Our team at Alpha Solutions recently built a complete data migration solution for a commerce platform switch. Millions of records. Four days. We had never worked on the source system before. Never even seen it.
No legacy experts on the team. No weeks of business analysis. No lengthy requirements documentation before we could start.
I want to walk through exactly what we did, because I think it illustrates something important about how AI is changing what implementation work actually looks like in practice.
A few years ago, a migration project like this would have started with a very different set of questions. Who on the team has worked on this legacy system before? Can we find a contractor with experience on that platform? How long will it take to map the data structures before we can write a single line of migration logic?
The search for the right expertise alone could take weeks. And even once you had the right people, the process of understanding an undocumented legacy system, figuring out how data was structured, where the edge cases lived, what the business rules buried in the database actually meant, was slow, manual and expensive. That is the work that historically burned project budgets before a single line of migration code was written.
We did it differently.
We used AI to read and understand the legacy system from the ground up. The first step was feeding the system's structure into Claude Code and GitHub Copilot and letting them map the data. But mapping was just the starting point. What followed was something closer to a conversation. We started asking the AI direct questions about what it was seeing. Can you review this table and tell me what is stored in it? Are there dependencies? Can you review the data types? The answers came back structured, clear and immediately useful. Our team went from staring at an unfamiliar system to having a working understanding of it in hours rather than days.
Where the core business logic lived in APIs, Claude Code was particularly effective. It traversed the codebase and converted the logic into plain English, which we used as the basis for our business requirements. This is work that normally takes weeks of workshops and meetings to produce. We had it in a fraction of the time, which meant when we did sit down with the business team to verify, the conversation was fast. A structured document is a lot easier to review than a raw codebase. Fewer meetings, sharper decisions.
We also asked AI to analyze the system and highlight anomalies and edge cases. It did a thorough job. Some of what it surfaced predated the combined knowledge of most of the client team, behaviors and exceptions that had been baked into the system for years that nobody currently on the team knew existed. We documented all of it before writing a single script, which meant we could design for the edge cases from the start rather than discovering them mid-migration.
We documented the edge cases with the business, got the full migration plan reviewed and approved, and then used AI to write the migration scripts. Once the data was moved, we used AI to run a comparison between the migrated dataset and the source. The validation summary gave us exactly what we needed to refine the mappings. We iterated through the process, addressed what the analysis flagged, cleaned up and ran again. Each pass got cleaner. Eventually we got it done.
Four days for the scripting and planning work. A migration that would have taken weeks of discovery and development under the old model.
The honest answer to why this worked is that AI didn't do the thinking. It compressed the time it takes to execute the mechanical parts of the process, understanding, mapping, scripting, validating, so that our team could focus almost entirely on the judgment calls. What does this data mean in a business context? Is this edge case a real exception or a data quality problem? Does this mapping preserve the integrity of the original logic? Those are questions that still require human expertise and experience. AI just meant we could get to them faster, without spending weeks on the groundwork first.
If you are planning a platform migration in 2026, the timeline and cost assumptions you are working from may be significantly off if they are based on how this work used to be done. The phases that historically consumed the most time are exactly the phases where AI has the most impact. Which means the overall cost curve for a well executed migration is meaningfully lower than it was two years ago.
That is good news for budgets. But it also raises a question worth asking your implementation partner directly: how are you using AI in your migration workflow, and what does that mean for my timeline and cost?
If they cannot show you a specific answer, it is worth understanding why.
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