6 Months of AI SDRs: What’s Worked, How They Brought In $1M+ in 90 Days, and the Real Data Everyone’s Asking For

After deploying 5 AI SDRs across inbound, outbound, and follow-up—here’s the actual numbers, unexpected learnings, and what it really takes to make them work

Six months ago, we had essentially zero AI SDRs at SaaStr.

Today, we’re running five specialized AI agents that have sent nearly 20,000 outbound messages, closed over $1M in revenue, and fundamentally changed how we think about sales development.

The results look incredible on paper: 6.7% outbound response rates (double the industry average), $1M+ closed in 90 days from our inbound agent alone, and 20% of our event ticket sales now coming from AI.

But here’s what nobody tells you about AI SDRs: they require massive human oversight, they can’t fix what’s already broken, and the path to success is often quite different than what some vendors promise.

SaaStr’s Chief AI Officer Amelia Lerutte and CEO Jason Lemkin share the real data, the brutal learnings, and exactly how we got these results.   And want to see the tools we use?  Click here.

The Big Misconception Killing AI SDR Deployments

The myth: Buy an AI SDR for $50-100K, it magically generates leads, you replace human headcount, profit.

The reality: AI SDRs scale what’s already working. They can’t create something from nothing.

This is the #1 reason AI SDR deployments fail. Companies expect magic. They want to spend $20-100K and suddenly have leads pouring in without figuring out what messaging works, what audiences convert, or what their actual sales process should be.

Here’s the truth that took us six months to fully internalize: Your AI SDR can only amplify your best practices. If your outbound didn’t work with humans, AI won’t save it.

Think of AI SDRs as taking your A-tier sales development rep and giving them infinite time, perfect memory, and the ability to personalize at scale. But they still need to know what to say, who to target, and how your sales process works.

We learned this the hard way. Before deploying AI, we had to:

• Identify what outbound messaging actually converted
• Clean up our RevOps processes (they were broken)
• Define clear goals for each agent type
• Create training based on real conversations that worked

Only then could we scale with AI. You can’t skip this step.

Our 5 AI SDRs: The Specialized Approach

Most companies think about “an AI SDR.” We run five, each specialized for different use cases:

Agent #1: Outbound Cold (Artisan)

• Pure cold outreach to new prospects
• Highly personalized based on company signals
• Goal: Book qualified meetings

Agent #2: Lapsed Customer Outreach (Artisan)

• Targets previous sponsors/attendees who haven’t engaged recently
• Leverages past relationship for warmth
• Goal: Re-engage and convert to new events

Agent #3: Active Nurture (Artisan)

• Follows up with people opening emails but not converting
• Tracks engagement signals
• Goal: Move them from awareness to action

Agent #4: Inbound Qualification (Qualified)

• Lives on our website, engages visitors in real-time
• Qualifies intent, books meetings, sells tickets directly
• Goal: Convert inbound interest instantly

Agent #5: Ghosted Lead Recovery (Salesforce Agent Force)

• Follows up with leads our human team dropped
• Leverages full Salesforce history for context
• Goal: Recover lost opportunities

Each agent is trained completely differently. Different messaging, different collateral, different success metrics. This specialization is why they work.

The Outbound AI SDR: Real Numbers After 6 months

Our outbound agents (primarily Artisan) have now sent nearly 20,000 messages. Here’s what actually happened:

Core Performance Metrics:

• 19,847 total messages sent in six months
• 6.7% overall response rate (industry average ~3%)
• 4% positive response rate (significantly above platform benchmarks)
• 3,000 emails per month from AI vs. 75-285 per month from previous human reps
• 10% of London ticket revenue attributed to outbound AI alone

What This Actually Means:

Our AI SDR sends more emails in one month than our best human SDR sent in 40+ months. And it does it with better response rates.

But here’s the critical nuance: These results required massive human input.

On weeks when I spend more time training the agent, reviewing outputs, and feeding it better contact lists—performance jumps noticeably. On weeks when I’m slammed with other work (like preparing for SaaStr London), performance dips.

The AI isn’t truly autonomous. It’s a force multiplier for human expertise.

