SAP’s recent AI Tour in Korea put Joule through its paces on manufacturing profitability analysis. The copilot traced a margin drop to a specific procurement decision within seconds, pulling data across cost centres and supplier contracts without manual querying. For companies running clean S/4HANA environments with standardised master data, this is genuinely useful. For companies that have not done that groundwork, it shows what they cannot yet access.
We spend a significant portion of our SAP practice conversations on exactly this gap. Clients want to hire for AI-enabled operations, but their ERP environments are not ready to support them. Data sits in silos, cost hierarchies differ across plants, and procurement records carry legacy formatting that Joule cannot parse cleanly. The AI capability exists. The foundation often does not.
A manufacturing client in Bavaria recently asked us for an S/4HANA finance lead who could also own the data standardisation roadmap. Their logic was simple: they wanted Joule-style analytics within 18 months, and their current data architecture would not support it. That spec is becoming routine in our searches.
For candidates in the SAP finance and supply chain space, companies are increasingly asking whether you have led the data cleanup that precedes AI deployment. Configuration knowledge gets you on the longlist. A track record of owning master data governance across multiple plants gets you shortlisted.
Korea’s demo showed what is possible when the preconditions are met. The harder question is how many companies will do the unglamorous standardisation work to get there.
Prompted by reporting from ERP Today.