The riskiest version of DM automation is the kind that sends every reply it generates, no matter how confident the underlying model actually was. The version worth using is the kind that knows the difference between a question it has answered correctly a thousand times and one it has never seen before — and treats those two cases differently.
What confidence scoring actually checks
Every AI-drafted reply is evaluated before it goes anywhere: how closely the incoming question matches your documented business context, whether the answer requires information the AI wasn't given, and whether the tone or subject matter (complaints, refund requests, anything legally sensitive) suggests a person should be the one responding. High-confidence, well-covered questions — pricing, shipping, restocks — go out automatically. Anything else is routed to a review queue instead of guessing.
Why "mostly right" isn't good enough for DMs
A DM sent under your business's name carries more weight than a generic chatbot response — it reads as something your brand said. That raises the cost of getting it wrong far above the cost of a slightly slower reply. Confidence-gated automation trades a small amount of coverage (some messages wait for a human) for a large reduction in the odds of an off-brand or inaccurate reply going out unsupervised.
What this looks like day to day
In practice, most stores see the review queue do the most work in the first few weeks, while the AI is still building up coverage of their specific catalog and policies, and taper off from there as the same categories of question keep getting answered correctly. The queue itself becomes a feedback loop: every time a business owner corrects or approves a flagged reply, it sharpens what the system already knows about that brand's voice and policies going forward.