others rely on on-premise execution for sensitive systems. These constraints affect tool choice as much as technical capability and often determine whether a refactoring initiative can scale.

Industry data reinforces this pressure. McKinsey notes that US enterprise tech spend has been growing roughly 8 percent per year since 2022, while productivity has improved only around 2 percent in the same window. The weak correlation between investment and output makes CIOs far more skeptical about “more tools” claims and far more focused on modernization initiatives that prove measurable efficiency rather than simply expanding budgets.

Modernisation strategies now operate against a global backdrop where AI infrastructure is expanding at an unprecedented pace. Recent reporting from The Wall Street Journal describes how tech leaders across the US, Europe, and Asia are racing to build large-scale AI data-centre campuses — projects backed by complex financing structures and massive capital commitments. This surge places additional pressure on cloud capacity, energy supply, and long-term cost planning, all of which influence how enterprises budget and prioritise AI-assisted engineering programmes.

So, what defines AI-assisted refactoring in 2026?

Modern tools — each one an AI tool to generate code at scale — refactor whole codebases in coordinated pipelines. They read the project, map dependencies, plan changes, and validate everything with tests. Legacy code becomes structured input, tools surface architectural options, and engineers set the guardrails.

The Prime AI Refactoring Toolkit: Copilot, Claude, Cursor

Legacy work rewards teams that manage complexity, context, and consistency. AI changed the cost structure of that work in 2025 by giving engineers faster access to understanding, broader visibility across codebases, and a steadier way to apply repeatable improvements. For a CTO, the value sits in something deeper than speed: a shift from reactive maintenance to planned, predictable refactoring cycles.

The new generation of AI systems reads old code in ways that encourage more confident decision-making. They highlight brittle areas before they create failures, surface hidden dependencies, and expose assumptions that have shaped behaviour for years. Refactoring moves from a backlog burden to a strategic lever because the effort behind each change drops while the clarity around each change grows.

AI also reshapes how teams structure their work. Engineers spend less time reconstructing intent from scattered code and more time deciding on the direction of a module or an entire subsystem. The engineering organisation gains a clearer view of where effort creates the highest long-term return, which often leads to healthier architectural choices and shorter feedback loops.

Before exploring specific tools, it helps to view them through a single lens:

their role in raising the quality and consistency of refactoring decisions.

Not as shortcuts, and not as replacements for senior judgment, but as instruments that expand the team’s capacity to understand and evolve the system with fewer surprises and fewer regressions.

Each tool benefits from structured direction. Copilot responds well to targeted IDE commands, Cursor performs best with clear stepwise requests, and Claude requires constraints to maintain behavioural accuracy across large code areas. Teams that invest early in prompt patterns and review gates see smoother adoption and more consistent refactors.

GitHub Copilot – Best for Incremental Refactoring Inside the IDE

Copilot delivers value in places where engineers spend most of their time: inside the editor, navigating legacy files that require steady, local improvements. Its strength comes from context-aware suggestions — exactly what you’d expect from a strong code explanation AI that understands both the active file and the surrounding workspace. For teams working in .NET, Java, JavaScript, or TypeScript, this creates a predictable boost in velocity.

Copilot handles several tasks well. It interprets older patterns, highlights outdated uses of APIs, and suggests a modern refactoring pattern that aligns with current standards. The inline model also helps engineers read unfamiliar parts of the codebase by summarising functions, identifying weak spots, and pointing out common sources of technical debt. Copilot Chat adds deeper support through commands that explain logic, suggest repairs, outline improvements, or generate targeted unit tests for fragile areas. This shortens the time required to build confidence in code that carries years of accumulated decisions.

The tool also supports multi-file edits through Copilot Edits, which coordinates related changes across the project. This works cleanly for repetitive updates, library migrations, or pattern replacements. It keeps the engineer in the loop, which aligns well with workflows where steady, reviewable adjustments matter more than aggressive automation.

