Legacy System Migration Trends 2026-2027: How AI Changes the Process

Our company has been engaged in legacy software migration for over fifteen years. During that time, I have witnessed several technological revolutions, each of which was, at one point, seen as an event capable of radically changing software development.

When I talk to veterans of the IT industry who built their companies 25–30 years ago, they often tell me, “Believe me, this isn’t the first revolution of its kind.” And indeed, almost every technological leap has been accompanied by the same expectations: development will become faster, automation will eliminate most of the manual work, and fewer programmers will be needed.

In practice, most of these revolutions didn’t actually lead to less work. The tools and the requirements for specialists changed, but at the same time, the amount of software that companies wanted to create kept growing.

Today, we are witnessing yet another such revolution—AI. But when it comes to legacy software migration, it is already beginning to change not only the technologies to which we are migrating applications, but also the migration process itself.

We Have Seen Technology Revolutions Before

When developers began moving away from assembly language toward higher-level languages, it seemed that implementing the same functionality would now require much less code and fewer people.

Then came object-oriented programming. Once again, there was an expectation that code reuse and more convenient abstractions would dramatically speed up development.

The next major change was the advent of graphical user interfaces, Windows, and macOS. Then came the mass migration to the web, and many people thought that desktop applications would all but disappear. After the advent of smartphones, a similar idea emerged—this time regarding web and desktop software.

There was also a “blockchain era” when, for a while, it seemed as though almost any software product had to use blockchain. Today, many of those predictions seem much more subdued.

AI stands out for the scale of its potential impact, but the pattern itself is familiar to me. A new tool is initially seen as a way to replace an existing process. Then the market realizes that it simply allows us to do much more.

In my opinion, this is exactly what is happening right now with software migration services.

AI Is Becoming Part of the Migration Process

In the past, a typical legacy application migration project was fairly straightforward. There is an existing application. We analyze its architecture, source code, database, dependencies, and business logic. After that, we select the target technology and begin the migration.

In various projects, we’ve used internal frameworks, our own tools, specialized third-party tools, or manual work. But the model itself remained the same: we migrated software from one technical environment to another.

The very approach to legacy software migration is changing right now.

AI can be involved in virtually every stage: initial assessment, code analysis, documentation, code review, task preparation, business analysis, working with repositories, and project management systems. It can help rewrite specific sections of code, create tests, work with APIs, and automate routine technical tasks.

To me, the main takeaway for 2026–2027 is already quite clear: there’s no escaping AI in software development and migration.

These days, we don’t build our own IDE before starting a project. We use Visual Studio, RAD Studio, IntelliJ IDEA, or other off-the-shelf tools. Most likely, the same will be true for LLMs: most software companies won’t build their own foundation models, but will use existing models and integrate them into their processes.

Therefore, the question is no longer whether or not to use AI. The question is this: where does it offer an advantage, what tasks can be delegated to it, and where should responsibility remain with humans?

AI Can Already Assist Across the Migration Lifecycle

If you look at the entire legacy system migration process, there are tasks at virtually every stage that AI can accelerate.

During the assessment phase, it can help analyze the codebase, identify dependencies, gather information about the project’s structure, and prepare documentation. This is especially useful for systems that have been in development for 10, 20, or 30 years.

During development, AI performs well on clearly defined technical tasks: working with APIs, writing standard code, data transformation, testing, documentation, specific calculations, and integrations.

AI can also be connected to a repository and used for code reviews or integrated with a project management system. We have already encountered projects where, based on the nature of the review comments, we got the impression that part of the code review on the client’s side was being performed by an AI agent.

We don’t know for sure, but the situation itself already seems quite realistic. In time, a developer may not always know who reviewed their pull request first—a person or an AI agent.

AI Is Strong at Technical Tasks, but Architecture Is Different

It is important not to confuse the automation of technical tasks with responsibility for the system.

Today, AI works well in situations where the input data is clear and the expected outcome is fairly well-defined. Whether it’s writing a specific piece of text, implementing API integration, performing a calculation, generating a standard piece of code, or identifying a potential technical issue—modern models are already truly useful for these tasks.

The situation is more complicated when it comes to architecture.

You can ask an LLM to analyze an existing architecture, suggest options for software modernization, or critically evaluate a proposed architecture. But I don’t yet see a situation where an architectural decision could be entirely delegated to a model.

Every project still needs a specialist who understands the system, makes decisions, and takes responsibility for them. Furthermore, this person must be able to explain the architecture to developers, the client, product owners, and the people who will maintain the system in the future.

Today, AI serves to enhance the architect’s work rather than replace him or her.

One-Click Legacy Software Migration Is Not Here Yet

There is also a more radical view of AI migration.

Take an old Delphi or C++ project, upload all the source code to Claude, ChatGPT, or another LLM, click a button, and in a few hours you’ll get a new project in .NET, Java, or Python with the business logic preserved, the database migrated, working integrations, and a UI.

As of now, I don’t see any such reliable process.

We have tried similar approaches, continue to test them, and will continue to do so. For a company whose areas of focus include legacy migration solutions, there really is a certain conflict of interest here. If a technology emerges that can automatically perform a significant portion of our current work, it will impact our business.

