ROI of AI-Assisted Database Migration

ROI Without the Theatre

Most migration ROI models are built backwards: pick an attractive percentage, multiply by a big number, present the result. A defensible one works the other way — measure the inputs, state the assumptions, and let the reader attack any line.

This is the model we would use, with the parts we can measure marked as measured and the parts we cannot marked as assumptions you must supply.

The Inputs

MEASURED (from your assessment, in an afternoon)
  object count by type
  automation rate on your schema
  hard-construct count
  PL/SQL line count
  dead objects (no reads, no writes, all-NULL columns)

YOURS TO SUPPLY (nobody else can honestly provide these)
  blended engineer cost
  hours per manually-converted object, from your own history
  number of rehearsal cycles your change process requires
  cost of a month of elapsed project time

Notice what is absent: any vendor-supplied efficiency multiplier. If a model’s headline result is driven by a number the vendor chose, it is a brochure.

Where the Return Comes From

• Scope reduction. Usually the largest and least discussed. Objects you decide not to migrate cost nothing to migrate — and a measured assessment routinely finds tables untouched for years and columns that are entirely NULL.
• Conversion automation. Direct and easy to compute: total objects minus the flagged remainder, times your hours-per-object.
• Defects caught early. The compounding one. A defect found at conversion costs an hour; the same defect found in UAT costs days, most of it spent identifying which object caused it.
• Elapsed time. Every month saved is a month of not running two systems, two backup regimes, two on-call rotations.

What Doesn’t Move

Application-layer SQL changes, UAT duration, and change-approval time are broadly unaffected by database tooling. On many projects those are the longest phases, and a model that shows them shrinking is not modelling your project. Being explicit about this is what makes the rest of the model credible.

The Term Everyone Omits

The dominant financial risk in a migration is not slow conversion — it is a failed or rolled-back cutover. One aborted go-live consumes more budget than the entire conversion phase, and it is caused by defects that were invisible until production traffic hit them.

Which reframes what validation is for. Column-level checksums and per-partition proof are not a quality nicety; they are the mitigation for the single most expensive event in the project. Any ROI model that omits the probability of a failed cutover is omitting the largest term.

Our Position

We are not going to publish a return multiple, because we would have to invent the inputs. Run the assessment — it is free and it changes nothing in your database — and put your own numbers into the structure above. If the result doesn’t justify the tool, it doesn’t, and you will have a better plan either way.