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Agentic AI can help modernization teams move faster by coordinating bounded tasks such as application discovery, dependency analysis, code transformation and testing. It does not choose the right modernization strategy or make the resulting system safe by itself: people still need to set business goals, approve architecture and security decisions, validate changes and control production releases.
Modernization is a set of choices, not a synonym for rewriting
Application modernization means updating existing applications with newer technologies and practices, including cloud-native approaches, DevOps and infrastructure as code. The right path depends on what the application is worth to the business, how critical it is, what outcome the organization needs, and how much investment and risk it can accept.
| Approach | What changes | When it may fit |
|---|---|---|
| Rehosting | Move an application to a new infrastructure environment with limited changes to its design. | When the immediate goal is to move the workload while deferring deeper application changes. |
| Replatforming | Move the application while making selected changes to its platform or operating environment. | When the target platform offers a practical improvement without requiring a full redesign. |
| Refactoring | Change code structure or implementation while retaining the application’s core purpose. | When targeted improvements can make the system easier to maintain, operate or extend. |
| Rearchitecting | Change the application’s architecture to meet new requirements or take advantage of a different operating model. | When the current design is a barrier to important business or technical goals. |
| Replacement | Retire the existing application and use a different product or system. | When an alternative better meets the need than continued investment in the current application. |
| Incremental enhancement | Improve the application in smaller, staged changes rather than undertaking a single large transformation. | When change needs to be phased, or when preserving service continuity is a priority. |
| Rebuilding | Create a substantially new implementation, potentially replacing much of the existing application. | When the desired outcome cannot reasonably be reached by smaller changes and the organization can manage the cost and risk. |
IBM describes these as options rather than a universal modernization sequence. A rewrite is not automatically more modern, faster or less expensive: compare the expected business outcome with the cost, disruption and risk of each path.
Where agentic AI can help in a modernization program
A useful way to think about agentic AI is as assistance that can analyze information and carry out defined tasks within a workflow. AWS describes AWS Transform as a collaborative enterprise transformation workbench using specialized AI agents and agentic workflows. Its listed activities include assessment, code analysis, dependency mapping, transformation planning, refactoring and testing. These are vendor-described capabilities, not a guarantee that a service will autonomously modernize any application end to end.
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| Work area | Where agents can assist | Human checkpoint |
|---|---|---|
| Discovery and assessment | Collect and summarize application information, identify code patterns and help assess modernization candidates. | Confirm that inventories, business context and criticality ratings are accurate. |
| Dependency analysis | Map relationships among application components and surface dependencies for review. | Check whether the map reflects real data flows, operational dependencies and external services. |
| Planning | Help organize findings into proposed work, transformation steps and test activities. | Choose the modernization objective, target architecture, sequencing and acceptable risk. |
| Code transformation | Assist with bounded code changes or refactoring tasks, subject to the chosen platform and supported workload. | Review generated changes for correctness, maintainability, security and architectural fit. |
| Testing | Help create or run tests and identify differences that need investigation. | Set acceptance criteria and decide whether test coverage and results are sufficient. |
| Deployment and operations | Support repeatable migration, testing or deployment steps where the workflow and tooling permit. | Approve production releases, rollback plans and ongoing operational controls. |
IBM’s described operating approach similarly separates human-led strategic setup and integration from automated migration, testing and deployment, followed by continued optimization. In practice, automate repeatable analysis and transformation steps; keep people accountable for business intent, exceptions, architecture, security, test acceptance and release decisions.
How to make modernization faster without creating avoidable risk
- Set the outcome before selecting tools. State what must improve: for example, a business capability, maintainability, operating model or ability to support a required workload. Identify non-negotiable availability, security and compliance requirements.
- Build and validate an application picture. Use discovery and code analysis to assemble inventories and dependency maps, then have application and operations owners check the findings. An incomplete map can make an apparently small change risky.
- Choose the modernization path per application. Compare rehosting, refactoring, replatforming, rearchitecting, replacement or incremental enhancement against business value, criticality, objectives, investment and risk. A portfolio does not need one strategy for every application.
