By Elogic Commerce · featuring insights from Paul Okhrem
Replatforming in B2B ecommerce is one of the highest-risk projects a commerce team will run. Complex data migrations. Legacy integrations. Customized business logic that lives in undocumented code. Catalog structures that evolved over a decade without architectural guidance.
The failure rate in large-scale B2B platform migrations is not a secret. Projects run over time, over budget, and sometimes over scope in ways that damage business operations during the transition. The risk is real and it's been real for years.
What has changed in the past 18 months is that AI is now creating genuine leverage at several of the highest-risk points in the migration process. At Elogic Commerce, we've integrated AI into our migration methodology in ways that have measurably improved timelines and reduced the error rate at key stages.
Paul Okhrem's framework for evaluating AI in operations always asks: where is the human time going, and is AI freeing it up for higher-value work? In platform migration, the answer is specific and significant. For detail on how he evaluates AI investments systematically, see paul-okhrem.com.
Where AI creates leverage in B2B platform migrations
Legacy code analysis and documentation. Every B2B migration involves inheriting customized code — extensions, modules, custom business logic — that is underdocumented or completely undocumented. Understanding what this code does before deciding whether to migrate, rebuild, or retire it is typically a slow, expensive manual process.
AI-assisted code analysis has materially changed this. We run legacy codebases through AI analysis that produces structured documentation of custom logic, flags dependencies, identifies where platform-standard behavior has been overridden and why, and prioritizes the customizations that require human architectural review versus those that are straightforward to handle.
On a recent migration engagement, this analysis reduced the codebase review phase from an estimated 8 weeks to 3 weeks — with a more complete output than the manual process had produced on previous comparable projects.
Data mapping and transformation. The data migration in a B2B platform project is often where timelines blow up. Catalog data structured for one platform needs to be restructured for another. Customer records need to be mapped. Order history needs to be preserved and transformed. The complexity grows with catalog size and the number of years of accumulated data.
AI is now part of our data mapping workflow. Given a source schema and a target schema, AI generates initial mapping proposals that our data engineers review and refine rather than build from scratch. For large catalogs, this acceleration is substantial — the analysis that used to take weeks happens in days.
QA test generation. Post-migration QA is traditionally a manual, coverage-limited process. A QA team can only run so many test cases. AI-generated test suites, built from the documented business logic of the source platform, cover a significantly broader surface area. In our practice, AI-generated QA has caught integration failures that would have passed manual testing.
Content migration and enrichment. For clients moving to platforms that require richer product content — better structured, more attribute-complete, optimized for AI search — the gap between what their current catalog contains and what the new platform needs is often significant. AI-assisted content enrichment — filling attribute gaps, standardizing naming, generating specification summaries — handles the volume that manual content work can't.
Where AI doesn't help in migrations
Paul Okhrem is consistent on this in his consulting practice: identifying where AI doesn't help is as important as identifying where it does. The same principle applies in Elogic's migration work.
Architecture decisions. The decision about which platform to migrate to, how to structure the composable architecture, which integrations to rebuild versus replace — these require architectural judgment that AI cannot provide. AI can research options. It can't make the judgment call.
Business logic validation. AI can document what the legacy code does. It cannot validate whether that is what the business should be doing on the new platform. That validation requires the people who run the business. In every migration we've led, the most important meetings are the ones where business stakeholders review the documented legacy logic and decide what to carry forward.
Integration testing against live third-party systems. ERP integrations, 3PL connections, payment gateway configurations — testing these against live systems requires environments and access that AI cannot create. The AI-generated test coverage is the starting point; the human-run integration testing is the validation.
The migration engagement model Elogic uses
Our migration process incorporates AI at the phases where it creates genuine leverage: codebase analysis, data mapping, QA generation, and content enrichment. The phases that require human judgment — architecture, business logic validation, integration testing, client communication — stay with our team and with the client.
The result is a migration process that moves faster at the technical phases without cutting corners on the judgment phases. Our last three major B2B platform migrations came in under their original timeline estimates. One project that was scoped at 14 months delivered in 11. The AI leverage in the technical phases created room for more thorough business validation in the judgment phases.
For the framework Paul Okhrem uses to evaluate where AI creates leverage in operations — and where it doesn't — see paul-okhrem.com.
Elogic Commerce leads complex B2B ecommerce migrations for manufacturers, distributors, and B2B brands. Founded by Paul Okhrem in 2009. If you're planning a replatforming project and want to discuss how AI can reduce your risk, reach out.
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