The 5 Sub-Agents Strategy:

Even within “outbound,” we don’t run one generic agent. We run five specialized versions:

1. Lapsed Sponsors: “We worked together on SaaStr Annual 2023, here’s what’s new…”
2. Current Sponsors: “You’re sponsoring Annual, have you considered London?”
3. Previous Attendees: “You attended last year, early access to this year…”
4. Engaged Non-Converters: “You’ve opened our last 5 emails about speaking…”
5. Pure Cold: “You’re building [specific product], here’s why SaaStr…”

Each has different training, different tone, different proof points. The specialization matters enormously.

The Unexpected Learning: Direct Selling

We initially deployed our outbound AI to book meetings for sponsorships. That worked fine.

But then something unexpected happened: For lower-priced products (event tickets under $1,000), the AI got really good at closing deals directly.

At first, I was nervous. “Can I trust the AI to sell without human review?” Six months in, the answer is yes. For sub-$1K tickets, it closes deals on its own. I let it run free now.

For higher ASP deals (sponsorships $50-100K+), it still books meetings and qualifies, then hands to humans. But the direct-selling capability on lower-ticket items has been game-changing.

The Deliverability Secret:

One critical learning: Artisan forces a 2-3 week warm-up period before sending at volume. This annoyed me initially. “Why am I waiting 2-3 weeks? Just let me send!”

Now I understand. Our emails hit primary inbox, not promotions tabs. Our deliverability is essentially perfect. I can’t even achieve this level with Marketo.

Skip the warm-up at your peril. Deliverability is everything in outbound. The best message in the world doesn’t matter if it hits spam.

How We Feed the Beast:

The #1 operational challenge: Constantly feeding the AI fresh, quality contacts.

I started uploading contact lists once per week. Now I do it twice per week when possible because the AI performs better with fresh inputs.

About 90% of contacts are ours—from our database, not scraped from Apollo or other intent data providers. We trust our data quality, and that trust shows in results.

I upload via CSV in batches of 800-1,000 contacts. This size seems to be the sweet spot for performance in Artisan specifically.

The Inbound AI SDR: The $1M Surprise

We added our inbound AI SDR (Qualified) in August—three months after starting outbound. This agent has produced the most surprising results of any deployment.

The Numbers (August-November, 3.5 months):

• 697,000+ sessions with website visitors
• 1,000+ meaningful conversations (vs. simple questions)
• 100+ meetings booked (approaching 100 as of November)
• $1M+ in closed revenue in just 90 days
• $2.5M+ in pipeline attributed to agent-booked meetings
• 70% of October’s closed-won deals came from this AI agent

Read that last stat again: In October, 70% of our closed revenue came through our AI SDR.

Why This Agent Crushes It:

The inbound AI SDR isn’t just booking meetings faster (though it does—instantly vs. up to 24 hours delay previously). It’s completely transforming the quality of those meetings.

Here’s what changed:

Before AI (The Old Way):

1. Prospect fills out contact form
2. Goes into queue for me to round-robin to rep (delay: minutes to hours)
3. Rep gets assignment, responds (delay: hours to 24 hours)
4. Back-and-forth to find meeting time (delay: days)
5. Meeting finally happens, first 10 minutes wasted on basic discovery
6. Deal cycle starts

Total time to meeting: 1-3 days Discovery needed: 10+ minutes Context provided: Minimal

After AI (The New Way):

1. Prospect visits website, AI engages instantly
2. AI qualifies, understands needs, books meeting—all in real-time
3. AI provides complete dossier to sales team before meeting

Total time to meeting: Seconds to minutes Discovery needed: Zero Context provided: Everything

The Context That Changes Everything:

Before each meeting, we now know:

• Complete conversation history (what they asked the AI, what they cared about)
• Every page they visited on our website
• How many times they’ve engaged over what timeframe
• Other people from their company who visited (CEO browsing speaking opportunities while CMO books meeting)
• Specific content they consumed (sponsorship packages, speaker guidelines, ticket options)

We don’t do discovery calls anymore. The AI did discovery. We jump straight to solution discussions.

