Agentic AI can speed application modernization by coordinating bounded work such as code analysis, dependency mapping, transformation planning, refactoring, and testing. It does not choose the right business outcome for you or make generated changes safe by default. The practical model is to automate repeatable analysis and implementation tasks while people remain accountable for strategy, architecture, security, acceptance criteria, and production releases.
What application modernization means—and what it does not
Modernization is not another word for rewriting. It means updating legacy applications to meet current business and technical needs, using approaches that can range from limited changes to replacement or rebuilding. IBM lists rehosting, refactoring, replatforming, rearchitecting, replacement, and incremental enhancement among the available strategies.
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The right choice depends on an application’s business value, criticality, objectives, and the investment and risk the organization can accept. A valuable, hard-to-change system may warrant deeper work; a stable application with little strategic differentiation may not. “Move everything to a new architecture” is not a strategy unless the expected benefits justify the cost and disruption.
| Modernization path | What it generally changes | Question to resolve first |
|---|---|---|
| Rehosting | Moves an application to a different hosting environment with limited changes to the application itself. | Will changing where it runs deliver enough value if the application remains largely as it is? |
| Replatforming | Moves the application while making selected changes to its platform or runtime. | Are the target platform’s benefits worth the compatibility and migration work? |
| Refactoring | Changes parts of the code or its structure without necessarily replacing the application. | Which specific constraints or maintenance costs must the changes address? |
| Rearchitecting | Changes the application’s architecture to support different technical or operational goals. | Do those goals warrant the broader design, testing, and operating-model changes? |
| Replacement or rebuilding | Replaces the existing application or creates a new implementation. | Can the organization preserve required business behavior and data while retiring the old system? |
| Incremental enhancement | Improves selected capabilities in smaller steps rather than undertaking a broad transformation. | Can targeted changes meet the need, and how will dependencies on the remaining legacy parts be managed? |
These paths are not interchangeable, and a program may use different ones for different applications. Make the decision at the application or workload level rather than assuming one estate-wide treatment is economical.
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Where agentic AI can help
AWS describes AWS Transform as a collaborative enterprise transformation workbench using specialized AI agents and agentic workflows. Its documentation identifies work that includes assessment, code analysis, dependency mapping, transformation planning, refactoring, and testing, as well as migration and technical-debt reduction. IBM describes a staged approach in which people lead strategic setup and integration, automated work supports migration, testing, and deployment, and teams continue optimization.
Together, these vendor descriptions suggest a useful division of labor: let AI help inspect and carry out bounded, reviewable tasks; keep people responsible for deciding why an application is changing, what it must continue to do, and whether a proposed change is acceptable. That is a practical governance approach, not a guarantee that any product can modernize an application safely from end to end.
Discovery and assessment
Use AI-assisted analysis to help build an inventory of applications, inspect code, and identify dependencies that can affect a move or change. The useful output is not merely a code summary; it is a clearer view of what depends on the application, what the proposed work must preserve, and where uncertainty remains. Confirm important findings with system owners and other evidence before using them to set scope.
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Once the target outcome and constraints are clear, AI assistance can help organize a transformation plan and produce or modify code. Keep tasks narrow enough to review: specify the intended change, the files or components in scope, constraints, and the evidence required for acceptance. Treat generated changes as proposals until they pass review and tests.
Testing and deployment
AI-supported testing can help teams examine changed behavior and work through a migration, but a test result is only as useful as the coverage and acceptance criteria behind it. Define expected behavior before transformation, include critical integrations and data paths, and set explicit release and rollback conditions. A passing automated test suite cannot establish that untested business rules remain correct.
How to choose between rehosting, refactoring, or rebuilding
Start with the outcome the organization needs, not with a preferred tool or a fashionable architecture. For each application, assess the business case alongside technical and operational constraints.
- Business value and criticality: What business capability does the application support, and what is the consequence of interruption or incorrect behavior?
- Target outcome: Is the primary goal to change hosting, reduce maintenance burden, improve performance, support new capabilities, or retire the system?
- Dependencies and complexity: What other applications, services, data stores, or processes must continue to work with it?
- Workload and language support: Can the proposed platform and its AI-assisted capabilities handle the actual technologies and workload?
- Risk, security, and compliance: What controls apply to code, data, access, generated changes, deployment, and auditability?
- Testability: Can the team establish expected behavior and test the important paths before changing the application?
