Use when a search or investigation could run indefinitely and you need a stopping rule. Set an explicit "good enough" threshold and stop at the first option that clears it.
Start with means, not goals; co-create with partners; leverage contingencies. Use for startup strategy, innovation projects, and uncertain/novel domains where planning is unreliable.
When a constraint is treated as fixed ("too expensive", "impossible", "always done this way"), ask whether it's physics or just convention, then rebuild from what's actually true.
Deciding what to build or why a feature isn't adopted. Reframe from features to the "job" users hire the product for — the progress they seek — to prioritize and position.
Use when a defect is selective (some endpoints/regions/users/times affected, not all) and the cause is unclear — map what IS vs IS-NOT affected; the boundary contrast points at the root cause.
Use when picking where to intervene in a system and tuning parameters keeps not sticking—rank candidate interventions by Meadows' hierarchy and choose the highest-leverage point you can move.
Choosing a technology/framework/dependency and longevity matters. Use the heuristic that for non-perishable things, expected remaining life is proportional to current age — favor the proven.
Use when provisioning capacity, setting a timeout/limit, or committing to an estimate under uncertainty. Size a buffer to the cost of being wrong instead of optimizing to the edge.