But that is exactly why we need to keep an eye on it before our customers and competitors do.

For now, the reality is different: AI can speed up certain stages of migration and automate some of the work, but there is still a long way to go before we can achieve a fully autonomous migration of a large legacy application.

Programming Language and UI Still Matter

The quality of automation depends heavily on the technology.

For popular languages that have a wealth of up-to-date documentation and examples, the results are usually better. Python, Java, and C# are obvious examples.

When we move on to more specialized or legacy technologies, the task becomes more challenging. This is particularly evident in Delphi, Object Pascal, and certain C++ systems.

The situation becomes even more complicated if the code is closely tied to the UI.

Rewriting an isolated business function from one language to another is one thing. Migrating a large desktop application—where business logic has been intertwined for years with forms, visual components, events, and framework-specific features—is quite another.

Therefore, AI migration should not be evaluated solely based on how well a model writes code in a particular language. In real-world legacy software modernization, it is important to understand the system as a whole.

Specialized Migration Tools Will Face New Competition

Another trend for 2026–2027 is the pressure LLMs are putting on the market for specialized tools.

For many years, there were separate products for code analysis, database analysis, dependency analysis, data migration, and various types of conversion.

I don’t think they’ll disappear tomorrow. But now they’ll have to compete with general-purpose LLMs, which are already capable of performing some of these tasks.

If a specialized product provides more accurate results, guarantees data isolation, and is reasonably priced, it still makes sense to use it. If, however, the team can solve the same problem using an LLM they’re already using, the value of an additional tool becomes less obvious.

For enterprise customers, this also raises a cost issue. Cloud LLMs come at a price, but a powerful on-premises LLM infrastructure is far from free either. Sometimes a specialized tool is cheaper, sometimes a cloud model is, and sometimes security requirements will necessitate a private deployment.

This is already becoming part of the economics of every specific software modernization project.

AI Governance and Intellectual Property Cannot Be Ignored

One of the factors that will slow down the adoption of AI in enterprise migration is the protection of intellectual property.

We are already facing a paradoxical situation: the client wants the project to be completed faster and more cheaply, but at the same time asks us not to use AI because they are concerned about the source code, data, and intellectual property.

It’s easy to understand. For many of our clients, a software product is the result of 20 or 30 years of work. It embodies accumulated business logic, domain knowledge, and competitive advantages. No one wants to transfer such a codebase to an external system without a clear understanding of where that data will end up and how it will be used.

That is why AI governance is gradually becoming part of migration planning. It is important to understand which models are acceptable, what data can be provided to them, and where that data is processed.

This is a big topic in its own right, so I won’t go into detail about it here. But we can no longer ignore it.

Smaller Teams Will Be Able to Deliver More

Another trend I expect to see is a reduction in the size of software development teams.

The era of projects in which a large consulting or outsourcing firm could assemble a team of 150–200 people to carry out a large volume of relatively routine work is likely coming to a gradual end.

I’m not saying that large engineering organizations will disappear. But it will take fewer people to complete the same amount of work.

At the same time, the expectations for them will be higher. A specialist must not only have a strong grasp of their technical field but also understand how to use AI, how to verify its results, and how to integrate it into the production process.

A small, high-performing AI-assisted team could potentially handle work that previously required a much larger team.

But that doesn’t mean the IT market will automatically shrink.

The AI Arms Race May Increase Delivery Instead of Reducing It

I call it the AI arms race.

Let’s say a company used to need a team of 50 people, but after implementing AI, the same amount of work can be done by 10. The obvious conclusion is to downsize the team.

But there is another possible scenario.

If your competitor can now produce the same product with ten specialists, what’s stopping you from using twenty people and delivering twice as much functionality?

If the end customer’s purchasing power does not decrease significantly, competition does not disappear. On the contrary, companies have an incentive to use productivity gains to increase delivery.

In that case, the number of deliverables may grow much faster than the number of people needed to create them.

We’ve seen a similar effect before. When software became cheaper and easier to develop, people didn’t start creating less software. They started creating more of it.

I think it’s quite likely that the same thing will happen with AI.

What Legacy Software Migration Will Look Like in 2026-2027

My main prediction for legacy software migration in 2026–2027 is simple: AI will cease to be an experimental add-on and become a standard part of the migration process.

It will be used in assessment, development, code review, testing, documentation, business analysis, and project management. Some specialized tools will come under pressure. Teams may become smaller, but each specialist will need to deliver much higher productivity.

However, humans are not yet being phased out of the process. On the contrary, the more code and solutions AI generates, the more important it is to understand who is responsible for the architecture, who verifies the results, and who is accountable to the client for a functioning product.

For us, this is no longer just theory. We use AI in legacy software migration and are gradually expanding the number of stages where it helps the team.

In Part 1, I wanted to focus specifically on the shift in approach to migration. In Legacy Software Migration Trends 2026–2027 — Part 2, I want to move on to a more practical question: which technologies should we migrate from and to today.

For legacy projects, this issue is particularly important because the decision isn’t just for a year or two. A company needs to understand where it is investing its development budget today and to what extent the chosen technology stack will remain relevant in 3–5 years—and sometimes even in 5–10 years.