The existing source note cites Uber tokenmaxxing as the enterprise AI hangover: usage rose, budgets tightened, and business outcomes remained hard to prove.
Grey Haven reads token consumption as a poor adoption metric. It is easy to measure and easy to confuse with progress, but it does not show whether work became faster, cheaper, safer, or more valuable.
Operators should connect AI usage to cycle time, rework, customer resolution, approval latency, defect reduction, and margin impact inside the workflow.
The practical implementation test is whether the idea can survive handoffs, exceptions, and accountability. A useful operator memo should identify the workflow owner, the data source, the approval boundary, and the failure cost before automation expands. That framing keeps the signal grounded in production reality rather than launch rhetoric. It also gives investors and managers a cleaner way to compare vendors: not by fluency alone, but by how much controlled work the system can complete without creating cleanup elsewhere. Procurement teams should ask for sample logs, exception reports, and a named operating metric before a pilot begins. Product teams should document where human judgment remains necessary and how the system returns control when confidence drops, context is missing, or policy conflict appears. This creates a concrete adoption record that can be reviewed after the pilot instead of relying on anecdotal enthusiasm.
Watch for programs that celebrate adoption charts without accountable economic improvement. Inference volume without process change is an expense line with a narrative attached.
Source/provenance: Grey Haven Signal update linear-ASM-115-1, based on the existing Grey Haven source note.
