Every enterprise technology vendor is now claiming that AI will "solve" migration. The pitch is compelling: point an AI model at your legacy COBOL, Java 6, or monolithic .NET codebase, and it generates the equivalent code in a modern stack. Migration in weeks instead of years. Effort reduction of 80% or more.
The reality is more nuanced than the pitch. But the reality is also more interesting than the sceptics suggest. AI-assisted migration is producing measurable results on real enterprise codebases. The results are uneven, the limitations are significant, and the enterprise migration risk has not gone away. But the economics of migration are genuinely changing, and DACH mid-market companies need to understand what that means for their planning.
What we tested
Over the past 12 months, we have run AI-assisted migration benchmarks on representative legacy modules from real enterprise systems. Not toy examples. Not greenfield code. Production modules from insurance platforms, industrial ERP extensions, and retail order management systems.
The test protocol is straightforward: take a legacy module, use current-generation AI tools to port it to a modern stack, measure the effort compared to a traditional manual migration of equivalent scope. The comparison baseline is not theoretical. It is actual project data from migrations we have executed in prior engagements.
The results are consistent enough to draw conclusions, with important caveats.
The headline number: 30-40% effort reduction
Across the modules we tested, AI-assisted migration reduced the code porting effort by 30-40% compared to traditional manual migration. This is the effort for the code translation itself, specifically converting legacy code to equivalent functionality on the target platform.
That number is real. It is reproducible. And it is significant enough to change the economics of migration planning.
But it requires careful interpretation.
What AI handles well
The 30-40% effort reduction comes from specific tasks where AI models excel:
Syntax translation. Converting code from one language to another, including idiomatic differences, library mappings, and framework-specific patterns. A Java 6 module with Struts patterns translating to a Spring Boot equivalent. A VB.NET batch processor becoming a Python service. The AI handles the mechanical translation work that traditionally consumed the largest block of developer hours.
Pattern conversion. Legacy codebases are full of repeated patterns: data access code, validation logic, serialization routines, error handling. AI models recognise these patterns and generate the equivalent modern implementation. Where a developer might spend a day converting 20 similar data access methods, the AI generates all 20 in minutes with manual review.
Boilerplate generation. Modern frameworks require significant scaffolding: configuration files, dependency declarations, test harnesses, deployment descriptors. AI generates this reliably and saves the tedious setup work that adds up across a large migration.
Documentation extraction. Legacy code often has sparse documentation. AI models can read the code, infer intent, and generate documentation for the migrated codebase. This is not just a migration benefit. It creates an asset that did not exist before: readable documentation of what the system actually does.
These tasks share a common characteristic: they are mechanical, repetitive, and pattern-based. They are exactly the kind of work where AI augmentation delivers the strongest results.
What AI does not handle
The 30-40% effort reduction sounds large until you understand what the remaining 60-70% consists of. This is where the vendor pitch diverges from enterprise reality.
Testing effort. Code translation is one phase of migration. Testing is another, and in enterprise migration, testing typically consumes 40-50% of total effort. AI can generate test scaffolding, but it cannot validate that the migrated system behaves correctly in your specific business context. Does the claims processing workflow still calculate the right reserve amount for a partial liability case? Does the order management system still apply the correct discount tiers for framework contract customers? These are domain-specific validations that require human judgement and business knowledge.
AI-generated code that "looks right" is not the same as code that works correctly in production. Testing remains fundamentally human work, and it does not shrink because the code was generated faster.
Regression risk. Every migration introduces regression risk. The migrated system may behave differently from the legacy system in ways that are not caught by tests. Subtle differences in floating-point precision, date handling, character encoding, or concurrency behaviour can create data inconsistencies that only surface weeks or months after cutover. AI-generated code has the same regression risk profile as manually written code. In some cases, it is higher, because the developer who reviewed the generated code may have less insight into edge cases than the developer who wrote it manually.
Integration validation. Legacy systems do not exist in isolation. They connect to other systems through APIs, database links, file exchanges, and message queues. Migrating the code does not migrate the integrations. Every upstream and downstream connection needs to be validated, and many need to be rebuilt. An insurance claims system that communicates with a reinsurance platform through a proprietary interface does not get a free integration migration just because the claims code was ported.
Data reconciliation. Migration typically involves data migration: moving data from legacy schemas to new ones, transforming formats, resolving inconsistencies, and validating completeness. This work is data-specific and domain-specific. AI tools can help with schema mapping, but the reconciliation itself requires deep understanding of what the data means in business terms.
Permission and security redesign. Legacy systems often have permission models that evolved over decades: role-based access mixed with individual overrides, implicit permissions embedded in business logic, security checks scattered across the codebase. Migrating the code does not migrate the security model. The new platform needs a coherent security architecture, and building it requires understanding the intent behind the legacy permissions, not just their implementation.
Process redesign. The most valuable migrations do not just port code. They redesign the processes that the code supports. A claims intake workflow that was designed around manual data entry should not be migrated as-is to a modern platform. It should be redesigned for digital intake, with AI-powered document processing and automated triage. AI tools cannot make process redesign decisions. They can translate code, but they cannot tell you whether the process the code implements is still the right one.
