Layer D · Process Outcomes & Benefits Realisation
Celonis
Process intelligence platform that reconstructs how processes actually run, providing the operational evidence to test whether AI changed business performance.
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At a glance
- Control layer
- D: Value and strategy
- Five-Layer model
- Productivity Value, Delivery Alignment
- Status
- Active
- Typical buyers
- AI leadership (CAIO), Technology leadership (CIO)
- Delivery
- Not yet assessed
- Process intelligence
- Outcome tracking
Spend controls it documents
Not a spend control
Value evidence it is designed to record
- Measured process change
Buyer fit
Position it as an outcome evidence layer, not a token cost platform. Process mining shows cycle times, handoffs, exceptions and rework before and after an AI deployment, and process simulation estimates downstream effects in advance. Fits large ERP-centred estates where the AI benefits case rests on process change.
Market outlook
Established category leader in process mining. Positioning is shifting towards being the context layer for enterprise AI.
What to check
- Does not measure AI cost. Join its process data to cost data from a FinOps or TBM platform.
- Baseline the process before the AI deployment, or the comparison will be weak.
- Implementation depends on clean event data from source systems; budget for it.
Sources
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By Ed Epstein, who works at IBM. The postings on this site are my own and don't necessarily represent IBM's positions, strategies or opinions. About and disclosure · Methodology