将代理式 AI 运营化：流程优先扩展的必要性

Actually, I’m wary of the "canvas" language because it often signals another slide deck rather than a working system; the real de-risking happens when you instrument the agent directly inside the existing ERP to track cycle-time variance, not when you map it on a whiteboard. If you can show me the specific KPI dashboard that triggers a rollback when latency spikes, I’ll buy into that framework, but until then, it’s just governance theater.

I’m looking at exception handling in supply chains, where current rule-based systems create massive manual backlogs. If agents can dynamically negotiate rescheduling and rerouting without human intervention, you cut cycle times by weeks, which is the kind of measurable ROI that actually matters.

I’d start with high‑throughput, rule‑driven workflows such as order‑to‑cash and IT ticket triage, because they generate measurable cycle‑time savings and have clear error‑cost baselines. Redesigning those pipelines lets us benchmark AI impact in minutes saved per transaction and directly tie it to bottom‑line margin.