A consultant configures Sales Accelerator sequences for a 20-person sales team. Reps report that the AI-generated next-best-action queue keeps recommending outbound calls to contacts a teammate already called earlier the same day, causing duplicate outreach. Calls are logged automatically through a Teams-to-Dataverse calling integration that syncs activity records on a scheduled interval rather than instantly. What is the most likely cause and correct fix?

The correct answer is the scheduled sync delay explanation: Sales Accelerator's next-best-action logic works from the activity history stored in Dataverse at the moment it generates a recommendation; it has no independent awareness of what happened in a call outside that record. When the Teams-to-Dataverse integration only syncs on a scheduled interval, a call that happened an hour ago may not yet exist as a record, so the engine still treats that contact as untouched and recommends it again. Closing that latency gap, or switching to a faster sync pattern, gives the engine current data to reason from and eliminates the duplicate suggestions. The confidence-threshold fix is incorrect because a confidence threshold governs how certain the model must be before it suggests an action at all, not whether its underlying data is current; raising it would suppress some valid suggestions without fixing the staleness. The retraining-the-model fix is incorrect because the problem is not a pattern the model failed to learn from history, it is a timing gap in the data pipeline feeding live recommendations, so retraining on old outcomes would not touch it. The turn-off-recommendations fix is incorrect because disabling the feature removes its value entirely instead of addressing the root cause, and reps would lose the benefit of prioritized outreach altogether.