Predictive dialer
A predictive dialer is an outbound calling system that starts calls before agents become available and uses pacing estimates to connect live answers to available agents.
The dialer estimates how many numbers to call from factors such as recent answer rates, average conversation length, current agent status, and call progress. Because many attempts may be unanswered, busy, or reach voicemail, it can place more calls than the number of agents free at that moment. Its purpose is to reduce the time human agents spend waiting between conversations.
That approach creates a central risk: a person may answer when no agent is ready. The caller may hear silence, experience a delay, receive a recorded message, or be disconnected. Operators therefore need conservative pacing, accurate detection of live answers, enough agent capacity, and a response for calls that cannot be connected promptly. The rules governing automated and abandoned calls depend on the campaign and jurisdiction and should be reviewed before use.
A predictive dialer differs from a power dialer. A power dialer generally starts the next call when an agent becomes available, so it is paced by known capacity. A predictive dialer starts calls based on expected future capacity, which can increase utilization but also increases the chance of a mismatch. Both are dialing methods, while an outbound campaign is the broader program defining the contact list, purpose, schedule, conversation, and outcomes.
For AI phone agents, the capacity model changes because software can handle multiple conversations at once, subject to configured concurrency and system limits. Predictive pacing may still be relevant when call volume is high or when a human must take certain live answers, but it should not be assumed to be necessary. If every answered call can immediately enter an AI conversation, straightforward controlled dialing may be easier to reason about.
Evaluation should include more than calls per hour. Review live-answer connection delay, abandoned calls, false machine detection, agent or system saturation, opt-outs, and completed campaign outcomes. Test changes gradually because answer patterns and conversation length can vary by contact group and time of day. A pacing model that appears safe under one set of conditions can exceed available capacity under another.