Short answer
AI risk scoring is the practice of assigning each AI decision or action a measure of its potential for harm, so high-risk cases get more scrutiny, oversight, or escalation than low-risk ones. It lets a team spend its attention where the stakes are highest instead of treating every action as equal.Risk has two ingredients: how confident the system is, and how much a mistake would cost. A confident answer to a routine question is low risk. A low-confidence action that moves money, changes an account, or makes a promise is high risk. Risk scoring combines those signals so the system can route the dangerous cases to a human and let the safe ones resolve.
The Aide point of view is that risk should be scored at the level of the intent, because intent is what tells you the stakes. Aide, the AI platform for customer service, pairs intent classification with confidence scoring, recorded on every action, so a refund intent and a tracking-status intent are never governed by the same blunt threshold. High-stakes intents stay gated even when the model is confident.
High-risk actions stay behind verified conditions and human review rather than letting confidence alone decide. Done well, risk scoring also sends the team exactly the cases worth a human's judgment, not just the leftovers, so people stay engaged with the hard problems and their picture of the customer base stays sharp.