Real Example:

Prospect books a sponsorship call. Before the meeting, the AI tells us: “Their CEO was also on the site yesterday looking at speaking opportunities, even though this person only asked about basic sponsorship.”

We mention this in the call: “Hey, we noticed your CEO was also checking out speaking. Should we look at a package that includes a speaking slot?”

Prospect: “Oh wow, I didn’t know they were looking at that. Yes, let’s include speaking.”

Instant upsell. This happens constantly now.

The Training That Makes It Work:

Most companies deploy Qualified with two buttons: “Talk to Support” or “Talk to Sales.” That’s it. Their agents are mediocre.

Our AI ingests everything:

• 20 million words across SaaStr.com, SaaStr.ai, London, Annual sites
• Our entire YouTube channel
• Recorded sales calls I upload
• Sponsor meetings transcripts
• Custom documentation and FAQs
• Historical email threads

The agent is empowered to:

• Sell event tickets directly (up to $1,000)
• Offer discount codes
• Follow up if codes aren’t used
• Book meetings for sponsorships
• Route to support when appropriate
• Remember returning visitors and full context

The Discount Code Workflow:

This was an unexpected use case that emerged from the data.

Week one with the inbound AI, I noticed the #1 question was: “Can I get a discount on tickets?”

I empowered the agent to give discounts and sell directly. Here’s what happens now:

1. Prospect asks for discount
2. AI: “Absolutely! Here’s code LONDON25 for 15% off. You can use it at checkout.”
3. Prospect leaves without buying
4. Day 3: AI follows up: “Hey Jason, I gave you code LONDON25 when we chatted. I noticed you haven’t used it yet. Still interested in attending?”
5. Prospect converts

This follow-up conversion happens at scale. The AI remembers every interaction, knows who didn’t convert, and follows up systematically.

Result: 20% of our London ticket revenue comes from AI agents (both inbound and outbound combined).

I physically could not do this level of personalized follow-up at scale. The AI does it effortlessly for thousands of prospects.

The Follow-Up AI SDR: Recovering Ghosted Leads

Our most recent deployment (October) was Salesforce Agent Force for a use case I’m embarrassed to admit we needed: following up with 1,000 leads our human team completely ghosted.

The Embarrassing Reality:

After SaaStr Annual, I audited our Salesforce. I found ~1,000 people who:

• Filled out our “I’m interested in sponsoring” form
• Were automatically routed to a sales rep
• Never received any human follow-up whatsoever

This is common. It’s also inexcusable. These were warm, inbound, high-intent leads. We just… forgot about them.

The Agent Force Solution:

These people were already in Salesforce with full interaction history. Perfect use case for Agent Force because it knows everything Salesforce knows.

Early Results (One Month Live):

• 72% open rate (unheard of in Marketo or cold email)
• Higher response rate than our other agents
• Still working through the initial 1,000 at a controlled pace

Why such high open rates? Because Agent Force personalizes based on complete Salesforce history:

• Past event attendance
• Previous sponsorship levels
• Interactions with our team
• Account tier and company information
• Engagement patterns

The emails don’t feel like “recovered leads” outreach. They feel like natural continuation of relationship.

Sample Email:

Hi Kyle,

I noticed you reached out after SaaStr Annual about sponsorship opportunities, but somehow we never connected (entirely my fault!).

I see you attended Annual 2022 and 2023—thanks for being such a consistent supporter. Based on your company’s growth since then, I think our London event in March might be perfect timing.

Would you be open to a quick call about 2025 opportunities? Here’s my calendar: [link]

Best, Amelia (via AI)

Simple, personal, acknowledges the gap, moves forward. Response rate is significantly higher than cold outreach.

The Setup Reality:

People think Agent Force is too technical. “You need a Salesforce admin.” “It’s for enterprises only.” “Setup takes months.”

I’m not a Salesforce admin. I’m not certified. I was better at Marketo than Salesforce before this project.

With Salesforce’s team help during onboarding, we got it working in days, not months. The key: I copied our Artisan training instructions, adapted them for “ghosted lead recovery,” and it worked immediately.

