How to Validate an Access to PostgreSQL Migration With AI-Assisted Workflow Testing

An Access to PostgreSQL migration is not proven by a successful data load. It is proven when the people who rely on the application can still complete the work that keeps the business moving.

Tables can migrate cleanly, records can appear in the new database, and the Access application can open without an error. Those checks matter, but they do not confirm that a user can find the right record, apply the right filters, update it correctly, and produce the report the business depends on.

Start with the work, not the schema

Most Access applications grew around real operational needs. Over time, they collect forms, saved queries, report logic, special processes, and shortcuts that are obvious to experienced users but absent from technical documentation. The schema is necessary context. It is not the whole story.

We begin by having client users walk through how they actually use the application in recorded Zoom sessions. They show how they look up records, narrow results, enter or update information, run reports, and deal with the exceptions that tend to matter most at month-end, during an audit, or when an unusual record appears.

Turn walkthroughs into repeatable test cases

We use AI to turn those walkthroughs into structured test cases. A user may demonstrate how to locate an account, filter related records by date and status, update a field, and run a report used in a weekly review. The resulting test documents the setup, user actions, expected results at meaningful checkpoints, and the final outcome that confirms success.

AI reduces the manual effort of transcribing and organizing a walkthrough. It does not replace review. A developer or business analyst approves the normalized test before it enters the regression suite, which keeps the client workflow, expected outcome, and test data grounded in the business rather than in an inferred assumption.

Automate the workflow through the Access application

The approved workflow can then be automated through the desktop application. For Windows based UI automation, our team has used pywinauto to execute the same interactions a user performs in Access, including opening forms, entering values, applying filters, moving through a process, and checking what appears on screen.

This approach finds defects in the place users experience them. A workflow can fail even when the underlying tables look correct, for example when a form returns incomplete data, a filter behaves differently, a report total changes, or an update no longer saves as expected.

Test the experience and reconcile the data

Desktop automation answers one question: can a person still complete the workflow? It must be paired with data reconciliation, which answers another: did the workflow return and save the correct data?

For each meaningful checkpoint, the test can validate records, totals, statuses, dates, filters, calculations, and report output against expected values in PostgreSQL. Testing only the interface can miss a data problem behind a normal looking screen. Testing only the database can miss a form, filter, report, or update path that no longer works. The migration needs both checks.

Keep evidence and make it reusable

Each approved workflow becomes a regression test that can be run again after a query change, form update, integration fix, or later phase of the migration. We retain the source walkthrough, test data, approved steps, expected results, screenshots or execution logs, relevant record identifiers, and the final pass, fail, or exception result.

That gives developers a reproducible defect report, gives project managers a clearer view of readiness, and gives client stakeholders evidence of what was actually tested. It also turns one migration effort into a practical regression asset for the application going forward.

Migration confidence means proving the work still gets done

The goal of an Access to PostgreSQL migration is not merely to move data. It is to preserve the business capability that made the application valuable. Combining client walkthroughs, AI-assisted test design, desktop automation, and data reconciliation gives the team a disciplined way to prove that the work still gets done.