Migrating Legacy Business Objects Reporting to AI-Native Analytics: A Five-Stage Maturity Model and Cost-Benefit Analysis Framework
Keywords:
AI-Native Analytics, Analytics Migration, Cost-Benefit Analysis, Enterprise Business Intelligence, Maturity Model, SAP BusinessObjectsAbstract
When SAP announced the Business Data Cloud in February 2025, consolidating Datasphere, Analytics Cloud, and Joule AI capabilities into a single governed platform, it converted a long-held strategic question for tens of thousands of BO-dependent organizations into an immediate operational decision. The question is no longer whether to migrate but how, how much it will actually cost, and what a migration needs to accomplish to justify the investment. No scholarly framework has answered these questions for the BO-to-AI-native migration context. This paper introduces the Analytics Migration Maturity Model (AM3), a five-stage framework mapping the migration journey from legacy BO dependency through hybrid operation to fully AI-native analytics capability. AM3 assesses four dimensions that infrastructure migration frameworks omit: technical readiness, organizational capability, data governance maturity, and AI adoption capacity. The Report Disposition Framework (RDF) provides systematic criteria for classifying BO artifacts as Migrate, Modernize, Retire, or Consolidate, the most consequential early decision in any migration program. The Total Cost of Analytics Ownership (TCAO) framework quantifies both direct and indirect migration costs, introducing the AI Adoption Delay Cost (AADC) as a novel component that makes the compounding competitive cost of delayed migration financially explicit. Retrospective validation across three large-scale enterprise migrations spanning consumer goods, healthcare, and manufacturing confirms AM3's descriptive accuracy and reveals the systematic cost underestimation patterns that motivated the TCAO framework's development.
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