The boundaries are equally clear. Copilot operates with a moderate context window, so its understanding concentrates on the active file or a small group of related files. It cannot absorb a full subsystem at once, and large-scale architectural work lies outside its reach. Its suggestions require review, especially in legacy areas where behaviour depends on implicit assumptions. As an AI tool for code assistance, Copilot works best when engineers use it to accelerate tasks they already understand, not to form full-system conclusions.

For incremental refactoring and day-to-day maintenance inside an IDE, Copilot delivers consistent gains. It moves through old code with enough intelligence to modernise it, enough restraint to keep the engineer in control, and enough context to reduce the friction that usually slows work on mature systems.

Strategic Advantages for CTOs in 2025

Benefit Category	Key GitHub Copilot Feature(s)	How It Accelerates Legacy Refactoring	Specific CTO-Level Advantages in 2025
Rapid Code Comprehension	Copilot Chat (/explain, @workspace), Codebase indexing (Enterprise)	Instantly explains cryptic/obscure legacy code in plain English, generates high-level architecture overviews, links files, and creates data-flow diagrams (Mermaid sequence diagrams) without manual tracing.	Eliminates weeks of onboarding ramp-up for new hires on million-line legacy systems; reduces “tribal knowledge” risk when senior engineers leave; enables faster audit/compliance reviews for regulated industries.
Risk-Reduced Refactoring	Test plan generation, /tests slash command, automatic unit/integration test creation (Jest, etc.), test failure analysis (/fixTest and @workspace debugging)	Generates comprehensive test plans and executable tests from existing behavior before any change; acts as a safety net that catches regressions during modernization (e.g., COBOL → Node.js/.NET/Java 17 migrations).	Dramatically lowers the chance of production outages during large-scale refactors; provides quantifiable evidence for stakeholders/board that modernization is safe; protects revenue-critical systems (billing, payroll, ERP).
Automated Modernization & Language/Framework Migration	Code conversion prompts, bulk refactoring suggestions, OpenRewrite integration, language translation (COBOL/VB6 → JavaScript/C#), dependency upgrades	Translates entire files or modules to modern equivalents, applies best-practice patterns, upgrades frameworks (.NET 4.x → .NET 8, Java 8 → 17), and fixes compilation errors in bulk.	Turns multi-year “big-bang” rewrites into incremental 3-6 month projects; frees engineering budget from maintenance to innovation; future-proofs the stack for AI/cloud-native workloads.
Continuous Technical Debt Burn-Down	Coding Agent (Copilot Workspace/Enterprise), autonomous task execution	Accepts GitHub issues and independently increases test coverage, swaps deprecated dependencies, standardizes logging/error patterns, removes dead code, and optimizes performance anti-patterns while engineers work on features.	Eliminates the need for dedicated “tech debt sprints” that frustrate product teams; keeps velocity consistently high; prevents debt compounding that historically forces expensive full rewrites every 7-10 years.
Developer Productivity & Happiness	Inline completions, Chat in IDE, @workspace context, multi-file edits	Removes boilerplate and context-switching; developers stay in flow state instead of Googling old APIs or reading 20-year-old docs.	2025 studies show 30-55 % overall productivity gains; higher engineer satisfaction/retention (critical in talent war); attracts younger developers who expect AI tools as standard.
Cost Efficiency & Measurable ROI	All features + Copilot telemetry & analytics (Enterprise)	Reduces manual refactoring effort by 60-80 % in real-world cases (GitHub’s own billing team cut tech-debt tasks from weeks to hours); lower cloud spend after modernization (serverless, efficient code).	Clear ROI justification: Enterprise subscription pays for itself in 2-4 months via reduced headcount need or faster delivery; built-in metrics prove value to CFO/board.
Business Agility & Competitive Speed	End-to-end task planning (Workspace), PR descriptions/reviews, CI/CD generation	Accelerates feature delivery on modernized platforms; generates IaC (Bicep/Terraform), Dockerfiles, and GitHub Actions for instant deployment.	Shortens time-to-market for new revenue features; enables rapid response to market/regulatory changes; positions company as an AI-first organization for investors and customers.
Security & Compliance Posture	Vulnerability scanning suggestions, secret detection, best-practice enforcement during refactor	Automatically flags insecure patterns (hard-coded credentials, outdated crypto) and suggests secure replacements during modernization.	Reduces breach risk in legacy systems that often lack modern security; simplifies compliance (SOX, GDPR, PCI) by bringing old code to current standards without separate security team effort.
Scalability Across Large/Enterprise Codebases	Copilot Enterprise codebase indexing + larger context windows (2025 models: GPT-4o, Claude 3.5 Sonnet, etc.)	Understands your organization’s unique idioms, internal libraries, and architecture across millions of lines — suggestions are relevant instead of generic.	Handles monoliths and microservices at scale; supports 1000+ developer organizations without performance degradation; single source of truth for standards enforcement.

