supply_chain · energy · workflow
Uniper deploys Celonis and Microsoft AI to improve 25+ processes and achieve double-digit million savings
Uniper had abundant data but could not turn it into actionable insight because processes were opaque and data lacked context, preventing strategic decision-making and hindering its mission to accelerate the energy transition through technology.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · Data consolidation in cloud
Microsoft consolidates Uniper's data in one public cloud and provides employees access to AI services.
Tools used
Celonis Process Intelligence PlatformProcess Intelligence Graph
Outcome
By scaling Celonis enterprise-wide alongside Microsoft AI, Uniper achieved double-digit millions in savings, improved 25+ processes, and launched 30+ AI use cases, with the plant maintenance workflow delivering autonomous parts ordering, schedule alignment, and minimized downtime.
Results
Time savedsaves employees time
Volume25+
Cost replaceddouble-digit millions
Grounding & classification
Source type: vendor customer story
31 fields verified against source quotes.
agentic workflowanomaly detectionpredictive analyticsknowledge basemetric backednamed customerproduction runtime claimedsource backedtools describedworkflow describedenergycost reductioncycle time reductiontime savedvendor customer storyback office opsprocurementsupply chainagentic task executionmonitor detect alert