- Total cost and operating model: What will the path cost to implement and operate, and does the team have the skills and review capacity to sustain it?
Compare the full lifecycle cost and risk of plausible paths, including the work needed to integrate, test, secure, operate, and eventually change the result. A low-effort move can leave the original constraints intact; a broad rewrite can create a large validation burden. Neither is automatically the economical option.
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A practical workflow for an AI-assisted modernization program
- Set the business objective and human ownership. Name the accountable application and business owners, define the intended outcome, and agree who approves architecture, security exceptions, test acceptance, and releases.
- Build and verify the baseline. Inventory the application, its dependencies, key behaviors, data flows, and operational constraints. Record unknowns instead of treating automated analysis as confirmed fact.
- Select the modernization path. Compare feasible options against value, criticality, cost, risk, complexity, performance needs, workload support, compliance obligations, and the team’s operating model.
- Define acceptance before asking an agent to change code. Write down required behavior, security checks, test coverage, review expectations, and release conditions. These criteria are the basis for judging proposed work.
- Run bounded tasks and inspect the output. Use agents for scoped analysis or transformations. Review code and dependency findings, investigate exceptions, and keep a record of decisions and changes.
- Test against the baseline and business requirements. Validate critical behavior, integrations, data handling, performance needs, security controls, and failure cases relevant to the application.
- Release with a recovery plan. Require the designated human approvals, use the organization’s controlled deployment process, and know how to roll back or otherwise recover if production behavior is unacceptable.
- Measure the result and adjust. Compare actual cost, delivery time, defects, operational effort, and business outcomes with the baseline. Use what the team learns to update later waves rather than assuming every application will benefit equally.
What the published numbers do—and do not—show
Modernization can address substantial accumulated maintenance work, but headline figures need their source and scope. In a February 24, 2026 article, IBM reported CAST’s estimate of 61 billion days of repair time worldwide in 2025. This is IBM’s account of a CAST estimate, not an independently verified measurement in the cited article; it describes a broad technical-debt problem, not the savings available to a particular modernization program.
AWS has also published vendor-reported outcomes. Its customer example says Experian modernized seven legacy .NET applications with a 40% reduction in developer effort and approximately 300 engineering days saved. Those are results AWS reports for that customer example, not a forecast for other organizations. AWS documentation has separately stated that AWS Transform workloads saved 1.69 million hours in the service’s first year and processed more than 4.5 billion lines of code. Those are AWS-reported cumulative figures, not an independent benchmark or a measure of the benefit a new customer should expect.
Microsoft’s June 2, 2026 Azure blog reported Forrester survey findings that 94% of IT leaders ranked modernization as a top AI-strategy priority, 43% of application portfolios were modernized on average, and 32% were AI-ready. Another Microsoft Azure blog reports that 91% of IT leaders see modernization as necessary for business AI advancements, also attributing the finding to Forrester’s Q1 2026 Cloud and AI Application Modernization Survey. These are Microsoft’s reports of survey findings; without the primary survey report and question wording, the figures should not be combined or treated as independently inspected results.
These examples show why attribution matters: vendor case studies, product documentation, and surveys can illustrate reported capabilities or priorities, but they do not establish a universal time saving, cost reduction, or success rate. The available vendor descriptions are not independent head-to-head tests of modernization platforms.
How to evaluate an agentic modernization platform
Assess a product against a representative workload and the controls your organization actually needs. Ask providers to demonstrate current feature support for your languages, frameworks, deployment environments, and operating constraints; features and availability can change.
- Scope and outcome: Does the offering address migration alone, deeper application transformation, or both?
- Discovery quality: Can it help map dependencies and surface uncertainty in a way teams can verify?
- Change review: Can reviewers understand what was generated or modified, why, and how to inspect it?
- Test evidence: What test creation or execution is supported, and how will you establish equivalence with required application behavior?
- Security and compliance: How are code and data handled, what access controls and audit evidence exist, and how do they fit your obligations?
- Release and recovery: How does the workflow integrate with existing deployment approvals, monitoring, and rollback processes?
- Integration and skills: Does it work with the team’s toolchain, and what expertise and human review are still required?
- Total cost: What are the implementation, service, integration, validation, and ongoing operating costs?
A short, representative pilot can reveal whether the workflow fits the estate, but it should test review effort and verification as well as the volume of code an agent can process. The best fit depends on the workloads, controls, and teams involved; the vendor material cited here does not establish a universal provider ranking.
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