Busting process ghosts - how to keep enterprise AI from seeing processes that don't exist
AI agents automating processes that don't exist. Celonis warns: without real operational grounding, agent ROI becomes equally ghostly.
Why it matters
Enterprise AI agents need live operational context—not generalized knowledge or stale process models—to avoid automating phantom workflows. This is a grounding and governance problem that affects agent deployment outcomes.
The key facts
10 to knowCelonis (process mining) identifies 'process ghosts' as a failure mode for enterprise agents
Agents relying on generalized knowledge or outdated process models automate non-existent processes
Agent ROI failure traced to lack of real operational context and live process visibility
Implies need for live process data layer beneath agent execution (process mining as agent grounding)
No quantified deployment data or customer case provided; warning/guidance framed as vendor thought leadership
Celonis identifies 'process ghosts' as a failure mode in agentic automation
Agents relying on generalized knowledge or outdated process models automate non-existent workflows
Enterprise AI ROI depends on agents having specific operational context, not generic training
Real-world process state must inform agent decision boundaries to avoid phantom automation
This is an agent grounding and observability problem, not a model capability problem
Go to the source
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Publisher excerpt: AI agents need specific operational context to successfully drive business process automation. Celonis' Manuel Haug warns that agents left to rely on generalized knowledge or outdated process models will automate processes that don't exist in the real world – and AI ROI becomes equally ghostly.