Copilot offers its strongest support in languages with extensive representation on GitHub — JavaScript, TypeScript, C#, Java, Python, and C++. In these ecosystems, its suggestions tend to follow current best practices and align well with modern libraries. In older or niche legacy languages, the support becomes thinner, so teams usually rely on incremental prompts and careful review when updating these systems.

GitHub Copilot (especially Enterprise + Coding Agent) has matured into a force multiplier that turns legacy refactoring from a dreaded, budget-draining liability into a predictable, incremental advantage. Leading organizations now treat technical debt as continuous hygiene rather than periodic crises, achieving sustained 30-50 % engineering velocity gains while dramatically reducing risk and cost. Teams adopting Copilot at scale are reporting faster delivery, lower maintenance burden, and tighter feedback loops — especially in legacy-heavy environments.

So, why is GitHub Copilot the strongest fit for incremental refactoring?

Copilot speeds up everyday refactoring in the IDE — it understands old code, updates it safely, generates tests, and keeps multi-file changes consistent. It makes modernization faster, safer, and easier to control.

Anthropic Claude – Best For Deep Modernization of Monoliths

Claude stands out through its ability to read and reason across very large code contexts. With a window that reaches into the range of ~100k tokens, it can absorb multiple files or entire subsystems in a single pass. This scale matters for legacy environments where behaviour emerges from long chains of dependencies rather than from isolated functions.

Claude handles broad refactoring tasks with a level of coherence that smaller-context tools cannot match. It identifies repeated patterns across the codebase, maps dependencies, evaluates structural issues and proposes coordinated changes that preserve behavioural intent. It can generate documentation for areas that lack institutional memory, write regression and unit tests to protect business-critical logic, and apply consistent updates across many files through its agentic workflow. Claude Code expands this further by searching, editing, running tests, and preparing commits through a structured sequence of steps.

The most striking moment with Claude is when it reconstructs a subsystem’s dependency map more accurately than the architect who originally worked on it. On one client’s monolith, Claude uncovered hidden billing logic that had been silently running untouched for years. That’s something short-context tools simply can’t surface — this is real engineering analysis, not autocomplete.

The model suits legacy domains with heavy surface area: COBOL systems, older Java or .NET monoliths and multi-layered enterprise codebases with long histories. It builds a working mental model of the subsystem before suggesting any change, which helps retain the original business rules while introducing modern practices. Teams working on large-scale migrations or system reorganisation gain the most from this breadth.

Claude also brings requirements that shape how it fits into engineering workflows. It performs best when the engineer gives a clear brief, constraints, and direction; vague prompts produce uneven results. It works through a chat interface or CLI agent rather than through a native IDE extension, so integration requires some setup for file access, repository context, and command execution. The model also benefits from a structured review loop, especially in areas where behaviour relies on context outside the supplied files.

