Risk tiers
Auto-approve low-risk outputs with logged sampling audit.
SLAs
If queue > N, page secondary approver.
Metric
Time-in-queue by timezone band.
Checkout moments need cognitive slack
Human-in-the-loop at checkout is not about slowing shoppers—it is about protecting irreversible actions (discounts, compliance attestations, account closures) with clear affordances: who approved, when, and under what policy.
Never hide AI involvement in fine print; surface it at the decision point with plain language and a frictionless path to a human.
Design patterns that work
Use explicit confirm steps for high-risk deltas; keep AI suggestions as pre-filled drafts the agent accepts or edits, not silent field mutations.
Preserve undo within the session window where business rules allow; shoppers forgive mistakes they can reverse.
Measuring trust, not only conversion
Track abandonment at the disclosure step separately from overall funnel drop. If disclosure spikes exits, the copy or placement—not the model—is wrong.
Survey a thin slice post-purchase: “Did you understand when AI assisted?” Trend lines beat one-off NPS spikes.
Staffing the loop
Staff peak coverage when AI confidence is lowest (new catalogs, promo weekends). Understaffed loops create false negatives where humans rubber-stamp.
SignalSpring’s CX stance: the loop exists to protect customers and agents, not to check a compliance box.