A predictive dialer places calls before your agents are free, using a forecast of when each one will finish, so that a live answer and an available agent arrive at roughly the same moment. AI agents remove the forecast from the problem, because the thing that places the call is also the thing that talks. That single difference decides most of the comparison.
It also means the choice is not really made at the center level. It is made per campaign, and it turns on two numbers that most buying conversations never raise: how many whole agents that campaign actually has, and how many abandoned calls its answered volume allows. The worksheet below works both out for your own programs. On a lot of outbound floors it produces an uncomfortable answer, which is that predictive dialing was never statistically supportable on any individual campaign running there.
What does a predictive dialer actually predict?
A predictive dialer predicts agent availability. It watches how long people spend talking, how long they spend in after-call work, and how often the numbers on your list get answered, then places calls slightly ahead of the moment it expects an agent to free up. The goal is to erase the gap between calls without producing answers nobody can take.
Genesys Cloud describes its own predictive mode in exactly those terms. The algorithm takes "average handle time, after-call time, idle time, the stage setting on the agent script, and historical contact rate" as inputs, and "can launch the call before the agent is available, so that by the time a customer answers the call, an agent is just becoming available" (Dialing modes, Genesys Cloud Resource Center, read October 14, 2026).
Launching the call early is the whole trick, and it is also where abandoned calls come from. If the forecast is wrong and the customer answers while every agent is still busy, somebody is sitting on the line listening to silence. Every pacing control on a predictive dialer exists to manage that one risk.
Why does predictive dialing need a minimum number of agents?
Predictive dialing needs a minimum number of agents because its forecast is a statistical estimate, and estimates need a sample. Handle time, after-call work and contact rate all vary call to call. With a small pool of agents the averages swing too much for the algorithm to pace against, so it either dials too slowly and wastes the agents, or too fast and abandons calls.
Vendors publish floors for this. Genesys recommends "at least 15 agents in a campaign using the predictive dialing mode," and for power mode, the less aggressive setting, "at least 10 to 15 agents in a campaign," citing in both cases "the difficulty of calculating average handle time, after-call time, idle time, and contact rate for a small sample size."
The detail that catches people out comes next in the same document:
If agents log in to other campaigns, they are not "whole agents" in any one campaign. For example, if 20 agents log in to both Campaign A and Campaign B, each of these campaigns effectively have only 10 agents, which is under the recommended minimum for an accurate predictive algorithm.
Read that against how outbound floors are actually staffed. Agents get shared across client programs, pulled onto inbound when the queue backs up, and moved between lists through the day. The headcount on the roster is not the number the algorithm gets.
How many effective agents does your campaign really have?
Count whole-agent equivalents per campaign, not people on the floor. Take each campaign on its own, multiply the agents assigned to it by the share of their logged-in time they genuinely spend on it, and compare the result against the floor the pacing mode needs. Blended inbound work comes out of the same budget.
Fill this in per campaign:
| Line | What to enter | Example: renewals program |
|---|---|---|
| 1 | Agents assigned to this campaign | 14 |
| 2 | Share of their logged-in time spent on it | 0.60 |
| 3 | Effective agents, line 1 times line 2 | 8.4 |
| 4 | Do these agents also take inbound? | Yes, overflow |
| 5 | If yes, subtract the share of time inbound takes | 0.10 of total, so 1.4 |
| 6 | Effective whole agents, line 3 minus line 5 | 7.0 |
| 7 | Floor for predictive pacing | 15 |
| 8 | Floor for power pacing | 10 |
| 9 | Verdict: line 6 against lines 7 and 8 | Below both |
Now run it across a real floor. Take a BPO with 24 outbound-capable agents and three client programs:
| Program | Agents assigned | Share of time | Effective agents | Supports predictive? |
|---|---|---|---|---|
| Renewals | 14 | 0.60 | 8.4 | No |
| Collections | 10 | 0.50 | 5.0 | No |
| Appointment confirmations | 18 | 0.35 | 6.3 | No |
The floor has 24 agents and not one of its campaigns reaches the 15 the algorithm wants. Two of the three sit under the power floor as well. Nothing is wrong with this operation. It is a normal shared floor, and sharing is the point of running one. But it means a predictive dialer bought for that floor would be pacing against noise on every program it touched.
If the three programs were merged into one list the arithmetic would change. Most of the time they cannot be, because the script, the consent basis and the client are different for each.
The three percent ceiling is measured per campaign too
Federal telemarketing rules cap abandoned calls at three percent of calls answered by a person, and the measurement window is the individual campaign, not your floor. A call counts as abandoned when "a person answers it and the telemarketer does not connect the call to a sales representative within two (2) seconds of the person's completed greeting" (16 CFR 310.4(b)(1)(iv), read October 14, 2026).
The safe harbor applies where the seller "employs technology that ensures abandonment of no more than three (3) percent of all calls answered by a person, measured over the duration of a single calling campaign, if less than 30 days, or separately over each successive 30-day period" (16 CFR 310.4(b)(4)(i)).
Three percent sounds generous until you convert it into calls:
| Calls answered by a person in the window | Abandoned calls allowed |
|---|---|
| 200 | 6 |
| 500 | 15 |
| 1,000 | 30 |
| 5,000 | 150 |
A campaign that reaches 200 people in a month gets six. One badly paced afternoon uses them all, and the window runs to the end of the period regardless. So the small campaign is squeezed from both directions at once. It has too few agents for the forecast to be accurate, and too few answered calls for the allowance to absorb the forecast being wrong.
Which is why asking whether a center should buy a predictive dialer rarely gets anywhere. Predictive pacing is a large-campaign technology, and centers are made of campaigns. The federal rules above are summarized here from the regulations themselves and are not legal advice; states add their own requirements, and your counsel should review any list policy and script before a campaign runs. The wider set of pacing, hours and opt-out rules sits with the rest of the outbound setup material.