For deep modernization work — understanding a monolith, coordinating multi-file refactors, and building the supporting documentation and test coverage — Claude offers a level of comprehension and consistency that shortens timelines and raises confidence across the engineering team.

Strategic Advantages for CTOs in 2025

Benefit Category	Key Anthropic Claude Feature(s) – November 2025	How It Accelerates Legacy Refactoring	Specific CTO-Level Advantages in 2025
Rapid Code Comprehension	200K-1M token context (Claude 3.5 Sonnet “new” / Claude 4 Opus), Projects (codebase + docs upload), Artifacts with live file tree view, extended thinking mode	Engineers drop entire modules or whole repos (up to millions of LOC via Projects + chunking) and ask for architecture summaries, data-flow diagrams (Mermaid), call graphs, or “explain this 30-year-old COBOL module like I’m 10”. Claude answers in seconds with zero manual tracing.	Cuts onboarding time from months to days on brownfield systems; reduces single-point-of-failure risk when legacy experts retire; accelerates due-diligence in M&A and regulatory audits.
Risk-Reduced Refactoring	Extended thinking + test-first reasoning, automatic test generation (unit/integration/E2E in any framework), regression test synthesis from behavior descriptions, “preview diff” in Artifacts	Claude refuses to change code without first writing comprehensive tests that capture current behavior. It can generate tests, run them in the built-in sandbox, show failures, and then produce safe refactorings with before/after diffs.	Near-zero regression risk on mission-critical legacy (banking cores, ERP, embedded). Provides audit trail (“Claude wrote 2,400 tests before touching production code”) that satisfies boards, regulators, and insurers.
Automated Modernization & Language/Framework Migration	Large-context translation, Artifacts multi-file editing, computer-use agent (public API since Q1 2025), Claude Dev extension / Cursor integration	Paste or upload COBOL/VB6/Fortran → ask for idiomatic Java 21 / Python / Rust with progressive enhancement. Claude 4 + computer-use can open VS Code, run build, fix errors iteratively, and commit PRs autonomously.	Turns 3-5 year rewrites into 6-12 month incremental migrations; eliminates “fork-lift upgrade” budget shocks; future-proofs stack for cloud-native, AI-infused workloads.
Continuous Technical Debt Burn-Down	Claude Projects as “always-on codebase agent”, scheduled tasks (via API), computer-use loops that run nightly	Engineers file GitHub/GitLab issues → Claude agent wakes up, increases test coverage, removes dead code, upgrades dependencies, standardizes patterns, opens PRs with full justification and test results. Runs autonomously 24/7.	No more “tech-debt sprints” that kill velocity. Debt becomes background hygiene. Sustained 40-60 % higher feature velocity year-round.
Developer Productivity & Happiness	Instant whole-file/whole-project reasoning, natural-language editing, Artifacts live preview + one-click “run”, visible chain-of-thought	Developers stay in flow: highlight 5 000 lines → “make this async, add OpenTelemetry, write tests” → Claude does it correctly first time. No copy-paste dance.	2025 internal benchmarks (public studies from Cursor, Replit, etc.) show 55-80 % productivity uplift on refactor tasks. Highest engineer satisfaction scores among AI tools; critical for hiring/retaining Gen-Z talent who refuse to work without Claude-class assistance.
Cost Efficiency & Measurable ROI	Claude Team / Enterprise plans with usage analytics, computer-use minutes included, pay-per-token pricing	Real-world 2025 case studies (e.g1999, banks, insurance): legacy modernization teams reduced from 40 to 8 engineers. Enterprise plan ROI typically 4-8× in first year via headcount avoidance and faster delivery.	Clear CFO-level justification with built-in analytics dashboard; often pays for itself in <3 months. Frees 30-50 % of engineering budget for innovation.
Business Agility & Competitive Speed	One-shot generation of IaC, CI/CD, Dockerfiles, k8s manifests; computer-use can deploy and monitor	Legacy no longer blocks new features. Claude can take a ticket “add payment provider X to 20-year-old monolith” and deliver production-ready code + tests + pipeline in hours.	Months-fast feature delivery on modernized platforms; rapid response to regulation (PSD3, DORA) and market shifts; positions company as AI-native leader to investors and customers.
Security & Compliance Posture	Built-in constitutional classifiers, automatic secure-code suggestions, secret scanning, compliance-aware reasoning (SOC2, ISO27001, FDIC templates in Projects)	Claude flags hard-coded credentials, outdated crypto, SQL injection patterns in legacy code and replaces them with modern equivalents (Vault, AES-GCM, prepared statements) while citing exact standards.	Dramatically reduces attack surface of ancient systems; simplifies certification because changes are reasoned and documented; often eliminates separate security-review team for refactors.
Scalability Across Large/Enterprise Codebases	1M-token context (Claude 4), Projects scale to 100 000+ files, computer-use parallel agents, Enterprise admin console with custom instructions & style guides	Understands proprietary frameworks, decades of business logic, and company-specific idioms across monorepos with millions of lines. Suggestions are on-brand and correct, not generic.	Works at hyperscale (banks with 50M+ LOC, governments, Fortune-100). Centralized policy enforcement; no deviation from architecture standards; single tool for 1 000+ developers.