What It Actually Takes: The Human Commitment

Here’s the part most AI SDR vendors gloss over: These agents consume the majority of both mine and Jason’s time.

Could we run more agents? Yes. Would they fail without our oversight? Absolutely.

The Weekly Time Commitment:

For me personally, across all five AI SDRs:

Outbound Agents (Artisan):

• 3-4 hours weekly uploading and preparing contact lists
• 2-3 hours reviewing performance, adjusting training
• 1-2 hours reviewing draft responses for high-value prospects
• 30-60 minutes monitoring responses and routing to humans

Inbound Agent (Qualified):

• 1-2 hours weekly reviewing conversations, identifying gaps
• 1 hour uploading new training materials (calls, docs, FAQs)
• 30 minutes spot-checking responses
• As-needed monitoring when agent raises hand for help

Follow-Up Agent (Agent Force):

• 1-2 hours weekly preparing and uploading contact segments
• 1 hour reviewing performance and adjusting targeting
• 30 minutes monitoring send patterns

Total: 15-20 hours per week actively managing five AI SDRs.

This is why we can’t just infinitely add more agents. The human oversight is real and necessary.

Performance Ebbs and Flows with Human Input:

We have clear data on this now: Agent performance directly correlates with human attention.

Weeks I spend more time:

• Response rates increase 10-20%
• More meetings booked
• Higher quality conversations
• Better revenue outcomes

Weeks I’m slammed with other work:

• Agents still run (that’s the beauty)
• But response rates dip
• Fewer meetings convert
• Revenue impact decreases

They don’t fail catastrophically without me. They just perform at B+ level instead of A+ level.

The Training Never Stops:

Every week, I’m:

• Adding new proof points that worked in human conversations
• Removing messaging that got negative feedback
• Updating targeting based on what’s converting
• Adding new use cases and capabilities
• Refining objection handling based on real responses

This isn’t set-and-forget technology. It’s like having five SDRs who need constant coaching—except they never complain, never quit, and work 24/7 once trained.

The Specialized vs. Generalist Debate

A common question: “Should I get one all-in-one AI SDR tool or multiple specialized ones?”

Our philosophy: Specialized over all-in-one.

Yes, it’s more work. Yes, I have to manage separate tools and avoid contact overlap manually. Yes, it’s sometimes annoying.

But specialized tools go deeper. Artisan is maniacally focused on outbound results. Qualified is obsessed with inbound conversion. Agent Force leverages Salesforce data better than any third party could.

The Tradeoff:

All-in-one platforms promise simplicity. “One platform for all your AI SDR needs!”

In practice, we’ve found that platforms trying to do everything do each thing at B+ level. Specialized platforms do one thing at A+ level.

For our use cases and our scale, we’ll take three A+ tools over one B+ tool. But this requires accepting operational complexity.

The Contact Overlap Problem:

One major challenge with multiple agents: Making sure the same person doesn’t get hit by three different AI SDRs.

Right now, this is mostly manual. I carefully segment:

• These contacts go to Artisan
• These contacts go to Qualified
• These contacts go to Agent Force

Each platform has de-duping within itself. But across platforms, I’m the de-duping layer.

This is improving. Artisan just added the ability to exclude specific Salesforce campaigns. Qualified syncs natively with Salesforce. They’re moving toward better interoperability.

But today, if you run multiple specialized AI SDRs, expect manual coordination work.

The Budget Reality: What It Actually Costs

Real talk: Effective AI SDRs cost $50-100K+ per platform annually.

Breakdown typically looks like:

• $60-70K annual subscription
• $20-30K training and onboarding
• Or ~$100K all-in depending on vendor

Some vendors are launching cheaper self-serve versions. We’ll test these in 2025. Initial hypothesis: They’ll work reasonably well for simple use cases but won’t match enterprise power because they ingest less data and require less customization.

Think of it like Zendesk support agents. The $299/month version works but only has 20% of the enterprise capability because it ingests your wiki vs. a decade of customer interactions and call transcripts.

How We Funded This:

We didn’t get new budget. We reallocated existing budget.

