Accelerating Transformations and Driving Innovation: Exploring the Value AI Migration Tools Bring

Across industries, aging software systems built on outdated languages and architectures use significant resources, pose growing security risks and hamper innovation strategies. These legacy systems are a widespread problem, as McKinsey reports that as much as 70% of the software used by Fortune 500 companies was developed more than 20 years ago. The costs of maintaining legacy systems are high and, according to Forrester, capture two-thirds of global tech spending. Updating legacy technologies is no longer just a task for IT teams, but a strategic imperative for companies.

AI migration tools are a solution to the financial drain, questionable security, tech debt and inefficient operations that come with older technologies. Read on to learn why your company needs them.

From human-guided to AI-augmented migrations

Traditionally, legacy system modernization meant massive manual efforts: large teams poring over millions of lines of code, rewriting and refactoring by hand and painstakingly testing for regressions. Projects could take years and cost fortunes – a scale and pace at odds with today’s rapidly evolving business needs. This is where AI migration tools can help. Using artificial intelligence – including generative models and intelligent automation – AI migration tools analyze, refactor and rewrite codebases with a speed and scope unimaginable a few years ago.

For example, Microsoft recently introduced agentic AI features to assist with application upgrades. Their GitHub Copilot for app modernization can scan an entire codebase, detect outdated patterns or breaking changes, suggest safe upgrade paths and even automatically apply code fixes for things like dependency or security updates. In one case, Microsoft’s engineers used this AI-assisted tool to upgrade internal projects to .NET 8 in mere hours – a process that would normally have taken months.

It’s not just application code that benefits – databases and mainframe systems can too. Cloud providers like AWS have launched AI-powered migration agents that tackle some of the toughest legacy challenges. AWS’s Transform tool, for instance, uses a specialized AI agent to modernize COBOL-based mainframes. It breaks down monolithic code, converts it to modern languages (like Java) and orchestrates the entire refactoring process. The result is that multi-year mainframe modernization journeys can be done in months. This level of acceleration demonstrates the technical potential of AI migration tools in scaling and rewriting even the largest, most complex software systems.

Crucially, these AI tools don’t operate in a vacuum; they work under the guidance of human experts. A human engineer defines the target state (e.g. the desired architecture or language) and oversees the process, but the heavy lifting is handled by AI. Tedious tasks like converting thousands of code files, implementing repetitive syntax changes, or generating boilerplate code and unit tests can be done by AI in a fraction of the time. These tools speed up projects and reduce the risk of human error and oversight costs. They can catch subtle dependencies or inconsistencies that a manual approach might miss, thanks to their ability to analyze complex codebases far faster than a human. In short, AI migration tools are redefining how to modernize legacy software – from a time-consuming manual process to a faster, more precise collaboration between developers and their AI “teammates”.

AI as autonomous operators: the future of work

Microsoft’s Work Trend Index (WTI) lays out the new organizational blueprint: “AI-operated but human-led” systems. According to the WTI, companies will combine human judgment with AI agents to form hybrid teams that enable companies to scale rapidly, operate with agility and generate more value. AI migration tools are a perfect example. Human experts establish the goals and direction, while AI agents execute tasks.

Microsoft identifies three phases of AI integration:

Phase one – AI as a personal assistant

Phase two – AI as a team collaborator

Phase three – AI as an autonomous operator working under human oversight

An AI migration tool is essentially a specialized software development agent that independently refactors code or redesigns components once a human defines the objectives. The developer’s role shifts towards being an “agent boss” – a term the WTI uses for someone who manages one or more AI agents. In this scenario, developers and architects become a project lead over AI executors, guiding the tool by setting requirements, validating outputs and handling the exceptional cases that require human creativity or judgment.

This combination of humans and AI is exactly the “hybrid team” concept that forward-looking organizations are embracing as it fosters developer productivity. Instead of spending weeks working on rote code conversion, a human engineer can focus on the higher-level design decisions while delegating code translation to an AI agent. As a result, teams can pursue modernization efforts without the usual burnout and morale hit that come from repetitive, manual work.

