What this means in practice

  • Place approvals by consequence, not on every step.
  • Show the reviewer the evidence the AI used, not just its conclusion.
  • Set stop rules for low confidence, missing data and out-of-scope requests.
  • Remove approvals only when the record shows they are consistently unnecessary.

How Lid Vizion approaches it

We start with the business decision and operating context, define ownership and measurable milestones, then use configuration, integrations, automation, or custom software only where each earns its place.

See it as a workflow

  • Guest and customer review responses: Collect new public reviews in one place, draft a specific, on-brand reply to each, flag the ones that need a manager, and share recurring themes with the team that can fix them.