Specifically: When SDRs left naturally after our Annual event, instead of replacing that headcount, we invested in AI SDR platforms.

We replaced the budget for two human SDRs with our AI SDR stack. The AI sends 10x more messages with better response rates, so the math works.

The ROI Calculation:

Is $100K for an AI SDR worth it?

Let’s do the math on our inbound agent:

• $100K annual cost (rough estimate)
• $1M closed revenue in 90 days
• 10X ROI in one quarter

Even if you cut those results in half—even if they’re 75% lower—the ROI is clear.

But you have to commit to making it work. You can’t deploy and forget.

The Vendor Selection Framework

Critical advice: Don’t tolerate mediocre sales reps in the AI age.

The best AI companies have shockingly bad sales teams that don’t understand their own products. Sales reps will tell you things that are flat wrong about capabilities, training requirements, or integration.

What to Demand:

1. Talk to the actual implementation specialist or technical person before signing—not just the sales rep
2. They should assess your data and confirm success viability in 20 minutes: “Yes, you have enough data for this to work” or “No, you need X first”
3. Ask them how many deployments they’ve done personally and what success rate looks like
4. If a sales rep blocks access to technical experts, walk away immediately

We passed on one excellent AI SDR vendor because their sales rep was incompetent, didn’t understand the product, and created barriers to talking with technical teams.

That rep cost their company all the PR, revenue, and referrals we would have driven. Don’t reward bad sales behavior with your budget.

The Deployment Partnership:

Every vendor we use provided hands-on setup help:

• Artisan connected us with their implementation specialist
• Qualified did the same
• Salesforce/Agent Force provided onboarding resources

This is standard and necessary. The vendor should be invested in your success and provide technical resources to ensure it.

You’re not figuring this out alone. Good vendors know this and staff accordingly.

The “Too Much Demand” Problem:

Interesting dynamic: All these vendors have more demand than they can handle.

Some turn away business even when you have budget. Main reasons:

• Not enough data to train effectively
• Use case doesn’t fit their platform well
• They’re at capacity and prioritizing customers most likely to succeed

This is actually good. It means they care about success rates more than just revenue. But it can be frustrating if you’re turned away.

If a vendor says they can’t support you, ask why and listen. They’re usually right about whether you’re ready.

What Actually Works: The Implementation Playbook

After six months and five AI SDR deployments, here’s the playbook that works:

Step 1: Identify What’s Already Working

Don’t deploy AI to fix broken processes. Deploy it to scale working processes.

• What messaging gets responses from humans today?
• What audiences convert at acceptable rates?
• What does your best SDR do that works?
• What sales process actually closes deals?

Document this. This becomes your AI training foundation.

Step 2: Start With One Agent, One Use Case

Don’t try to deploy across inbound, outbound, and follow-up simultaneously. Pick one:

• Pure outbound if you have contact lists and proven messaging
• Inbound if you have website traffic and can define qualification
• Follow-up if you have a database of unconverted leads

Get one working phenomenally before adding a second.

Step 3: Choose 1-2 Vendors Maximum

Do a bake-off if needed, but limit it to two vendors you’ll properly train and compare.

We talked to a CMO doing 10 simultaneous vendor trials. That’s insane. You won’t train any of them properly. The bake-off will fail and you’ll conclude “AI doesn’t work.”

Two vendors maximum. Train them properly. Make an informed decision.

Step 4: Take Your Best Person and Learn Together

Don’t hire a “Chief AI Officer” initially. Don’t delegate to someone who doesn’t understand the work.

Take your best SDR, sit down together, and figure out AI together. Learn by doing.

Eventually, that person’s role will evolve to focus more on AI operations. But start as partners.

Step 5: Commit to 90 Days of Daily Management

Plan for this to consume significant time for three months:

• Daily monitoring of outputs
• Weekly training updates
• Constant refinement of messaging and targeting
• Regular review of conversations and responses

This is not set-and-forget. It’s coaching five SDRs simultaneously.