Closing the capacity gap and boosting developer productivity

One of the driving forces behind the adoption of AI migration tools is the need to close the “capacity gap” in engineering. The WTI defines the capacity gap as “the deficit between business demands and the maximum capacity of humans alone to meet them”. This gap is evident in software development. On one hand, business leaders say productivity must increase – 53% of leaders feel their teams need to deliver more. On the other hand, 80% of workers report they simply don’t have enough time or energy to get all their work done. Developers need to deal with feature backlogs, bug fixes and legacy refactoring tasks, which often lead to long hours and fragmented focus. Human teams, no matter how talented, have a natural limit to the volume and speed of work they can handle.

AI migration tools address this problem by serving as force-multipliers for development teams. Microsoft research shows that leaders are recognizing this: 82% are confident they’ll leverage AI “digital labor” to expand their workforce capacity in the next 12–18 months. These AI tools are essentially digital employees – they can be “hired” on demand to take on a workload that would otherwise require additional human developers. By deploying an AI agent to handle the conversion of a million lines of legacy code, a company is effectively adding the equivalent of dozens of engineers’ worth of output, but at a fraction of the cost. In practical terms, this means AI migration tools can help a CTO meet ambitious transformation targets without saying “we need to hire more developers” or “we need to move deadlines.”

The productivity boost from these tools is immediate. By automating tasks, developers reclaim time and mental energy to focus on higher-value work like designing new features, improving architecture, or testing of edge cases. In essence, every hour an AI agent works on code migration is an hour given back to a human developer to apply creativity and critical thinking elsewhere. This is how AI tools elevate productivity – not by replacing developers, but by unburdening them of menial work.

There’s also a skills dimension to closing the capacity gap. Many legacy systems run on technologies that fewer people specialize in (e.g. COBOL, older .NET, mainframe assembler). Organizations are facing a talent crunch, as experts who understand old systems are retiring, and younger engineers aren’t versed in older tools. AI migration platforms can encode the expertise needed to handle these old technologies, thereby preserving institutional knowledge and applying it at scale. The result? Organizations aren’t hindered by a lack of human skills and can accelerate modernization timelines without waiting to assemble a perfect (and pricey) specialist team.

Ultimately, AI is about meeting business demands faster and with greater agility. The Work Trend Index notes that early adopter companies already report they’re able to take on more work – 55% say they can handle additional tasks versus only 25% of other firms. By boosting developer productivity through AI migration tools, companies not only get backlogs under control, they also create room for innovation. Instead of dealing with maintenance, teams can invest time in strategic initiatives that drive value.

Investment, momentum and the inevitability of AI-driven migration

The convergence of strategic need and technological maturity has made AI-powered migration not just an option, but an inevitability for future-ready software strategies. Just look at where capital and resources are going. The market for legacy system modernization solutions (in which AI is playing a growing role) is booming – 2025’s market size of $24.98 billion is projected to reach $56.87 billion by 2030. Organizations are investing in tools that can rejuvenate aging software assets. This isn’t hype – it’s a response to real pain points and a real opportunities.

This has profound competitive implications: companies that modernize faster can out-iterate and out-innovate those stuck on legacy platforms. Recognizing this, startups are emerging with AI-native development tools that go beyond basic code generation – focusing on automated code migration, continuous end-to-end testing and collaborative human-agent workflows.

Many organizations have gotten a taste of what AI can do in various domains (from customer support bots to AI-enhanced analytics) and are now looking to apply it to the problem of legacy code. There is a growing acceptance that AI-powered migration will soon be a standard part of the software lifecycle – a routine step whenever you need to port an application to a new platform or language, much like automated testing is a standard practice today.

The inevitability of AI migration is also tied to the broader workplace shift to AI integration that Microsoft’s WTI highlights. That report proclaims 2025 as the year the “Frontier Firm” is born – when companies stop merely experimenting with AI and begin rebuilding their businesses around it. In practical terms, rebuilding around AI means rearchitecting processes and systems with AI in the loop from the start. Legacy modernization is a prime candidate for such rearchitecting. It’s both a critical enabler (you can’t fully benefit from AI and cloud if your core systems are stuck in the past) and a proving ground for AI’s capabilities (if AI can successfully convert your 20-year-old system to a new stack, you gain enormous confidence in what else it can do). The organizations that treat AI migration as a strategic program, not a one-off experiment will be ahead of the curve. Conversely, those that delay are likely to face escalating costs, growing risks, and a widening competitive gap as each year their legacy systems fall further behind and their more innovative competitors leap ahead.

