At SaaStr AI London, Amelia and I went deep on our AI SDR journey. We shared all our data, all the emails we’ve sent, all the performance metrics—everything. And the response was overwhelming.
But here’s the thing: the #1 objection we kept hearing was “Yeah, but this won’t work for me. I don’t have your scale. I don’t have your data. I don’t have 10 years of history.”
That’s simply not true.
If you have customers, if you have revenue, if you have a database of any size—AI agents will work for you. You don’t need as much data as you think. You don’t need as much trailing history as you think. What you need is a methodology.
Here’s what we’ve learned after sending 60,000+ hyper-personalized emails, booking 130+ meetings automatically, and generating 15% of our London event revenue through AI agents alone. It could 50% by the time of SaaStr AI Annual in May 2026.
The 5 Biggest Learnings From Deploying AI SDRs
#1. AI Agents Crush the Work Humans Won’t Do
This is the single most important insight we’ve discovered.
Our human SDRs wouldn’t follow up with return attendees for ticket sales. It wasn’t worth their time—they wanted to hunt six-figure sponsorships instead. We tried incentives. We tried Starbucks cards. We begged them. They said they’d do it, then we’d check the activity logs and discover they lied.
The result? When we deployed AI agents on those exact same leads, they generated 15% of our London ticket revenue. Revenue we literally would not have gotten otherwise.
Same story with our “ghosted” leads—people who reached out wanting to sponsor SaaStr for five and six figures, and our human team just… never responded. Not because they didn’t like the leads. Because every salesperson is force-ranking in their head, putting all their effort into the one big deal closing this quarter.
The AI agent hit those ghosted leads with a 70% open rate.
Here’s the mental model shift: Don’t think of AI SDRs as magic revenue generators. Think of them as the team that finally does the work your humans refuse to do. The small leads. The low-scored leads. The “not worth my time” leads. Those leads deserve better, and AI doesn’t discriminate.
#2. Hyper-Personalization at Scale Actually Works—But “Pretty Good” Is Good Enough
Before AI agents, our human SDRs sent maybe 75-300 personalized emails per rep per month. In six months with AI, we’ve sent nearly 60,000 hyper-personalized emails. That’s 32x the max human output.
But here’s what people get wrong when they see our results: they expect jaw-dropping, month-of-research-level personalization.
That’s not what this is.
On a scale of 1-10, our AI emails are maybe a 3 to a 6 in customization. They’re pretty good. They reference the prospect’s company, what they’ve been looking at, maybe something they posted about. But they’re not poems. They’re not love letters.
And that’s fine. Because the bar isn’t “better than the best human SDR having the best day.” The bar is:
As good or better than your average human SDR, with 24/7 consistency.
A lot of folks on the internet say “I could do better if I hired 30 top-tier Oxford graduates to craft one email each day.” Sure, maybe. But those people want to be promoted to AE in three months. They’re not going to stay. And you can’t hire 30 of them anyway.
Pretty good emails with zero errors, sent consistently at scale, crushes inconsistent brilliance every time.
#3. Train Your Agents Like You’d Train Your Best New Hire
Here’s where almost everyone fails with AI SDRs:
They buy a product, do nothing, and expect millions in revenue.
It didn’t work that way before Claude 4 when these products barely functioned. It didn’t work after Q1 2025 when they started getting good. It doesn’t work now.
The way AI agents work for GTM is:
- You figure out something that works with humans first
- You nail the email, the script, the objections, the questions
- You document what worked
- You give it to the agent and train it for a month
- Then you do it at scale
If you’re expecting an agent to sell when you can’t sell, that’s never worked. Go back to founder-led sales basics. But instead of handing off to that first human hire, you hand off to your first agent hire.
Same principles. Same rigor. Different execution.
#4. Segment Ruthlessly—Never Unleash AI on Your Entire Database
This is critical. Do NOT just point an AI SDR at your entire database and hit send.
Here’s how we approach it:
- Batch contacts into groups of 800-1,000 max for each campaign
- Create sub-agents or sub-campaigns for each persona (CRO, CMO, website visitors, churned customers, etc.)
- Train each sub-agent specifically for that persona and use case
- Give each agent different goals (book a meeting, sell a ticket, follow up on a ghosted lead)
Start with low-stakes segments:
- People you ghosted
- Good inbound you couldn’t fully follow up on
- Post-meeting follow-ups that fell through the cracks
Don’t start with mission-critical leads. You’ll be disappointed if you can’t get it working quickly, and these agents have ramp time.
#5. You Need Exactly Two Humans to Make This Work
This surprised us, but it’s become gospel:
Human #1: A forward-deployed engineer from the vendor.