Production stabilisation. The first weeks after cutover are the most dangerous period of any migration. Issues surface that no test environment caught. Performance behaves differently under real load. Users encounter edge cases that were not anticipated. Stabilisation requires experienced engineers with deep knowledge of both the legacy and modern systems. AI tools do not help here.
The honest assessment
AI-assisted migration reduces code porting cost faster than expected. That is the good news, and it is genuinely significant. For a migration where code porting represents 30% of total effort, a 40% reduction in porting effort means roughly 12% reduction in total migration cost. That is meaningful. It is not transformative.
The tasks that AI does not handle, specifically testing, integration, data migration, security, and stabilisation, collectively represent the majority of enterprise migration effort. These tasks are not getting cheaper at the same rate. Some of them are inherently irreducible, because they require business judgement, domain knowledge, and organisational coordination that no tool can automate.
The bottom line: AI does not eliminate enterprise migration risk. It reduces one significant component of migration cost and will likely reduce it further over time. That changes the planning calculus, but it does not change the fundamental nature of enterprise migration as a complex, risk-laden programme of work.
What this means for planning
For DACH mid-market companies planning modernisation, AI-assisted migration creates three practical implications:
Migration cost is no longer a fixed number
Traditional migration estimates assume a constant cost per module, adjusted for complexity. AI-assisted migration introduces a time-dependent variable: the cost of porting declines as AI tools improve. This matters for migration sequencing. Modules that can wait should be scheduled later in the programme, when tools are better.
This is the core insight behind the AI-shift migration strategy: if migration cost is declining, creating value now and migrating later may produce better total outcomes than migrating immediately.
Do not bet everything on future capability
The trajectory of AI migration tools is positive, but the pace of improvement is uncertain. Planning a migration programme on the assumption that AI will reduce effort by 70% in two years is speculative. Planning on the basis that current 30-40% code porting reduction will improve modestly is conservative and defensible.
The pragmatic approach: use current AI capabilities in your migration work today. Capture the 30-40% code porting reduction that is available now. Monitor improvements. Adjust plans as capabilities evolve. Do not defer action based on hypothetical future tools.
Benchmark regularly on your own code
Vendor benchmarks, including ours, are based on specific codebases and specific tool versions. Your legacy code has its own characteristics: language, framework, complexity, domain logic density. What works well on a Java retail system may work poorly on a COBOL insurance core. The only benchmark that matters is the one you run on your own modules.
We recommend running a benchmark every quarter. Take a representative legacy module, ideally one you have already migrated manually so you have a baseline, and test current AI tools against it. Measure the effort. Compare it to the last benchmark. Track the trend. This gives you organisation-specific data for migration decision gates.
Practical guardrails for AI-assisted migration
If you are using AI tools in a current or planned migration, these guardrails reduce risk:
Do not skip code review. AI-generated code must be reviewed with the same rigour as human-written code. In practice, this means developers who understand the business logic need to validate the output, not just check syntax.
Do not reduce testing scope. The temptation when code is generated faster is to test less. This is exactly backwards. AI-generated code should be tested more rigorously because the developer has less implicit knowledge of the implementation decisions than they would with code they wrote manually.
Do not rely on a single tool benchmark. AI migration tools vary significantly in capability. Test multiple tools. Test them on different module types. The tool that handles data access patterns well may struggle with complex business logic. Build a multi-tool approach where each tool is used for its strengths.
Maintain manual migration capability. AI tools will not handle every module. Some legacy code is too idiosyncratic, too poorly documented, or too tightly coupled for automated porting. Your migration team needs the skills and processes to handle these modules manually. Do not build a programme that assumes 100% AI coverage.
Keep decision gates. Every six months, assess whether the AI-assisted approach is delivering the expected effort reduction. If it is not, adjust. If it is exceeding expectations, accelerate. The worst outcome is a migration programme that runs on assumptions that were never validated.
Where this goes next
The current generation of AI migration tools handles syntax translation and pattern conversion well. The next generation will likely handle more complex transformations: business rule extraction, automated test generation from legacy behaviour, and integration pattern mapping. These capabilities are in research and early prototype stages. They are not production-ready today.
When they arrive, they will further shift the economics. The 30-40% code porting reduction may become 50-60%. The total migration effort reduction may move from 12% to 20-25%. That is meaningful but still far from the "AI solves migration" narrative that vendors promote.
The practical implication: plan for incremental improvement, not revolution. Use what works today. Monitor what is coming. Adjust as capabilities mature. And never lose sight of the fact that code porting is only one part of enterprise migration. The hard parts, the parts that require human judgement and organisational change, remain hard.
What to do next
If you are planning or executing a migration, start by benchmarking AI tools on your own codebase. If you are considering whether to migrate now or wait, read the AI-Shift Migration Strategy for the decision framework. If you want a structured assessment of your legacy situation before making any decision, the Legacy Readiness Check provides the diagnostic.
For a direct conversation about how AI-assisted migration fits into your specific modernisation plan, book a Fit Call.
This article is part of the Legacy Modernization for AI series by Andreas Anding. For the build-vs-buy question in AI initiatives, see Build vs. Buy for Enterprise AI.