Legacy rewrites have a well-documented track record: roughly 70–80% fail to meet their goals, exceed budget dramatically, or get cancelled before completion. The projects that succeed tend to share a few characteristics — small senior teams, clear scope boundaries, phased delivery — but the timeline is almost always years, not months. A 200,000-line ColdFusion or OutSystems codebase migrated manually typically takes 18 to 36 months under optimistic conditions. Most enterprise teams don't have 18 months of runway to run a parallel migration while keeping the production system operational.
AI-accelerated migration promises to compress that timeline by 3–5×. That's a claim worth examining carefully, because there's a meaningful difference between "using AI" and doing something that actually speeds up a migration. Here's what the acceleration actually comes from — and where the limits are.
Why Traditional Rewrites Take So Long
The slow parts of a manual rewrite aren't what most teams expect. Writing the new code is rarely the bottleneck. The actual time sinks are understanding what the old code does, making architectural decisions about the target system, and validating that the new system produces the same outputs as the legacy one.
Understanding legacy code is expensive because it's almost never documented accurately. Business logic accumulates in layers over years: a stored procedure modified in 2011 to handle an edge case no one remembers, a CF custom tag that does something slightly different from what its name suggests, a cfc component that's called in three different ways depending on which application is invoking it. Developers assigned to migration projects spend weeks or months in discovery mode before they can confidently write a single line of target-language code.
Architectural decisions compound the problem. A ColdFusion application written in 2008 doesn't have a clean mapping to Spring Boot's service layer model. Someone has to decide how to structure the target code — not just translate the old code statement by statement, but make real design choices about where business logic lives, how data flows, and how modules should be separated. On a manual rewrite, those decisions happen slowly, through team discussion, documentation, and iteration. They're also frequently revisited mid-project as hidden dependencies surface.
Finally, validation is slow by default. Without automated equivalence testing — a system that can run the same inputs through both old and new code and compare outputs — teams validate manually, which is labor-intensive and incomplete. Defects in migrated business logic often surface in production, months after the rewrite was declared complete.
What AI Actually Does Differently
The AI techniques that actually move the needle on migration timelines operate at the parsing and analysis layer, not the generation layer. This is a critical distinction that separates genuine acceleration from marketing language.
Asking an LLM to "rewrite this ColdFusion file in Java" produces plausible-looking code that is often wrong in ways that aren't immediately visible. LLMs are excellent at capturing surface syntax but poor at preserving semantic correctness across business logic that depends on subtle state, execution order, or side effects. They also hallucinate: inserting logic that wasn't present, omitting logic that was, or generating code that passes obvious test cases but fails under the specific data patterns your business has accumulated over fifteen years. LLM-generated migration code requires expert review on every file — which doesn't meaningfully reduce the review burden versus a manual rewrite.
Deterministic parsing works differently. At Adapt, the first step is parsing the source codebase — OML, CFML, Oracle Forms metadata, or other legacy format — into an abstract syntax tree (AST) or intermediate representation that captures business logic structurally, not textually. This representation is language-agnostic: it knows that a particular function validates a date range, or that a particular screen maps three inputs to two database writes, without encoding those facts as ColdFusion syntax. The AST becomes the authoritative model of what the application does.
AI then operates on this structured representation rather than on raw source code. Instead of translating CF tags to Java statements (which is where hallucination happens), the AI maps abstract business logic constructs to idiomatic target-language patterns — and does so consistently across the entire codebase, because it's working from a normalized model rather than file-by-file text. Consistency is itself a major time savings: it eliminates the variation that makes manual review so expensive.
The Architecture That Unlocks Speed
Parsing and generation alone don't deliver 3–5× acceleration. The delivery architecture matters as much as the tooling.
Traditional SI rewrites staff large teams — sometimes 15 to 30 engineers — because that's how they absorb scope uncertainty. Large teams create coordination overhead, inconsistent design decisions, and quality variation that requires extensive management and review. The more engineers touch the codebase, the more defects get introduced and the more senior time gets consumed in code review rather than migration.
AI-accelerated delivery works with small, senior-led teams because the factory handles volume. A senior architect defines the target architecture and makes structural decisions once, at the beginning of the engagement. Those decisions are encoded into the generation pipeline so that they're applied consistently across every module — not re-debated for each file by a junior developer. The senior team's time concentrates on the work only humans can do well: validating equivalence, handling exceptions that the parser flags as ambiguous, and making the architectural decisions that define the target system's quality.
Phased delivery reinforces this. Rather than running a complete parallel rewrite and doing a big-bang cutover, a well-structured AI migration delivers working functionality in phases — module by module, or application by application — with each phase validated in production before the next begins. This eliminates the risk concentration of big-bang cutovers, reduces the discovery problem (you find edge cases in Phase 1, before they affect Phase 4), and means the client starts receiving value in weeks rather than years.
Evaluating AI migration vendors? Start with the audit.
The Replatforming Audit maps your source codebase, identifies complexity hotspots, and produces a phased migration plan with timeline and cost estimates — before you commit to a factory engagement.
Book a discovery callWhere the 3–5× Claim Comes From
The acceleration factors stack multiplicatively, not additively.
Discovery is faster because the AST parser systematically maps the entire codebase in days rather than the weeks or months a human team needs to read, annotate, and discuss it. Architectural decisions happen once, at the start of the engagement, rather than being relitigated throughout. Code generation is parallel and consistent rather than sequential and variable. Validation is automated for equivalence on the business logic that the parser captured deterministically — reducing manual review to the genuinely ambiguous cases rather than covering the entire codebase.
McKinsey's analysis of generative AI in application modernization cites 40–50% timeline reductions as a baseline for AI-augmented approaches. Gartner projects that by 2029, 90% of enterprise software modernization will use AI-augmented tools — up from less than 15% today — which suggests the industry has reached a consensus that the acceleration is real. Adapt's own engagements have shown specific cases where legacy platforms were replaced in approximately five months against estimates of 18–24 months using traditional approaches — consistent with the 3–5× range across different source stacks and codebase sizes.
The acceleration is not uniform. Simple applications with clean architecture migrate faster. Applications with 20 years of accumulated procedural logic, database-level business rules, and undocumented edge cases take longer — but they take longer under any approach, and the gap between AI-accelerated and manual timelines grows with complexity, not shrinks.
What This Means When Evaluating Migration Options
If you're evaluating migration options for an OutSystems, ColdFusion, Oracle Forms, or other legacy estate, the right questions to ask an AI migration vendor are specific: What exactly does your tool parse? What does it generate? Where does human review happen, and how is it structured? What does the equivalence testing framework look like? What happens when the parser encounters code it can't confidently map?
Vague answers to those questions — "our AI handles it," "the model generates idiomatic code" — are signals that the offering is LLM-translation dressed up as something more rigorous. The genuine approaches produce artifacts from the parsing phase that you can inspect: dependency graphs, business logic maps, flagged exceptions. Ask to see them.
Adapt's factory process starts with an audit that produces exactly these artifacts — before any commitment to the migration engagement. For ColdFusion shops specifically, we walk through the implications of the current support timeline and help you understand which applications are candidates for AI-accelerated migration versus simpler approaches like Lucee or targeted rewrites. For OutSystems, we've modeled the five-year TCO of staying versus exiting across dozens of engagements, and the economics of a phased AI migration typically become compelling well before the third renewal. Reach out if you'd like to run those numbers against your actual portfolio.