Call them a solution architect, an FDE, whatever—you need someone from the vendor who will work with you on training and get your agent into production. If the vendor won’t give you this help, don’t buy from them. No matter how slick their sales pitch. A worse product with great implementation support beats a great product you can’t get working.
Human #2: A GTM engineer on your team.
This is the AI nerd. They could come from marketing (technical marketers, HubSpot nerds, anyone who’s built complex campaigns). They could come from RevOps if they’re technical enough. They probably can’t come from your standard sales team.
Find the one GTM nerd on your team. Promote them. Have them own this. They’ll manage the orchestration—which contacts go to which agents, what CTAs, what follow-ups, what happens when leads close.
Self-serve AI SDR products are coming, but we’re not there yet. Even Zendesk’s CEO told me their enterprise customers hit 60-80% automation with months of training, while self-serve gets 20%. Training with no humans isn’t quite ready.
The Tech Stack That’s Actually Working
We run 20+ agents now. More agents than humans. Here’s the core:
- Artisan: ~6% response rate on outbound
- Qualified: ~6% response rate on inbound, 130+ meetings booked since August
- Agentforce: 70% open rate on re-engagement (our newest agent, hitting ghosted leads)
All of them required about two weeks to deploy and tune. All of them required ongoing spot-checking and training refinement. All of them are connected to a single source of truth so we know which agents get which contacts.
On the chat vs. voice vs. video question everyone asks: Don’t overanalyze it. Our data shows about 85% prefer chat, 15% prefer voice. Chat is easiest to implement. Voice takes a bit more work (though we did our voice clone on 11 Labs in five minutes). Video is two orders of magnitude more work.
Start with chat. Layer in voice when ready. Video might add trust for high-ASP sales. Just sequence them and stop debating.
The Top 5 Mistakes We Made (And You Should Avoid)
Mistake #1: We Kept Humans Too Long on Work They Hated
For six years, we tried to get human SDRs to reach out to return attendees about tickets. Incentives, begging, monitoring—nothing worked. They said they’d do it, they didn’t.
The lesson: If your team consistently refuses to do certain work, stop fighting it. Deploy an AI agent on that segment immediately. That 15% of London revenue was found money we’d been leaving on the table for half a decade.
Mistake #2: We Didn’t Read Every Message in the Early Days
When we first deployed, we assumed the AI would just work. We weren’t reading every single message our agents were sending.
The lesson: In the first 30 days of any new agent, read everything. Every email, every chat response, every follow-up. You’ll catch errors, you’ll find training gaps, you’ll understand what’s actually being sent in your name. Only after you’ve built trust should you move to spot-checking and flag-based alerts.
Mistake #3: We Underestimated Ramp Time
We wanted instant results. The products promised quick wins. Reality was different.
The lesson: Budget two weeks minimum to deploy each agent properly. If you get frustrated because “it should work in a day,” you’re setting yourself up for failure. This is training time that pays dividends for months.
Mistake #4: We Almost Bet on People Who Left
GTM turnover was high before AI. It might be even higher now. We saw a CMO at a $50M ARR company who wanted to bake off 10 AI SDRs—and he was gone before implementation finished.
The lesson: Don’t stake your entire AI go-to-market strategy on someone who might leave January 1st. If you’re building agents around someone (especially cloning their voice, training on their style), make damn sure they have a real stake in the company and a real reason to stay.
Mistake #5: We Tried to Evaluate Too Many Vendors at Once
We’ve talked to founders doing bake-offs with 8, 10, even more AI SDR vendors simultaneously. It’s chaos. Nothing gets properly trained. Nothing gets fair evaluation.
The lesson: Pick three vendors max for any bake-off. Better yet, pick one that has a strong customer reference you trust, get the implementation help you need, and commit to making it work. Then expand from there.
The Bottom Line
If you’re still having humans qualify prospects and waiting days for follow-ups in 2026, there’s no excuse. The products are good now. Chat, voice, even video—they all work.
But this isn’t plug-and-play magic. It’s:
- Take what humans have figured out
- Document it
- Train an agent with what works
- Segment ruthlessly
- Have two humans (vendor + internal) own the rollout
- Read everything early, then build trust over time
Even if you grow just 15-20% faster in 2026 because of AI agents, it’s a gift from heaven. Because a lot of that growth is found revenue—the leads that weren’t being touched, the follow-ups that weren’t happening, the work humans just refused to do.
Your leads deserve better. And now there’s no excuse not to give it to them.
All our agent details, vendor breakdowns, and data are available at saastr.ai/agents.
