AI can speed up Cloud Modernisation & the decisions still belong to us (at least for now)

Moving an application to the cloud is not the same as making it ready for AI. AI workloads need data that is available when they need it, systems that can scale, and good automation and governance. If we only move an old application as it is, we may end up with the same limitations in a different place.

That’s why I think it helps to separate migration from modernisation. Migration changes where an application runs. Modernisation changes how it is built, operated and improved over time.

Before AI agents entered the picture, the modernisation journey already had a familiar shape: discover and analyse the application, decide what to do, migrate it, and decommission what is no longer needed. Those steps still matter. What changes is how much of the repeatable work we can now accelerate.

An agent can help explore a codebase, identify dependencies, review configuration and suggest possible modernisation paths. It can also help draft a plan or prepare code changes. That’s useful, especially when the work is repetitive and the application has a lot of moving parts.

As AI reduces the effort needed for some migration tasks, it also changes how we plan the work. Teams may need fewer hours for parts of the assessment or code changes, while spending more time on review, testing and decisions. That affects team composition and timelines. We should not assume every task becomes faster by the same amount, or simply apply a productivity multiplier to the old plan. The work is shifting, and our estimates need to reflect that.

But the agent does not automatically know the full business context. It may not know which behaviour is critical to users, which integration is still important, or what level of risk the organisation is willing to accept. Those decisions need people who understand the system and the business around it.

There is another challenge, too. Once an agent can generate a large amount of code, a person may no longer be able to judge the full outcome just by reading through the changes. Code review is still useful, but it cannot be our only evidence that the application works as expected. We also need automated tests, checks against important user behaviours, and good observability once changes are running.

In most of migration and modernization agents the workflow is: Assess - Plan - Review - Execute. The key step was reviewing the plan before changing the code. That gives the team a chance to check the proposed scope, spot assumptions and agree on what should happen next.

This matters even more when an application has no reliable automated tests. In that situation, “the code changed successfully” does not prove that the application still behaves as users expect. The team needs to think about how to validate the important behaviours as part of the modernisation work.

For me, a good way to work with agents is to let them propose and accelerate, while keeping people responsible for validation and decisions. Give the agent useful context. Work in small steps. Review the plan before execution. And make sure there is a way to check the result, not just a way to produce code faster.

Cloud modernisation for AI is not simply a technology upgrade. It’s also a change in how teams do the work. And yes, agents can take on more of the repeatable tasks, which gives us more time to focus on architecture, business outcomes and the decisions that really need human judgement.

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Cloud as a Story - Vunvulea Radu

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