Manufacturing
The AI conversation has moved from the back office to the plant floor.
Predictive maintenance and agentic automation are no longer pilots; they are expected. The systems making that possible are the ones we have spent twenty years building.
What is current
What is actually being decided right now.
The technology choices look similar on a feature list. The operating priorities do not.
Agentic automation reaching the shop floor, not just back-office RPA
OT and IT systems that were never meant to communicate now required to work together
SAP Joule evaluated against custom agents built on plant-level data
The AI layer, for Manufacturing
Native or custom. Decided against your plant, not a demo.
Options are evaluated against the systems, controls, economics, and ownership model already in place.
SAP Joule
Strong where the use case stays inside the SAP data model: finance, supply chain, and procurement.
Custom agents on OT and MES data
Necessary where a use case needs plant-floor and SAP data reasoning together.
Five-Factor evaluation
Workflow fit, data boundary, security, economics, and operating ownership guide each decision.
Industry context changes the answer.
Where this shows up
What disciplined systems work actually changes on the floor.
Evidence and operating patterns from work in comparable enterprise environments.
30% improvement
SAP S/4HANA modernization improved on-time delivery for a global manufacturing enterprise.
Under 45 minutes
CloudUnifAI reduced resolution time from four hours, with 65% of incidents handled through auto-remediation.
80% less effort
A unified Azure analytics warehouse reduced manual reporting effort for a packaging and distribution company.
Where to start
The practices behind this work.
Start with the operating outcome. Bring in the practices required to make it production-ready.
Tell us what runs your plant floor. We will show you where AI pays back first.
Bring us the systems, constraints, and outcome. We will bring an architecture point of view grounded in delivery.