AI agents recommend next best actions by analyzing the current interaction in context of customer history, product data, and outcome patterns from similar past interactions. During a live conversation, the AI identifies where the customer is in their issue resolution journey and surfaces the action most likely to achieve resolution efficiently - whether that is applying a retention offer, escalating to a specialist, suggesting a self-service step, or scheduling a follow-up. Recommendations are ranked by predicted effectiveness based on machine learning models trained on thousands of similar interactions. Agents see these suggestions in real time and choose whether to follow them, maintaining human judgment in the loop. Over time, feedback from agent decisions improves model accuracy. NiCE AI Models power next best action recommendations trained specifically on contact center interaction data.
How It Works