The Evidence Map for AI-Assisted Modernization
Use this to decide whether an AI modernization proposal can enter a controlled release path, not whether its demonstration is impressive.
Gates before you scale
Choose one business flow, source component, data set, interface set, exception class, performance constraint, and retirement condition.
Link requirements, source routines, data transformations, interfaces, recovered documentation, generated candidates, tests, reviewer, and sign-off evidence.
Run source-versus-target comparisons, data reconciliations, contract tests, negative cases, and production-like performance checks. Inherited tests are necessary but insufficient.
Track rework, review load, defect escape, reconciliation pass rate, accepted-release lead time, and parallel-run cost beside code output.
Make rollback, ownership, and the old-component retirement decision explicit. A translation that creates a permanent parallel estate has not proved economic value.
Owner, briefing, proof
The accountable business owner decides which outcomes and exceptions must survive, and signs the acceptance record.
A release packet names the behavior boundary, baseline, risk, interfaces, evidence gaps, and decision gate.
Traceable source-to-test-to-reconciliation-to-approval evidence, plus measured post-release behavior and retirement outcome.
Claim ledger
What would change our mind
An independently evaluated, multi-release legacy-modernization study that measures AI contribution through validation, data conversion, interface work, security review, parallel run, rework, production outcome, and confirmed retirement. That would answer whether AI reduces total modernization cost or only moves work into the proof queue.
Where this can lead
A sponsor can begin with a release-level diagnostic: owner, behavior boundary, evidence gaps, and acceptance test. If the diagnosis finds a recurring proof gap, build the ledger and release controls across the modernization portfolio.