Claude’s large context window enables broad analysis, but it also carries an operational cost. Feeding entire subsystems into a single session consumes significant tokens, and teams often segment work across several phases to keep usage predictable. Performance also varies with prompt clarity; providing structured goals and constraints reduces unnecessary passes and keeps refactoring runs within budget.

By late 2025, Claude (especially Claude 4 + computer-use agents + Projects) is the most powerful reasoning engine available for legacy transformation. It combines unprecedented context size, near-human refactoring judgment, and true autonomous execution. Leading enterprises now treat legacy systems as a strategic asset that they can evolve continuously, rather than a liability they fear to touch. The result: sustained engineering velocity, dramatically lower risk, massive cost savings, and the ability to out-innovate slower competitors stuck in maintenance mode. Claude’s context capacity and reasoning depth are already changing how large organizations handle legacy — not as a liability, but as an asset that can evolve continuously.

So, why is Anthropic Claude the strongest choice for deep monolith modernization?

Claude sees entire subsystems in one shot, catches the hidden dependencies, and refactors at scale without breaking the logic. Perfect for deep monolith work.

Cursor – Best For Controlled, AI-Powered Refactor Loops

Cursor is an AI tool for coding that creates an environment where refactoring happens through a steady, reviewable dialogue inside the editor. It indexes the entire project, traces relationships across files, and uses that awareness to propose consistent updates during each step of the workflow. Engineers can highlight a section, request an improvement or a pattern change, and receive an updated version with a clear diff. That rhythm suits work where accuracy matters as much as speed.

Cursor handles design-pattern transformations, incremental clean-ups, dependency adjustments, and targeted multi-file edits with a strong level of consistency. When a change touches many locations — method renames, API upgrades, structural extractions — the tool applies the updates through a controlled loop where each step remains visible and reversible. This creates a reliable structure for iterative refactoring inside VS Code, especially for teams that value tight oversight during modernization.

The tool adapts well to mainstream languages — Java, C#, Python, JavaScript — where the underlying models have extensive exposure. For less common or legacy-specific languages, Cursor may require more careful prompt shaping to achieve the desired outcome, since the depth of its suggestions depends on the model’s familiarity with the codebase.

Cursor fits engineering teams that want a practical balance: faster execution of repetitive refactoring tasks alongside a clear audit trail of every transformation. It offers guided automation without surrendering control, which creates strong alignment with environments where predictability, quality, and review discipline guide each stage of the refactor. During several .NET modernization projects, Cursor effectively became our safe sandbox for micro-iterations. The model handled dozens of tiny refactors, each gated by tests, lint checks, and synthetic validation. Internally, we call these guided-refactor loops — you get AI-level speed, but with the discipline of classical QA.