Predictive dialer vs AI agents, side by side
| Predictive dialer | AI agents | |
|---|---|---|
| What it forecasts | When a human agent will be free | Nothing; the caller is already talking to the thing that dialed |
| What sets capacity | Whole agents on that campaign | How many calls run at once |
| Smallest campaign it suits | Large enough for stable averages, 15 agents in the Genesys guidance | A single list |
| Where abandoned calls come from | A person answers before an agent is free | That gap does not open the same way |
| Who talks to the customer | A person, after the connect | The agent, transferring to a person under your rules |
| What you tune | Pacing, continuously, per campaign | Script, rules and transfer conditions, per program |
| What it needs from your floor | Agents dedicated enough to count as whole | No agents reserved to run it |
| Answering machine handling | Call analysis built for it | Voicemail read from the transcript afterwards |
The last row is a real advantage for the dialer and worth saying plainly. Dialers have spent thirty years learning to tell a person from an answering machine before a human hears anything. Telvana does not do answering machine detection, and it does not retry a number on its own. A call that reaches voicemail ends and gets tagged, and your team decides what happens next.
Which one should run this campaign?
Match the campaign to the row it lands in. The first three rows come straight out of the worksheet above, and the last two come from the conversation itself.
| Where the campaign lands | What it means | What fits |
|---|---|---|
| 15 or more effective agents and thousands of answered calls per window | The forecast has the sample it needs | Predictive pacing |
| 10 to 15 effective agents | Power pacing is borderline and predictive is unsupported | Power with real monitoring, or AI agents |
| Under 10 effective agents | No pacing algorithm has enough to work with | Preview or progressive, or AI agents |
| The conversation runs the same way nearly every time | A person is reading from a script | AI agents |
| The conversation needs negotiation, judgment or a hard decision | The cost of keeping people on it is justified | Keep people on it, paced conservatively |
Plenty of centers end up on both sides of that table, which is the sensible outcome rather than a compromise. Keep the dialer on the programs where somebody has to do the talking and move the repeatable lists off it. Doing that also raises the effective agent count on the programs the dialer keeps, because the agents stop being split.
What changes when the agent is the one talking
The pacing problem disappears, because there is nothing to pace. A Telvana agent places the call and handles it, so no answered call waits for a representative to come free. Capacity is how many conversations run at once rather than how many people are between calls.
In practice, campaigns run from a contact list inside the calling hours you set, or calls go out from your own systems through the API when an event in them says to call. The agent works the list, completes the task it was given, and transfers to a number you choose when your rules say a person is needed. That transfer is a plain one, so whoever picks up should open the call record, where every call is recorded, transcribed, summarized and tagged by outcome. The voice agent itself sends no texts; SMS is a separate channel.
The blending problem goes away too, which matters most for outsourcers. OakTech runs its clients' calling programs on Telvana with a separate workspace per client program and more than 20 agents in production. A workspace does not get thinner when the one beside it gets busy, so program economics stop depending on how the floor was staffed that week.
None of this makes the compliance question simpler, and it is worth being clear about that. The FCC adopted a Declaratory Ruling on February 8, 2024 recognizing that calls made with AI-generated voices are "artificial" under the Telephone Consumer Protection Act (FCC Makes AI-Generated Voices in Robocalls Illegal). In the United States that generally means prior express consent for the calls it covers. Different constraint from pacing, same lawyer.
For which programs to move first and what the agent does minute by minute on the call, AI outbound calling covers the mechanics. If the question behind all this is really whether to staff the work at all, outsourcing against AI agents has the cost comparison and a per-call-type scorecard.
Frequently asked questions
What is the difference between a predictive dialer and an AI dialer?
A predictive dialer forecasts when human agents will be free and places calls ahead of them, then connects whoever answers to a person. An AI dialer, more precisely an AI agent, places the call and holds the conversation itself, involving a person only when your handoff rules say so. One schedules humans, the other replaces the need to schedule them.
How many agents do you need for a predictive dialer?
Genesys recommends at least 15 agents per campaign for predictive mode and 10 to 15 for power mode, because the pacing algorithm needs a large enough sample to estimate handle time and contact rate. Count whole-agent equivalents, not headcount: agents split across two campaigns count as half an agent in each.
Does a predictive dialer abandon calls?
Yes, by design. It places calls before agents are free, so some answered calls arrive with nobody available. Federal rules cap abandonment at three percent of calls answered by a person, measured per campaign over its duration or each successive 30 day period, with conditions attached to the safe harbor.
Can you run a predictive dialer and AI agents together?
Yes, and it is the common arrangement. Keep the dialer on programs that need a person negotiating or exercising judgment, and move the repeatable lists to AI agents. Pulling those lists off the dialer also concentrates your remaining agents on fewer campaigns, which makes the pacing forecast more accurate on the ones that stay.
Do AI agents retry numbers that do not answer?
Telvana does not. The call ends, the outcome is tagged, and your team decides whether to call again, switch channel or leave it. There is also no answering machine detection; voicemail is identified from the transcript after the fact rather than during the call.
What does it cost compared with a dialer?
Telvana starts at $399 a month based on call volume, plus a one-time setup fee covering building, testing and launching the agent, with the details on the pricing page. Dialer pricing is usually per seat, which is the comparison to make: seats you staff against volume you run.
See which of your campaigns an AI agent could run
Run the worksheet on your own programs first. If the effective agent count comes out under the floor on the lists you most want to work, bring those lists to a demo and we will show you an agent handling one of their calls. The broader picture of where agents fit across inbound and outbound is in the AI call center guide.