06 · Migration

Enterprise Data Migration

Source-to-target migration with defined mappings, reconciliation checks and cutover controls.

Proposed extension stack Python Enterprise LLM SharePoint

Case scope: The enterprise process, systems and control foundation reflects hands-on delivery patterns. AI and agent components shown below are proposed design extensions.

Mapping checks Exception routing Reconciliation evidence Cutover controls

At a glance

Business problem Inconsistent source data, unclear mappings and reconciliation gaps can create significant risk before cutover

My contribution Mapping requirements · business rules · migration validation · cutover support

Current environment SAP / legacy exports · mapping specifications · validation evidence · SharePoint

Business value Mapping and reconciliation exceptions remain visible to owners before the cutover decision.

Proposed AI extension · AI / Agent / Human

Helps resolve uncertain mappings, explain reconciliation failures and group related migration issues for review.

Agent

Keeps migration exceptions moving to the right owners and tracks unresolved items through cutover readiness.

Human

Business and data owners approve mappings, exceptions and overrides. Final cutover sign-off remains human.

Current workflow

01Source assessment

02Mapping

03Transformation

04Load validation

05Reconciliation

06Cutover

What I owned

Mapping requirements Business rules Source profiling criteria Duplicate criteria Reconciliation rules Exception categories Ownership model Cutover-readiness criteria Acceptance criteria UAT AI use-case definition AI output requirements

Proposed AI-enabled architecture

SAP / legacy export→ SharePoint→ Python profiling + validation→ Enterprise LLM→ Exception queue→ Owner review→ Human approval→ Reconciliation evidence + cutover decision logged

Technical design

MVP scenarios

Scenario 1 Clean record. Record satisfies mapping and validation rules. Pass

Scenario 2 Known validation failure. Required field, datatype or predefined rule fails. Fail

Scenario 3 Ambiguous mapping. More than one plausible target field exists. AI explains likely options and routes for approval. Review

Scenario 4 No target equivalent. No clear destination field exists. AI surfaces the closest candidates or flags that no suitable match is evident. Review

Scenario 5 Reconciliation mismatch. Source and target totals do not reconcile. AI groups affected records and summarizes likely causes. Investigation

Scenario 6 Duplicate or conflicting records. Python detects duplicate or conflicting source records. AI explains the likely business impact. Exception

Scenario 7 Insufficient context. Required mapping rule or business decision is missing. AI does not guess. The agent routes the gap for clarification. Decision required

Controls and governance

Profiling, duplicate detection and reconciliation rules remain deterministic.

AI only handles interpretation and unresolved exceptions.

AI does not approve mappings or cutover readiness.

Missing context triggers review, not inference.

Business and data-owner approval is required for mapping exceptions.

Human overrides and reasons are logged.

Final reconciliation and cutover sign-off remain human decisions.

Implementation note

Azure OpenAI is the proposed LLM for interpreting migration exceptions and reconciliation failures.