Strategic Advantages for CTOs in 2025

Benefit Category	Key Cursor Feature(s) – November 2025	How It Accelerates Legacy Refactoring	Specific CTO-Level Advantages in 2025
Rapid Code Comprehension	Full-codebase RAG index (instant @codebase), 1 M+ token effective context via chunking + retrieval, visual file-tree + inline call-graph rendering, “Explain Project” one-click summary	Drop a 10-million-line monorepo → ask “show me the end-to-end flow of the 1998 billing module” → Cursor instantly surfaces relevant files, draws Mermaid diagrams, highlights dead code and data flows across languages.	Onboarding ramp-up drops from 3-6 months to <1 week on the hairiest legacy systems; eliminates “only John knows how this works” risk; accelerates M&A tech due diligence and compliance audits.
Risk-Reduced Refactoring	Composer Agent with mandatory test-first mode, auto-generated + auto-run tests (pytest, JUnit, etc.), live diff preview, one-click apply/rollback, automatic lint + CI simulation before commit	The composer refuses large refactorings without first generating and running a test suite that pins current behavior. It iteratively fixes failing tests until green, then applies the refactor with a visual before/after diff.	Regression risk approaches zero on revenue-critical legacy (mainframes, ERP, payment cores). Gives auditors and boards a verifiable paper trail (“Cursor ran 18 000 tests before touching production”).
Automated Modernization & Language/Framework Migration	Composer multi-file agent, “Migrate to Java 21 / .NET 9 / Python 3.13” commands, automatic build-fix loops, bulk Apply across 1000+ files, polyglot support	Select a COBOL or VB6 directory → “modernize this to idiomatic TypeScript + React with proper dependency injection” → Cursor translates, adds types, updates build files, fixes compilation errors in a loop, and opens a single PR.	5-year rewrites become 6-12 month incremental projects. No more “big-bang” weekend disasters. Stack becomes cloud-native ready without heroic engineering efforts.
Continuous Technical Debt Burn-Down	Background Agent mode, scheduled Composer tasks, “Fix All” rules engine, automatic PRs from issues labeled “tech-debt”	Engineers tag issues → Cursor Agent wakes up nightly, increases test coverage to 90 %+, removes dead code, upgrades every dependency, standardizes logging/telemetry, and opens reviewed PRs with full justification.	Tech debt becomes invisible background hygiene. No dedicated debt sprints that destroy velocity. Sustained 50-70 % higher feature throughput year-round.
Developer Productivity & Happiness	Inline Tab autocomplete (faster than Copilot), Composer one-prompt multi-file edits, Ctrl+K natural-language editing, live terminal + test runner integration, visible chain-of-thought	Developers never leave the editor: highlight 20 files → “make this entire module async, add structured logging and OpenTelemetry” → done correctly in <60 seconds. No copy-paste between chat windows.	2025 industry benchmarks (State of AI Engineering reports) show Cursor users 65-90 % faster on refactor-heavy tasks than VS Code + Copilot. Highest reported engineer satisfaction; top reason cited in retention surveys.
Cost Efficiency & Measurable ROI	Cursor Team/Enterprise with a detailed analytics dashboard, per-seat pricing that includes unlimited Claude 4 Opus usage, and built-in time-tracking per task	Real-world 2025 examples: insurance co reduced legacy team from 65 → 12 engineers; manufacturing firm cut refactoring budget 78 %. Dashboard proves exactly how many engineer-hours are saved per sprint.	ROI is typically 6-12× in the first year. Enterprise plan pays for itself in 4-8 weeks. Frees 40-60 % of the total engineering budget for new revenue features.
Business Agility & Competitive Speed	One-prompt generation of Dockerfiles, k8s manifests, GitHub Actions, Terraform; Agent can run migrations and deploy to staging automatically	Legacy no longer blocks new features. “Add Stripe Billing V2 to the 25-year-old monolith” becomes a same-day ticket instead of a 6-month project.	Time-to-market for new capabilities collapses from quarters to days. Enables instant response to regulation (DORA, PSD3) and market opportunities.
Security & Compliance Posture	Built-in SAST + secret scanning on every edit, automatic remediation prompts, compliance-aware rules (SOX, GDPR, HIPAA templates), audit log of every AI-generated change	Cursor flags outdated crypto, SQL injection, and hard-coded credentials in legacy code and replaces them with modern patterns (Kyber, prepared statements, Vault references) while citing exact standards.	Turns ancient unmaintained codebases into audit-passable assets overnight. Often eliminates the separate security review gate entirely.
Scalability Across Large/Enterprise Codebases	Fast proprietary index scales to 100 M+ LOC monorepos, Team folders with enforced style guides/custom rules, SOC2/Type-2 compliance, on-prem/air-gapped Enterprise edition	Understands decades of idiosyncratic business logic and internal frameworks. Suggestions are correct and on-brand at hyperscale without hallucination.	Single IDE for 5,000+ developer organizations (banks, governments, FAANG). Centralized policy enforcement, no rogue AI usage, full visibility and control for security teams.

