Traditional migrations are difficult. They’re manual, expensive, and often take longer than anyone wants. Migrating infrastructure to the cloud isn’t just about changing where your servers live; it’s about changing how you operate. However, with the advent of artificial intelligence, it has now become easier to accelerate the work and strip out the inefficiency.
With an AI-assisted migration, you have the advantage of it taking less time, at a lower cost, and with increased future capability. The main case for using AI to speed up your move comes down to basic economics. A faster migration means a shorter period of running both your old and new environments, which cuts your hosting costs directly. Applying AI tools also stretches your budget further, helping you maximise AWS funding programmes. Beyond the move itself, doing this work well sets a robust foundation. Once you land in AWS, your teams have immediate access to the AI services they need to improve your products and internal systems.
Accelerating the Discovery Phase
Discovery is often the most significant bottleneck in any migration project. Traditional, manual auditing of server logs, configurations, and unstructured technical notes is slow and prone to human error. AI changes this by automating:
- Information Ingestion: LLMs can directly ingest raw technical sources—including documentation, knowledge bases, logs, and config files, to extract key data and produce structured, standardised reports.
- Dynamic Dependency Mapping: Advanced AI tools can parse operational configurations to map complex application relationships and capture infrastructure baselines, effectively removing human bottlenecks in legacy environment analysis.
- Rapid Assessment: Automated intelligence extraction eliminates manual audits, allowing for significantly faster workspace assessments.
Executing the Migration Phase
The added advantages that cloud-native AI tooling and services provide you have also made the actual migration phase much easier.
AWS Transform
Organisations are turning to agentic AI tools like AWS Transform to accelerate their cloud journey. AWS Transform serves as an autonomous engineering layer that analyses, plans, and executes the migration of legacy workloads. Its structured process covers the entire lifecycle:
- Discovery & Assessment: Scanning codebases and infrastructure to map dependencies.
- Wave Planning: Grouping applications to migrate together without disrupting systems.
- Human-in-the-Loop Review: Enabling cross-functional teams to approve and adjust AI-generated plans.
- Execution & Validation: Automatically refactoring code, modifying database schemas, and generating test suites.
Key features of the service include:
- VMware and infrastructure migration
- Mainframe modernisation
- Windows and .NET modernisation
- AI workload migration
- Custom modernisation and reducing technical debt
AWS Transform has helped customers migrate and modernise their workloads by analysing their code to automatically extract hidden business logic, map intricate system dependencies, and deterministically generate modern, secure replacements.
As of now (August 2026), seven billion lines of code have been analysed and 1,942,058 hours of manual effort have been saved using AWS Transform.
Kiro
Kiro, an AI-powered IDE assistant, shifts the dynamic. Rather than just offering code completion, it works as an agentic partner that understands your specific project context.
- Spec-driven development: You define the intent, and Kiro maps out the implementation, helping you move from initial specification to functional code faster.
- Agentic chat: It explores your codebase to answer questions about architecture and dependencies, reducing the time spent hunting for context in legacy documentation.
- Routine automation: Through agent hooks, it handles the repetitive, undifferentiated heavy lifting (like standardising boilerplate or refactoring routine tasks), letting you focus on high-value logic.
By automating the mundane, Kiro allows a single developer to do the work that previously required a larger team, keeping your migration momentum high without sacrificing quality.
Using an AI tool to rewrite code or map dependencies doesn’t remove the need for good engineering practice. If anything, it requires more rigour.
You still need experienced people to review the plans, check the assumptions, and ensure the automated refactoring meets your standards. What AWS Transform does give you is a way to clear the heavy lifting and pay down technical debt faster. By letting the AI handle the repetitive, manual work, your team gets back the time they need to build a solid, reliable foundation in AWS — one you can actually trust when you push to production.
Migration is rarely straightforward. You’re often faced with untangling years of technical debt while keeping the business running. It’s hard work, but it becomes manageable when you stop treating it like a single, massive launch.
KEY TAKEAWAY
Start by auditing your estate. Find where your teams lose time to manual, repetitive tasks. Those are the areas AI can handle first. Once you’ve cleared that hurdle, look at the AWS funding programmes available to offset your costs, from deeper discovery activities right through to execution. And remember, you don’t need to do it all at once. You just need to get the foundations right.
