Before committing to a broader SAP BW modernization initiative, a global CPG company partnered with us on a proof of concept designed to validate migration readiness, assess its target data architecture, and identify opportunities to accelerate future migration activities through AI and automation.
Using our AI-accelerated Migration Factory, the organization gained clear visibility into migration complexity, reduced uncertainty, and built confidence in its modernization strategy before moving into large-scale execution.
A global CPG company operating across multiple international markets was preparing for a large-scale SAP BW modernization initiative.
Before committing to migration execution, the organization needed to understand migration complexity, validate its target data architecture, and identify opportunities to accelerate the journey through AI and automation. The company wanted a structured way to assess migration challenges, reduce delivery risk, and build confidence in its long-term modernization strategy.
Challenge:
Before launching a broader modernization program, the company needed to validate both its migration approach and target architecture through a focused proof of concept.
Years of accumulated business logic, custom data models, and tightly coupled dependencies made it difficult to estimate migration effort and potential risks accurately. The organization also needed to determine whether its target data architecture would provide a viable foundation for future analytics and AI use cases.
Key questions included:
What is the true complexity of the migration?
How much of the migration process can be automated?
Can AI accelerate migration activities while maintaining quality?
Is the target data architecture fit for purpose?
What is the most efficient path forward for future modernization waves?
Rather than moving directly into execution, the company chose to begin with migration readiness and approach validation.
We applied Migration Factory, our AI-accelerated readiness framework, to assess migration scope, validate the target architecture, and identify opportunities to accelerate delivery through AI and automation.
The proof of concept focused on four connected stages:
Discover: Created visibility into legacy assets, dependencies, and migration complexity.
Document: Converted fragmented SAP BW knowledge into structured migration intelligence.
Develop: Demonstrated how AI-generated assets could accelerate future migration activities.
Validate: Confirmed that business logic, governance requirements, and target architecture could be preserved throughout future modernization efforts.
By replacing fragmented manual analysis with an AI-supported framework for asset discovery, dependency mapping, documentation, and readiness assessment, we transformed undocumented legacy knowledge into reusable migration intelligence. The result was a clearer understanding of migration effort, architecture readiness, and the most effective path forward.
Before committing to a broader modernization program, the organization needed confidence in its strategy.
The proof of concept validated the proposed approach, quantified automation opportunities, and established a clearer path toward future SAP BW modernization initiatives. Most importantly, it reduced modernization risk before execution began.
The engagement provided complete visibility into migration assets, dependencies, and complexity while validating the target data architecture and identifying significant opportunities to reduce manual effort through automation. It also strengthened governance through version-controlled documentation and created reusable migration intelligence that can support future migration waves.
The result was a more predictable path to modernization, supported by validated architecture decisions, quantified automation opportunities, and a scalable framework for future transformation initiatives.
Migration scope defined in under two hours
100% automated metadata extraction and documentation generation
Complete visibility into migration assets, dependencies, and complexity
Up to 70% automation potential identified for re-platforming activities
Up to 80% reduction in future re-coding effort through metadata-driven conversion approaches
AI-generated first drafts reduced foundational development from days to hours
Target data architecture successfully validated
Migration blueprint created for future planning and estimation
Version-controlled documentation strengthened governance and knowledge retention
Repeatable methodology validated for future SAP BW modernization initiatives