The original title is: "Why human copy-paste persists in AI workflows and how to close the automation gap"

Short summary

Enterprise AI workflows often look automated but still depend on humans to copy-paste, reformat, and resolve exceptions at system boundaries — the seams where AI output meets structured downstream systems. The author argues real automation must be measured from initial event to completed business outcome, not from prompt to response. Citing McKinsey and Deloitte research, the piece advocates for workflow redesign and human-in-the-loop systems that request judgment only where it genuinely adds value.

• •Almost-automated workflows shift manual labor to less visible seams — data transfer, exception handling, and approval bottlenecks
• •ROI should be measured from event to business outcome, not from prompt to response
• •Human-in-the-loop design should request judgment only where it adds value, not for every routine exception

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