Embracing AI-augmented development

The trend is clear: AI migration tools are poised to become a key component of enterprise software strategy. They address challenges like aging systems, capacity constraints, talent shortages – with capabilities that are rapidly evolving. Embracing AI for legacy code modernization is not just about fixing old code; it’s about reimagining how your organization builds and improves software going forward. It’s an opportunity to turn legacy burdens into a competitive advantage, by modernizing faster and more safely than competitors who rely on traditional methods.

Organizations that take the lead will find themselves more agile, more scalable and better able to capitalize on new opportunities because their core systems won’t be holding them back. They’ll also cultivate workforces adept at leveraging AI, which creates a cycle of productivity and innovation. Microsoft reports that 81% of leaders expect AI agents to be extensively integrated into their company’s strategy in the next 12–18 months – the age of “human-led, agent-operated” business is arriving faster than many anticipated.

For CTOs and enterprise decision-makers, the time to act is now – to pilot AI migration tools, to train teams on new workflows and to allocate budgets to modernization efforts that will pay off in agility and cost savings. As with any emerging technology, there is a learning curve, but the cost of waiting could be far greater. Each passing quarter is time spent under the weight of legacy systems and another quarter your competitors might leap ahead with faster release cycles or better customer experiences enabled by modern platforms. Alternatively, early adopters of AI-assisted migration can modernize in months what might have otherwise taken years – with higher quality and lower risk. AI migration tools and techniques are no longer experimental; they are ready for mainstream use and are rapidly becoming part and parcel of software engineering.

AI migration technology represents the best of both worlds: it elevates human developers by freeing them up, and it breathes new life into legacy assets by rejuvenating them for the modern era.

At Software Mind, we are active participants in this transformation. We’ve seen how an AI agent, under skilled human leadership, can turn a difficult legacy problem into a solvable task. If you’d like to learn more about how AI-augmented migration services can support your business, get in touch with our experts by filling out this form.

FAQ

How big a problem are legacy systems?

Legacy systems are a significant problem across industries. McKinsey reports that as much as 70% of the software used by Fortune 500 companies was developed more than 20 years ago. The costs of maintaining legacy systems are high and, according to Forrester, capture two-thirds of global tech spending. Ageing software systems built on outdated languages and architectures use significant resources, pose growing security risks and hamper innovation strategies.

What are AI migrators?

AI migrators use artificial intelligence, including generative models and intelligent automation, to analyze, refactor and rewrite codebases with precision and at speed. They automate and accelerate data migration between systems.

What benefits do AI migrators bring to companies?

Tedious tasks like converting thousands of code files, implementing repetitive syntax changes, or generating boilerplate code and unit tests can be done by AI in a fraction of the time. AI migrators speed up projects and reduce the risk of human error and oversight costs. They can catch subtle dependencies or inconsistencies that a manual approach might miss, thanks to their ability to analyze complex codebases far faster than a human. In short, AI migration tools are redefining how to modernize legacy software – from a time-consuming manual process to a faster, more precise collaboration between developers and their AI “teammates.”

Are companies prioritizing investment in AI migration tools?

Yes. The market for legacy system modernization solutions (in which AI is playing a growing role) is booming – 2025’s market size of $24.98 billion is projected to reach $56.87 billion by 2030. Organizations are investing in tools that can rejuvenate aging software assets.

What are some noteworthy examples of AI modernization tools?

Microsoft recently introduced agentic AI features to assist with application upgrades. Their GitHub Copilot for app modernization can scan an entire codebase, detect outdated patterns or breaking changes, suggest safe upgrade paths and even automatically apply code fixes for things like dependency or security updates. In one case, Microsoft’s engineers used this AI-assisted tool to upgrade internal projects to .NET 8 in mere hours – a process that would normally have taken months.

It’s not just application code that benefits – databases and mainframe systems can too. Cloud providers like AWS have launched AI-powered migration agents that tackle some of the toughest legacy challenges. AWS’s Transform tool, for instance, uses a specialized AI agent to modernize COBOL-based mainframes. It breaks down monolithic code, converts it to modern languages (like Java) and orchestrates the entire refactoring process.