Evolution of Cloud Migration with AWS Transform

2025 marks the year of AI’s great bloom, driving major progress in software development and cloud innovation. AI-driven approaches are transforming how applications are migrated from on-premises to the AWS Cloud, delivering faster, more accurate and cost-effective outcomes. In response to this shift, AWS has also announced that several existing migration services, including Application Discovery Service (ADS), Migration Hub and Mainframe Modernization, will be placed in maintenance mode.

In the following sections, we will not dive deep into the migration process itself. Instead, we will review the existing AWS migration strategies and compare them with the latest AI-driven application migration approach at a high level, highlighting how it improves efficiency, accuracy and overall business value in cloud transformation.

Current Solution

In the current approach, we usually combine ADS, Migration Hub, MGN and DMS to establish a standard migration pipeline. This involves using ADS to perform comprehensive discovery and dependency mapping, Migration Hub to centralise and track migration progress through assessment and planning and DMS or MGN to execute the actual data and server migration respectively.

This standard solution has been in use for some time and has consistently delivered strong results. However, as AI continues to bring breakthroughs to the IT industry, we may adopt a more powerful and convenient migration approach. In the following section, we will take a look at the latest approach introduced by AWS’s new service, AWS Transform, and explore what it can do for us.

AWS Transform

AWS Transform is an agentic, AI-powered modernisation service that helps users assess, refactor and migrate workloads to AWS at scale. It replaces traditional migration solutions with a more AI-driven approach. As of 2025, AWS Transform offers three main workloads for migration, which we will explore in detail one by one in the following sections.

.NET

The solution demonstrates how AWS Transform simplifies and accelerates the modernisation of .NET workloads by using AI-driven automation.

By integrating with GitHub, it streamlines the entire process from code analysis to deployment, reducing manual effort and human error. Through automated assessment and transformation, legacy .NET applications can be quickly refactored for cloud readiness and deployed seamlessly to AWS compute services.

This approach enables faster migration, improved scalability and reduced operational overhead compared to traditional methods.

Mainframe

This solution highlights how AWS Transform brings automation and intelligence to mainframe modernisation, simplifying what is traditionally a complex and resource-intensive process.

By applying AI and integration with AWS services, Transform helps organisations analyse, refactor and migrate legacy mainframe applications into modern and cloud-ready architectures.

This reduces manual effort, shortens migration timelines and lowers the risk of errors, enabling enterprises to unlock greater agility, scalability and cost efficiency on AWS.

VMware

The solution demonstrates how AWS Transform modernises VMware-based workloads through an AI-driven and automated approach. With discovery agents and AWS services, it enables organisations to assess their on-premises environments, determine optimal migration paths and execute either lift-and-shift or refactoring strategies with minimal manual effort.

Transform uses automation to streamline workload replication, modernisation and deployment across AWS compute and storage services. This unified process helps businesses accelerate cloud adoption, reduce migration complexity and also improve operational efficiency while transitioning from traditional VMware environments to AWS.

Comparison

The traditional migration pipeline, which combines ADS, Migration Hub, MGN and DMS, follows a tool-based, sequential workflow that relies heavily on manual coordination. It involves multiple services and data exchanges across discovery, assessment, replication and deployment, making the process time-consuming and operationally complex. While effective for standard rehosting or lift-and-shift migrations, this approach offers limited automation and depends greatly on human effort for analysis, refactoring and orchestration.

In contrast, the new AWS Transform approach streamlines the process by using AI to automate analysis, refactoring and migration within a single integrated service. It reduces manual intervention, accelerates modernisation and supports both application-level and infrastructure-level transformation. Overall, it provides a faster, smarter, and more efficient way to modernise and migrate workloads to AWS.

Conclusion

AI marks a major step forward in cloud migration by replacing complex, manual workflows with intelligent automation. It enables faster, more accurate and scalable modernisation, helping organisations move to AWS with greater efficiency and confidence.

Looking ahead, these tools are expected to become even more powerful and comprehensive, further transforming how enterprises modernise and migrate to the cloud.