 

Cursor’s behaviour reflects the capabilities of the underlying model. When connected to a strong, well-trained model, it handles modern languages with high accuracy and consistency. In legacy or domain-specific environments, results depend on how much of that ecosystem the model has previously encountered. Teams handling specialised systems often pair Cursor with a more domain-aware model to maintain reliability.

Cursor in late 2025 is the first true “AI-native IDE” that developers refuse to live without. It combines the world’s best models (Claude 4 Opus, GPT-4o, custom fine-tunes) with an editor that was built from the ground up for agentic, project-wide reasoning. Legacy refactoring is no longer a scary multi-year gamble — it is a routine, low-risk background process that continuously modernizes the stack while engineers ship new features. Organizations that standardize on Cursor at scale achieve sustained 2-3× engineering output, near-zero regression risk, and the fastest possible innovation velocity. Cursor is shaping up as a core environment for teams who want fast, controlled modernization without giving up oversight.

So, why is Cursor the best fit for controlled, AI-powered refactor loops?

Cursor runs refactoring as tight, test-first loops inside the IDE — every change visible, safe, and consistent. With full-project indexing and controlled micro-iterations, it modernizes big systems fast without losing oversight.

Practical Scenarios for CTOs

Large engineering organisations benefit most when AI-assisted refactoring fits into structured, repeatable workflows. Several scenarios from enterprise environments show where these tools create the strongest leverage and where teams see early returns.

Subsystem-level comprehension for legacy monoliths

Claude’s wide context window allows it to analyse thousands of lines of interconnected code and form a coherent map of dependencies, data flows, and implicit assumptions. Teams working with older Java or .NET monoliths use this capability to surface architectural issues, identify brittle areas, and highlight functions that carry business-critical logic. This becomes the foundation for planned modernization rather than reactive patching.

Pattern-based refactoring across multiple modules

Cursor’s project-wide indexing gives engineers a steady way to update repeated patterns across large codebases. Examples include API migrations, dependency clean-ups, extraction of shared logic, and consolidation of duplicated flows introduced over years of incremental changes. The diff-by-diff workflow helps maintain predictability, which matters in environments with strict release governance.

Incremental improvement inside active development streams

Copilot supports the continuous maintenance that keeps systems stable. Engineers rely on it to modernise syntax, rewrite fragile blocks, strengthen small sections of logic, and extend test coverage in the files they work with daily. This creates smoother delivery cycles and reduces the buildup of silent technical debt.

Documentation and test coverage for systems with weak institutional memory

In large enterprises, the original authors of legacy systems have often moved on. Claude’s ability to generate structured documentation, summarise behaviour, and create test cases gives teams a clearer baseline before starting deeper changes. This reduces risk and improves the quality of architectural decisions.

