The pattern where human-authored business logic and machine-learned outputs combine to produce decisions. Most real-world implementations live here rather than in pure-ML or pure-rules architectures. The orchestration question is which layer wins when they conflict, and how rule overrides feed back into model training.
The capability a stack needs to run this pattern, and the vendors that provide it, on Martech Stack Builder.
Let the system act on its own inside clear bounds, escalate the ambiguous cases, and learn from how the human resolved them
Let a model propose the next action when it can, and serve a known-good rule when it cannot, so the experience never depends on the model being up