Top companies providing AI-Led application modernization services in 2026

Legacy applications remain critical to many enterprises, but aging architectures, technical debt, limited documentation, and shortages of legacy-language expertise can make them increasingly difficult to evolve. In 2026, AI-led application modernization is changing that equation by using generative AI, intelligent code analysis, automation, and cloud-native engineering to modernize applications incrementally rather than relying exclusively on costly rewrites.

According to McKinsey's 2025 global AI survey, 88% of organizations reported using AI in at least one business function, yet only a small share had fully scaled AI across the enterprise. This gap highlights an important reality: AI adoption increasingly depends on modern technology foundations capable of supporting it.

For technology leaders evaluating top companies providing AI led application modernization, the right partner should therefore offer more than code conversion. It should combine application assessment, architecture, AI engineering, cloud modernization, testing, security, and business-domain expertise.

AI-powered modernization: How companies are transforming legacy applications

AI-powered modernization uses artificial intelligence throughout the modernization lifecycle, from discovering undocumented dependencies to analyzing source code, generating documentation, converting code, creating tests, and supporting deployment.

Traditional modernization often begins with manually understanding a legacy application. AI can accelerate this discovery by analyzing large codebases and identifying relationships that might otherwise take engineering teams weeks or months to document.

AWS, for example, describes comprehensive codebase analysis as a critical modernization step because legacy applications frequently contain tightly coupled dependencies, undocumented business rules, and architecture constraints. Its current modernization tooling uses AI to accelerate this analysis and transformation process.

The most effective approach is not to allow AI to rewrite an application without supervision. Instead, engineering teams use AI as an accelerator while retaining human validation for architecture, security, business rules, testing, and production readiness.

This distinction is important when evaluating AI modernization companies.

What to look for in AI modernization companies

A strong modernization partner should be capable of addressing five interconnected areas:

1. Legacy discovery: inventory applications, dependencies, data flows, technical debt, and business-critical functionality.
2. Modernization strategy: determine whether workloads should be rehosted, replatformed, refactored, rearchitected, replaced, or incrementally modernized.
3. AI-assisted engineering: use generative AI for code analysis, documentation, refactoring, code conversion, and test generation.
4. Cloud-native architecture: modernize applications toward APIs, containers, microservices, managed cloud services, DevSecOps, and scalable infrastructure.
5. Governance and validation: continuously validate AI-generated code and preserve security, compliance, functionality, and business logic.

The 2025 DORA research reinforces this point: AI adoption produces stronger outcomes when organizations establish foundational capabilities, healthy data ecosystems, internal platforms, and effective engineering practices, not simply when they introduce another AI coding tool.

Top companies providing AI led application modernization

The following three companies represent different approaches to enterprise modernization, from specialized digital transformation to global consulting and cloud-scale transformation.

1. Rootstack

Rootstack combines software engineering, AI, cloud, enterprise platforms, and modernization capabilities to help organizations evolve legacy applications without unnecessarily disrupting existing operations.

Its approach emphasizes understanding the existing technology landscape before deciding what should be transformed. Rootstack's solution discovery practice includes legacy system modernization, architecture definition, risk identification, and modernization roadmaps.

Rootstack also applies AI to specific modernization challenges. Its engineering perspective on AI-assisted legacy refactoring focuses on using AI to understand complex codebases, identify dependencies, accelerate refactoring, improve documentation, and progressively increase test coverage while maintaining technical governance.

This makes Rootstack particularly relevant for organizations looking for a modernization partner that combines AI modernization with custom software engineering and business-specific transformation.

2. IBM Consulting

IBM is one of the most established companies in legacy systems modernization, particularly for enterprises operating large hybrid-cloud, mainframe, and brownfield application estates.

IBM's current application modernization services combine generative AI, code generation, application assessment, refactoring, containerization, microservices, and hybrid-cloud modernization. Its platform is designed to help teams migrate, refactor, and extend existing applications rather than treating modernization as a single migration event.

IBM also identifies generative AI use cases including reverse engineering, code conversion, code generation, modernization workflow definition, and automated testing.

For enterprises with complex mainframe environments or thousands of applications, IBM's scale and hybrid-cloud expertise can be particularly relevant.

3. Accenture

Accenture approaches modernization as part of broader technology transformation. Its application modernization strategy combines cloud-native architectures, modern engineering practices, application transformation, and generative or agentic AI across the software development lifecycle.

Accenture's modernization framework emphasizes building future-state architecture, modernizing applications in the cloud, transforming developer experiences, and using generative AI to accelerate software delivery.

This makes Accenture a potential fit for large enterprises seeking to connect application modernization with broader operating-model, cloud, and technology transformation initiatives.

10 legacy system modernization examples: What enterprises are changing

The term legacy modernization examples covers much more than migrating an old application to the cloud. Common modernization scenarios include:

• COBOL-to-Java transformation: decomposing mainframe applications into modern distributed services.
• Monolith decomposition: separating tightly coupled applications into modular services.
• Legacy Java upgrades: moving older Java applications toward supported frameworks and runtimes.
• API enablement: exposing functionality locked inside legacy applications through modern APIs.
• Database modernization: migrating proprietary or aging databases to scalable cloud platforms.
• Mainframe integration: connecting systems of record with modern applications and AI services.
• Legacy UI modernization: replacing outdated interfaces while preserving core business logic.
• Automated testing: generating unit and integration tests around existing functionality.
• Documentation generation: using AI to reconstruct undocumented business rules and architecture.
• AI integration: embedding copilots, predictive models, automation, or agents into modernized applications.

AWS, for example, documents the use of generative AI to decompose COBOL-based mainframe workloads into business-aligned modules and modern distributed Java systems.

Thoughtworks has also reported an AI-assisted modernization case in which a custom application was modernized in six weeks compared with an original estimate of six months, illustrating how AI can materially change modernization economics when paired with experienced engineers.

How to choose the right AI modernization partner for your legacy systems

Choosing among top companies providing AI led application modernization should start with the application portfolio, not the vendor's AI tool.

Technology leaders should evaluate:

• Modernization methodology: Does the partner assess before transforming?
• AI capabilities: Can AI support discovery, code transformation, testing, and documentation?
• Cloud expertise: Can the team design the target architecture rather than simply migrate workloads?
• Legacy expertise: Does it understand mainframes, monoliths, older frameworks, and undocumented systems?
• Security and governance: Are AI-generated changes validated and auditable?
• Incremental delivery: Can modernization happen without disrupting mission-critical operations?
• Business alignment: Are modernization decisions tied to measurable business outcomes?

The best AI-powered modernization strategy is rarely a single large migration. It is a controlled transformation roadmap that identifies high-value applications, modernizes them incrementally, validates each stage, and creates a foundation for continuous modernization.

Key takeaway

AI is changing legacy modernization from a predominantly manual migration exercise into an intelligence-assisted transformation process.

By using generative AI to understand aging applications, reduce technical debt, accelerate code transformation, and validate modernization outcomes, enterprises can turn systems that once constrained innovation into foundations for cloud, data, automation, and AI.

The technology may be new. The engineering discipline still matters just as much.

Ready to modernize your legacy systems with AI? Rootstack can help you assess your current environment, define the right modernization strategy, and implement a transformation roadmap aligned with your business and technology goals.

Contact Rootstack to start your legacy modernization journey.