Find the Modernization Path That Protects What Matters

Summary

• AI can accelerate code modernization, but it creates durable value only when teams ground its output in application understanding, engineering discipline, and validation.
• Mission-critical COBOL and PL/I applications require more than code conversion. Leaders need to understand dependencies, business rules, transaction behavior, and operational controls before they choose a modernization path.
• The right modernization path depends on business objectives, architecture, risk tolerance, and operational priorities.
• Testing and validation help teams demonstrate that modernized applications still support expected business outcomes. 
   

AI has changed the code modernization conversation

Enterprise leaders no longer ask only whether artificial intelligence can explain code, summarize logic, or accelerate documentation. They ask a harder question: Can AI help modernize mission-critical applications without weakening the business behavior those applications protect?

And we believe that the publication of the Gartner^® Magic Quadrant^™ for AI-Augmented Code Modernization Tools reflects growing market focus on that question.

Our experience points in the same direction. Organizations increasingly recognize that business objectives should drive modernization decisions, not technology constraints. The challenge is not simply choosing a new platform or development model. It is determining how to evolve mission-critical COBOL and PL/I applications while preserving business behavior, architectural integrity, operational continuity, and transaction integrity.

That is why modernization leaders need more than AI-assisted code conversion. They need application understanding, disciplined path selection, deterministic validation, and confidence that essential business behavior will be preserved.

At Rocket Software, we view AI-augmented code modernization as an execution discipline. AI can reduce the time developers, architects, and application owners spend interpreting unfamiliar COBOL and PL/I applications. But AI creates durable value only when modernization leaders ground its output in code facts, structured application models, product documentation, and customer-specific context.

What does Rocket’s Gartner recognition signal for modernization leaders?

Rocket Software was recognized as a Challenger in the 2026 Gartner^® Magic Quadrant^™ for AI-Augmented Code Modernization Tools.  

We see that recognition as part of a larger shift: organizations are looking for practical ways to use AI in modernization without losing the application understanding, testing discipline, and validation that mission-critical environments require.

For organizations that need to modernize code while continuing to serve customers, employees, partners, and constituents, that distinction matters. In our view, Rocket’s position reflects a practical approach to mission-critical modernization: combining AI-assisted analysis with application understanding, automated testing, and validation so organizations can modernize with greater confidence.

The real modernization challenge is not whether organizations can generate new code. It’s whether they can change platforms, tools, and deployment models while preserving the business logic, architectural integrity, transaction behavior, recovery processes, and operational controls that keep core systems running.

Modernization leaders need more than AI-assisted code conversion. They need application understanding, disciplined path selection, deterministic validation, and confidence that business behavior will be preserved.

What should leaders expect from AI-augmented code modernization?

AI-augmented code modernization uses artificial intelligence alongside application analysis, engineering tools, testing, and human review to help teams understand, assess, transform, validate, and maintain existing applications.

Unlike basic code conversion, AI-augmented modernization connects source code to the dependencies, data flows, transactions, operational processes, testing evidence, and business outcomes that shape application behavior. 

What should leaders expect from AI-augmented code modernization?

Leaders should expect AI to reduce the time modernization engineers spend understanding applications, not remove the need for engineering judgment. The strongest use cases start with comprehension: plain-language code explanation, business rule extraction, dependency analysis, impact assessment, and documentation.

Those capabilities matter because mission-critical applications rarely reveal themselves through source code alone. A COBOL program can connect to batch schedules, transaction flows, file updates, database interactions, copybook references, job control, and operational procedures. AI output becomes more useful when deterministic analysis shows how those pieces connect.

That is why Rocket combines AI with static code analysis, structured application models, product documentation, and customer-specific context. The goal is not AI theater: output that looks impressive without producing reliable, verifiable outcomes. The goal is faster understanding, with traceability back to the programs, data elements, dependencies, and execution paths that shape application behavior.

The goal is not AI theater. The goal is faster understanding with traceability back to the application behavior modernization leaders need to preserve.

How should enterprises choose the right modernization path?

AI can accelerate analysis, but leaders still need to make a clear architectural decision. Application modernization can take many forms, including platform modernization, application replatforming, code refactoring, and language modernization. The right approach depends on business objectives, architecture, risk tolerance, and operational priorities. AI can help leaders evaluate options, but it should not blur the path.

A shared discipline connects every modernization strategy: application analysis, AI-assisted understanding, automated testing, and validation help ensure organizations change what needs to change without breaking the business behavior they depend on.

Rocket's code modernization portfolio helps organizations understand, modernize, test, validate, and evolve mission-critical COBOL and PL/I applications across mainframe, distributed, and cloud environments. Through solutions including Rocket Enterprise Suite and Rocket Visual COBOL, organizations can analyze dependencies, extract business logic, improve development workflows, automate testing and validation, and choose modernization strategies aligned to business objectives.

The common thread across those paths is discipline. Modernization leaders need to understand what the application does, where dependencies exist, which business rules matter most, and how they will prove that transformed code still supports expected outcomes.

Why does validation define modernization credibility?

Modernization credibility comes from evidence. Developers, testers, architects, and release leaders need test cases that reflect meaningful business scenarios, regression tests that compare outputs before and after change, and operational checks that confirm performance, recovery, and transaction behavior.

That validation work turns modernization from a promise into a controlled engineering process. AI can explain what exists, flag affected application areas, and document what modernization engineers need to review. Engineers still need to validate that the modernized application preserves expected behavior before production change.

Modernization credibility comes from evidence that the application still behaves as expected.

The value of this approach is reflected in real modernization outcomes. Organizations using Rocket code modernization solutions have achieved measurable results, including improved developer productivity, reduced testing cycles, lower transformation risk, significant operational cost savings, and greater confidence in modernization initiatives while maintaining business continuity.

How do modernization leaders make this stick?

The next phase of modernization will reward organizations that treat AI as part of a repeatable operating discipline, not a shortcut around application knowledge. Leaders should ask four practical questions before they commit to a modernization path:

• Which business behaviors must the application preserve?
• Which dependencies shape transaction outcomes, data movement, and operational timing?
• Are we changing the execution environment, the developer workflow, or both?
• How will we prove that the modernized application produces the expected results?

AI can accelerate answers to those questions, but it cannot make those questions optional. Modernization still depends on architecture, context, engineering discipline, and validation. For enterprises running mission-critical applications, that is the difference between moving code and moving the business forward safely, repeatedly, and with confidence.

Read the full Gartner report

Disclaimer

Source: Gartner, Magic Quadrant^™ for AI-Augmented Code Modernization Tools, by Prasanna Lakshmi Narasimha, Deacon D.K Wan, Erin Khoo, Wei Jin., August 3, 2026.

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