Hlib Storchak · 2026-08-05 · ~11 min readQuick answer
"AI SDRs convert meetings to opportunities at 15% versus 25% for human reps, a 40% gap" is repeated across dozens of 2026 blog posts, usually credited to "SuperAGI benchmarks" or "Bridge Group SDR Metrics 2026." I checked both sources directly. Neither one contains that number. Different sites citing the same alleged source give different figures for the same claim. Treat it as an unsourced statistic, not a benchmark, and use the real data below instead.
The claim you'll see everywhere
I'm Hlib Storchak. I build outbound systems for B2B founders and sales teams, and I've booked 2000+ meetings for B2B clients doing it, which is why a claimed conversion-rate gap between AI and human SDRs isn't academic to me. Somewhere in the last few months, a specific number started showing up in nearly every "AI SDR vs human SDR" post: AI SDRs convert booked meetings into qualified opportunities at roughly 15%, human SDRs at roughly 25%, a 40% relative gap. It shows up with almost the same phrasing on comparison blogs, AI SDR vendor sites, and aggregator listicles, usually attributed to "SuperAGI benchmarks" or "Bridge Group SDR Metrics 2026," sometimes both in the same paragraph.
That's a specific, citable-sounding number, and it was the premise I set out to write this article around. Before I did, I went and tried to find the actual benchmark behind it, the way I'd want a client to check any vendor's claim before it changes a buying decision. I couldn't find it. What I found instead is a good case study in how a made-up-sounding but "sourced" statistic spreads through AI SDR content in 2026, and what to check before you repeat one yourself.
Where I went looking for the source
The two names attached to the 15/25 figure most often are SuperAGI, an AI agent company that publishes a large volume of SDR-comparison blog content, and The Bridge Group, a real sales operations research firm whose SDR benchmark reports are a genuine, widely cited primary source in this industry (I've used their data in other pieces on this blog). Finding a "Bridge Group SDR Metrics 2026" citation is a good sign on its face, because that report exists. So I went to both, directly.
Method. I searched for the exact phrase and figure across the open web, then opened the specific pages that were cited as the source, not just the aggregator posts repeating the citation, to check whether the number actually appears where it's supposed to.
What SuperAGI's own posts actually say
SuperAGI has published a long series of blog posts comparing AI and human SDRs, with titles like "AI vs Traditional SDRs: A Comparative Analysis of Pipeline Performance and Cost Efficiency" and "AI vs Human SDRs: A Comparative Analysis of Cost, Efficiency, and Performance." I read through the accessible ones. They discuss meeting conversion and qualified-opportunity generation in ranges, not the specific 15% vs 25% pairing that gets attributed to them. One even gives a different range entirely: meeting conversion at 10 to 20% for human SDRs versus 5 to 15% for AI SDRs, which is not the same claim, and isn't even framed the same direction. Nowhere in what I could access does SuperAGI publish "15% vs 25%, a 40% gap" as a discrete, sourced finding.
What Bridge Group's own report actually says
The Bridge Group's actual, real 2025 SDR benchmark report does contain genuinely useful, specific findings: quota attainment at 60% of reps, described in the report's own language as the lowest on record, and 2025 marked as the first year "AI SDRs" appeared as a distinct category in their survey, at 1% of respondents. Their 2026 update on AE and quota metrics adds a real, quotable AI finding too: organizations in the highest AI Engagement Score tercile reported 57% of reps at quota, compared to 39% in the lowest tercile. None of that is a meeting-to-opportunity conversion rate, and none of it is a 15% vs 25% comparison. It's adjacent, real, and useful data, just not the specific number that gets pinned on Bridge Group's name.
