We Saved $500K by Letting AI Find What We'd Miss

We spent two years decommissioning a database. The last four months took AI. Six weeks later, we're still clean.

Here's the story.

Our company was paying ~$500K annually for an enterprise SQL Server license. Leadership wanted it gone. The data needed to migrate to Snowflake, and every downstream dependency had to be accounted for before we flipped the switch.

This database served multiple banks. Daily files, monthly summaries, reports, scheduled jobs — all producing outputs that financial institutions relied on. Miss one dependency and a bank doesn't get an expected file. That's not a bug. That's a regulatory conversation.

For nearly two years, teams chipped away at it. Validating Talend ETL packages. Tracing SSRS reports. Auditing SQL Agent jobs. Unwinding cross-database references that pointed back to this server from places nobody remembered.

The obvious dependencies got handled. The hidden ones weren't surfacing through manual review.

Then AI entered the workflow.

It wasn't incremental improvement — it was a different capability entirely. Without the AI-driven scans, those buried dependencies would not have been found until after we flipped the switch — causing production issues against bank-facing systems.

I used AI to systematically scan every layer: internal Reporting Services databases, legacy SSIS packages on our company drive, all DDL uploaded to Git, and every Talend Plan, Task, and Adhoc for references back to this server.

Each pass turned up hidden dependencies — the kind buried three joins deep across databases that no human catches on manual review.

Did we catch everything? Almost.

Three weeks after one of the final turndowns, a monthly summary process fired on the 1st and hit a wall. It only ran once a month — exactly why it slipped through.

If you read my earlier posts, you know what happened next. That was the emergency. Two teams, one day, full migration of a process that normally takes a sprint. AI-assisted, cross-team, production deployment. It worked.

Six weeks since final turndown. Two monthly cycles. No new issues. The database is gone. The $500K license is gone.

My boss: "How long that took was painful, but once we started using AI it got so much easier and quicker."

He's already assigned the next one — another major database, plus eliminating SSRS entirely. Same pattern: Snowflake, Power BI, eliminate legacy costs.

The takeaway: AI didn't replace two years of careful dependency analysis. It compressed what would have been another year into four months — finding stragglers hiding in nested joins that manual reviews kept missing.

Humans set boundaries and validate risk. AI does the exhaustive searching humans can't sustain at scale.

$500K saved. One database down. Next one's on my desk.