Colombina, one of Latin America’s largest confectionery producers, recently completed its migration to SAP S/4HANA Cloud Private Edition. The headline benefits are familiar: faster month-end closes, improved cybersecurity, a platform positioned for AI. What caught our attention was the sequencing. The company treated cloud migration and AI readiness as a single project, with the infrastructure decisions driven by where they want machine learning to sit in eighteen months.
DACH manufacturing companies are starting to write role specifications the same way. A client in Bavaria asked us last quarter for an S/4HANA project lead with specific experience in pre-positioning data layers for predictive maintenance. The spec included a requirement to design the cloud architecture with AI workloads already mapped, something that would have been a separate conversation two years ago.
Companies running these searches want configuration expertise and something else: candidates who can articulate how today’s cloud choices constrain or enable tomorrow’s AI use cases. Project managers need to understand data residency implications. Functional consultants need to speak to integration points with analytics platforms. Basis administrators need experience in hybrid cloud environments where AI workloads require low-latency access to transactional data.
For candidates building their next move, the question worth asking in interviews has shifted. Can you explain what happens to the data after go-live? How will the architecture you build support workloads that do not exist yet? Companies running migrations like Colombina’s are filtering for that perspective. The ones who can answer concretely are the ones getting called back.
Prompted by reporting from SAP News Centre. Read the original article.