The numbers don't even agree with each other
The clearest tell that a stat is unsourced is that different sites repeating "the same" citation give different underlying numbers. That's exactly what happened here. I pulled the figures as they actually appear across sources citing this claim or something adjacent to it.
| Claimed figure | Attributed to | What I found checking the primary source |
|---|---|---|
| AI 15% vs human 25% meeting-to-opportunity, a "40% gap" | "SuperAGI benchmarks" | Not present in SuperAGI's accessible posts on this topic |
| Human meeting conversion 10-20%, AI 5-15% | SuperAGI (different post) | A real figure on SuperAGI's site, but a different claim and direction than the 15/25 pairing |
| Meeting-to-opportunity 47% (human) vs 28% (AI) | "Bridge Group SDR Metrics 2026, Apollo and ZoomInfo platform data, Outreach, 11x.ai aggregates" | A four-source blended citation I could not trace to any single report; not present in Bridge Group's own published material |
| 57% of reps at quota (high AI-engagement tercile) vs 39% (low tercile) | Bridge Group's own 2026 AE and quota research | Present, verbatim, on Bridge Group's own blog |
Four sources, citing two of the same names, giving four different numbers for what's supposed to be one finding. That pattern doesn't happen when everyone is quoting the same real report. It happens when a number gets invented once, gets a credible name attached to make it feel researched, and then gets rephrased slightly by each site that repeats it, since nobody along the chain checked the original.
How a stat like this gets laundered into "fact"
The mechanism is simple and it isn't unique to AI SDR content. A blog post, often AI-assisted itself, needs a specific-sounding statistic to support a section on AI vs human SDR performance. It writes one, sometimes rounding from a real but different figure, sometimes inventing it outright, and attaches a plausible, real-sounding source, a respected research firm or a well-known AI company. The next site that wants the same section, writing about the same topic, finds the first post in a search, takes the number and the attribution at face value, and republishes it, sometimes with a small variation. Repeat that ten times across a year and you get a "widely cited" statistic that traces back to nothing, reinforced purely by how many places now say it. I've flagged this same laundering pattern before, in vendor claims specifically, in a look at Outreach, 11x, and Amplemarket's self-reported numbers. This is the aggregator-content version of the same problem, and it's arguably worse, because there's no single vendor to hold accountable for it.
Why this matters for a buying decision, not just pedantry
If you're deciding whether to pilot an AI SDR tool, or how many human reps to keep alongside one, a specific number like "AI converts at 15% vs 25% for humans" feels like it should anchor that decision. It's concrete, it's got a named source, and it confirms a plausible-sounding story. The problem is that a number nobody can verify tells you nothing real about your own funnel. If you size a hybrid pod, set a target conversion rate, or justify a headcount decision off a stat that turns out to have no source, you've made a real decision off a number that was never real to begin with. The fix isn't to distrust every AI SDR statistic. It's to check the two or three that are actually going to move a decision before you let them.
What the real, sourced 2026 SDR data does say
Strip out the unsourced figure and there's still a genuine, well-documented picture worth acting on. Bridge Group's own numbers show quota attainment at 60% of reps in 2025, the lowest on record for that survey, with a meaningful lift for teams in the top AI-engagement tercile, 57% at quota versus 39% at the bottom. The Bridge Group AI SDR category itself is brand new, just 1% of respondents in its first year, which is its own signal: most of the industry commentary on "AI SDR performance" is being written well ahead of the sample size needed to actually benchmark it. Separately, and something I've written about directly, AI SDR tools show 50 to 70% annual churn per UserGems, roughly double human SDR turnover, a real and verifiable signal that a lot of AI SDR deployments aren't working out even when the vendor's demo looked good.
What's consistently missing from the real, sourced material is any clean, independently verified "AI converts meetings to opportunities at X%, humans at Y%" head-to-head. That comparison, as a single tidy number, doesn't appear to exist yet in a form anyone can point to and defend. Anyone telling you it does is either citing the laundered stat or citing their own product's numbers, which is a different thing again.
Where I'd actually expect a quality gap, reasoning it through
None of this means there's no real gap between AI-booked and human-booked meeting quality, only that the specific number circulating isn't it. Reasoned from mechanics rather than a stat: an AI SDR books a meeting off pattern-matched signals in a reply, it doesn't hear tone, hesitation, or a half-formed objection the way a human on a call or in a message thread does. That's a plausible reason AI-booked meetings would sometimes be less qualified going in. It's also exactly why the hybrid pattern, AI for volume and initial qualification, a human for the judgment call before a meeting lands on an AE's calendar, keeps showing up as the more durable setup across the teams I've watched run both. I've laid out that case in more detail in why hybrid pods tend to beat pure-AI outbound, and the mechanism there holds regardless of what the exact conversion percentages turn out to be once someone actually measures them properly.