- Break work into bounded transformations. Define the change, affected components, expected behavior and acceptance tests before asking an agent or team to perform it. Keep generated changes reviewable and traceable to the intended task.
- Test behavior, not just whether code builds. Use tests that reflect important business behavior, integrations and security requirements. Review differences between old and new behavior, and do not treat a successful automated run as proof that every requirement is met.
- Release with an accountable owner and recovery path. Decide who approves production deployment, how monitoring will detect problems, and how to roll back or otherwise recover. Automation can make execution repeatable, but it does not transfer responsibility for the production decision.
- Measure the result and adjust. Track outcomes relevant to the original goal alongside effort, defects, delays and operational impact. Use what the team learns to refine the next application or workstream.
How to judge whether a path is economical
The lowest initial migration effort is not necessarily the lowest total cost. Estimate the work to discover and transform the application, validate its behavior, address security and compliance, integrate it with existing systems, and operate the result. Include the cost and risk of deferring change as well as the possibility that a deeper transformation requires more investment before it produces value.
- Business value and criticality: What outcome justifies the work, and what would service disruption mean?
- Scope and complexity: How many dependencies, integrations and exceptions are involved?
- Workload support: Does the chosen approach or platform support the application, language and target environment in question?
- Quality controls: Can the team review generated code, test important behavior and meet security and compliance requirements?
- Delivery and recovery: Are deployment, monitoring and rollback approaches appropriate for the workload?
- Operating model: Does the organization have the skills, toolchain integration and ownership needed to maintain the modernized system?
- Human review effort: How much specialist review remains necessary, and does that change the expected economics?
These are useful comparison dimensions, not evidence of a single best provider. AWS, IBM and Microsoft describe modernization products or services, but the available evidence here is vendor-authored rather than an independent head-to-head performance comparison. Verify current supported workloads, feature availability, controls and integration fit directly with providers, then evaluate them against a representative part of your own estate.
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What reported figures can—and cannot—tell you
Vendor and survey figures can illustrate the scale of modernization activity, but they should not be treated as forecasts for another organization’s results.
- Technical debt: IBM’s February 24, 2026 article reported a CAST estimate of 61 billion days of repair time worldwide in 2025. This is IBM reporting CAST’s estimate; it is not an independently checked figure here and does not predict the time required to modernize a particular portfolio.
- Experian example: AWS reported that Experian modernized seven legacy .NET applications with a 40% reduction in developer effort and approximately 300 engineering days saved. This is an AWS-reported customer outcome, not a typical or guaranteed result for other estates.
- AWS Transform figures: An AWS blog reported customers had saved 1,009,000 hours of manual effort while analyzing 1.8 billion lines of code. AWS documentation 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. These are AWS-reported figures with different stated scopes; they should not be combined into a single estimate of expected savings.
- Modernization survey: In a June 2, 2026 blog, Microsoft reported findings from Forrester’s Q1 2026 Cloud and AI Application Modernization Survey: 94% of IT leaders ranked modernization as a top AI-strategy priority, portfolios were 43% modernized on average, and 32% were AI-ready. Microsoft’s Azure blog also reported that 91% of IT leaders saw modernization as necessary for business AI advancements, attributing that finding to the same survey. These are Microsoft accounts of survey results; without the primary report and question wording, they are not interchangeable measures or proof of causation.
The figures illustrate why organizations are investing in modernization, but none establishes that using agents will produce a particular speed, savings or readiness level. Treat case outcomes as examples to investigate, not business cases to copy.
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Agentic workflows can coordinate work, but modernization remains a business and engineering program. Assign accountable people to decide why an application should change, which strategy fits, what architecture and security controls are acceptable, how exceptions are handled, what evidence constitutes a successful test, and when a release is safe. Those decisions set the boundaries within which automation can be useful.
That division of responsibility is especially important in older systems, where undocumented behavior, fragile dependencies or regulatory obligations can make a technically plausible change unacceptable. Keep review and approval proportional to an application’s business criticality and the potential impact of failure.
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