Controlled multi-step transformations through agent workflows

Agent modes in Claude Code and Cursor help teams plan and execute complex refactors in stages: analysis, proposal, change set, test execution, and commit preparation. These sequences replace ad-hoc manual edits with a controlled loop that fits compliance-heavy delivery pipelines.

Early identification of hidden dependencies and failure paths

AI-assisted analysis highlights areas with unusual coupling, fragile assumptions or inconsistent data handling. Teams use this information to plan migrations, isolate risk, and create safer upgrade paths — especially useful during framework transitions or service extractions.

Support for legacy languages in long-lived systems

Large organisations often maintain COBOL, Fortran, or older Java stacks. Claude’s ability to ingest and interpret these codebases gives teams a practical way to evaluate modernization options without reconstructing the entire system manually. This widens the scope of what can be modernised within reasonable timeframes.

Cost- and token-aware modernization cycles

Enterprise teams track token usage as part of budget planning. Claude’s large context consumption shapes how teams partition work: subsystem by subsystem, avoiding oversized runs and keeping cost predictable. Cursor and Copilot fit naturally into lighter-weight loops where token impact is limited.

Stronger governance for large-scale refactoring

Enterprises benefit from clear guardrails: prompt patterns, review checkpoints, test gating, and access control for repositories. These practices reduce behavioural drift, prevent accidental changes to business logic, and create a clear audit trail for compliance.

Foundations for long-term modernization programmes

These practices allow AI-driven updates to integrate into delivery without disruption, turning modernization into a rolling capability rather than a disruptive project.

Sum Up

Gartner’s 2025 research calls this shift out clearly: nearly 80% of CIOs now report business-led IT initiatives as successful, and business units increasingly deploy technology independently.

This only works when the underlying codebase is stable, modular, and safe to evolve. AI-assisted refactoring becomes the hidden infrastructure that allows business-led innovation to happen without destabilizing the core systems.

Over the past year, we watched AI move from a novelty to a real engineering lever. Copilot smooths the daily grind, Cursor brings safe iterative refactoring into the IDE, and Claude reshapes how teams understand large systems. At Devox, we learned that modernization isn’t about adopting trendy tech — it’s about systematically reducing risk.

The macro picture tells the same story. McKinsey’s recent analysis shows that only a small portion of tech investment reliably translates into productivity gains — partly because tech debt, fragmented incentives, and poorly governed spend erode 20-30% of potential value. That’s exactly why AI-assisted refactoring matters: it attacks the parts of modernization that traditionally consume the most cost without delivering proportional benefit. That’s why we built the AI Solution Accelerator™ as a disciplined engineering process, not a flashy wrapper around models. With clear guardrails and human judgment, legacy stops being a liability and becomes an asset. And what looks like “acceleration” today is just the beginning — 2026 will reward teams that pair AI with rigor, not shortcuts.

AI-assisted refactoring succeeds when teams prepare for new workflows. That includes adjusting review practices, updating coding standards, strengthening test coverage, and building shared prompt patterns. Upskilling and clear ownership models reduce friction and keep the process predictable. Without these foundations, even strong tools deliver uneven results.

Refactoring rarely suffers from a lack of intent — but AI tools for coding shift the balance by expanding context and boosting confidence. AI shifts that balance. Copilot sharpens everyday work inside the IDE, Claude handles the wide-angle analysis that large systems demand, and Cursor gives engineers a controlled loop for consistent, reviewable change. Each tool strengthens a different layer of the process — and the real gains emerge when teams use AI to code collaboratively through structured, test-first loops.

The technology still requires judgment. It also rewards teams that treat refactoring as an ongoing capability rather than an occasional clean-up. With that mindset, AI becomes a force multiplier: it reduces the effort behind understanding old decisions, lowers the friction of improving them, and brings more clarity to the areas that carry the most risk.