A checklist for verifying any AI SDR stat before you cite or buy on it
Run any specific-sounding AI SDR performance claim through this before it changes a decision:
- Open the primary source directly, not the post citing it. If it's gated or you can't find the number on the page, treat the citation as unconfirmed.
- Check whether other sites "citing the same source" give the same number. If they don't agree, none of them checked it.
- Check who benefits from the claim. A number that favors the product being sold on the same page deserves more scrutiny, not less.
- Check the sample size and date. "2026 data" with no respondent count or methodology is a marketing phrase, not a citation.
- Ask whether the claim is even the right comparison for your decision. A conversion-rate gap matters less than whether it holds at your volume, your ICP, and your definition of a qualified meeting.
Key takeaways
- "AI SDRs convert at 15% vs 25%, a 40% gap" is not present in SuperAGI's or Bridge Group's own published material, the two sources most often cited for it.
- Different sites citing the same alleged source give different numbers for the same claim, a clear sign the stat was never checked against a primary document.
- Bridge Group's real, verifiable 2025-2026 data shows 60% quota attainment (lowest on record) and a real AI-engagement lift, 57% at quota versus 39%, without a meeting-to-opportunity comparison attached.
- UserGems' 50-70% AI SDR tool churn rate is a real, sourced signal worth weighing more heavily than the unverifiable conversion stat.
- A quality gap between AI-booked and human-booked meetings is mechanically plausible even without a clean benchmark proving a specific number.
- Check any vendor or aggregator AI SDR stat against its actual primary source before it factors into a pilot or headcount decision.
What I tell clients evaluating an AI SDR pilot
The thing I actually push back on with clients isn't AI SDR tools themselves, it's decisions getting made off a stat nobody in the room can trace to a primary source. When a client brings me a vendor deck with a specific lift number, the first thing I do is exactly what I did in this article: try to find it on the cited source's own site. More often than I'd like, it isn't there, or it's there in a materially different form. That doesn't mean the tool is bad. It means the number shouldn't be the reason you buy it. I'd rather size a pilot around your own funnel's baseline numbers, measured over a real few weeks, than around a percentage someone else's marketing team attached to a report that doesn't say what's being claimed.
My honest take
I don't think most people repeating the 15/25 stat are being deliberately dishonest. I think it's a number that felt true, got a credible name attached at some point, and then spread because checking it takes ten extra minutes nobody had. That's exactly why it's worth flagging once, clearly: not because AI SDRs are secretly worse than the story says, but because the specific number people are using to make that argument doesn't hold up to a direct check. Use the real Bridge Group and UserGems figures above if you need something defensible. Skip the 15/25 pairing until someone actually shows the study behind it.
FAQ
Is it true that AI SDRs convert meetings to opportunities at 15% versus 25% for human SDRs?
I couldn't verify that specific figure. It's attributed to "SuperAGI benchmarks" or "Bridge Group SDR Metrics 2026" across dozens of sites, but neither source's own published material contains that number, and other sites citing the same alleged source give different figures for the same claim.
Where does the 15% vs 25% AI SDR stat actually come from?
I couldn't trace it to a primary document. The pattern looks like a statistic that was written once, given a credible-sounding source, and then repeated by aggregator content without anyone checking the original.
What real data exists on AI SDR performance in 2026?
Bridge Group's own 2025-2026 research shows 60% quota attainment (the lowest on record) and a real AI-engagement lift, 57% of reps at quota in the top tercile versus 39% in the bottom. UserGems separately reports 50 to 70% annual churn on AI SDR tools, roughly double human SDR turnover.
Does this mean AI SDRs don't have a meeting quality problem?
Not necessarily. A quality gap between AI-booked and human-booked meetings is mechanically plausible, since AI books off pattern-matched text signals without hearing tone or a half-formed objection. It just means the specific 15/25 number isn't the evidence for it.
How should I check an AI SDR vendor's performance claim before I buy?
Open the primary source directly, check whether other sites citing it agree on the number, check who benefits from the claim, check the sample size and date, and confirm the comparison actually matches your own volume and ICP before it factors into the decision.