Step 6: Empower Gradually

Start with AI in draft mode:

• It suggests messages, you approve and send
• You review every interaction
• You correct and train constantly

After 30-60 days of this, start empowering:

• Let it send to certain segments without approval
• Let it handle objections independently
• Let it close small deals directly

We’re six months in and still have some agents in draft mode for high-value prospects while others run autonomously for lower-stakes interactions.

Step 7: Scale What Works

Once you have one agent crushing it, add a second with a different use case.

We went:

1. Outbound first (May)
2. Inbound second (August)
3. Follow-up third (October)
4. Now adding more use cases within existing platforms

Each one took 60-90 days to reach peak performance. Don’t rush this.

The Mistakes That Kill AI SDR Deployments

After watching dozens of companies try and fail with AI SDRs, here are the fatal mistakes:

Mistake #1: Expecting Magic Without Work

“I’ll buy this AI SDR, it’ll generate leads, I’ll make money.”

No. You’ll buy this AI SDR, spend 20 hours per week training it, constantly refine it, and then it’ll generate leads.

The companies succeeding with AI SDRs are putting in massive human effort. The companies failing expected automation without investment.

Mistake #2: Deploying to Fix What’s Broken

If your outbound doesn’t work with humans, AI won’t fix it.

If your messaging is off, your ICP is wrong, your offer is weak—AI will just scale your failure.

Fix the fundamentals first. Then scale with AI.

Mistake #3: Generic Training

“Here’s our website, here are some email templates, go!”

That produces mediocre results.

Winning training:

• Specific proof points from real conversations that worked
• Objection handling based on actual objections you’ve received
• Clear escalation rules for when to loop in humans
• Detailed ICP definition with examples and non-examples
• Response frameworks that match your brand voice exactly

Mistake #4: Set and Forget

“I deployed it three months ago and it’s not working.”

When did you last update the training? What have you refined based on results? How often do you review conversations?

“Uh… I deployed it and haven’t touched it since.”

That’s why it’s not working.

Mistake #5: Ignoring the Vendor’s Expertise

Every vendor knows things about their platform you don’t. They’ve seen hundreds of deployments.

When they say “You need a 2-3 week warm-up period,” don’t ignore it. When they say “This feature won’t work for your use case,” believe them. When they suggest a specific training approach, try it their way first.

You can innovate later. Start by following their proven playbook.

The Future: What’s Coming Next

Agent-to-agent communication is the next frontier.

Right now, our five AI SDRs don’t talk to each other. I manually prevent overlap. This is improving but still mostly manual.

Within 6-12 months, I expect platforms will communicate better:

• “This prospect is already in an Artisan sequence, don’t add to Qualified outreach”
• “This person just had a positive inbound conversation, suppress outbound”
• “This account is in active deal cycle, route all touches to assigned AE”

The infrastructure for this exists. The integrations are coming.

Voice and video agents are next.

We’re filming with Qualified to turn our chat agent into a full video/voice agent. It’ll have my voice, my face, and conduct two-way conversations.

This should roll out by SaaStr London in March. Come see it in person.

Lower-priced self-serve versions from every vendor.

The $100K enterprise version will stay for complex use cases. But $299-999/month self-serve versions are launching across the board.

We’ll test these in 2025 and report back. They won’t match enterprise capability but might be good enough for smaller teams or simpler use cases.

The consolidation question.

Will we eventually move to one platform that does everything? Maybe, if one gets good enough at everything.

Right now, specialized wins. But I could see a world where one platform nails inbound, outbound, and follow-up at A+ level and we consolidate.

That’s probably 12-24 months away.

The Honest Assessment: Is It Worth It?

Unequivocally yes, but only if you commit.

Our results after six months:

• 20K+ messages sent vs. <2K from humans in same period
• $1M+ closed revenue from inbound agent in 90 days
• 10X scale on activities that were already working
• Better conversion rates than human-only in many cases
• 20% of ticket revenue from AI agents

But this required:

• 15-20 hours weekly from me managing the agents
• Deep commitment to training and iteration
• Willingness to trust agents with real revenue operations
• Tolerance for failure and public criticism
• Significant budget reallocation ($200-300K+ across platforms)
• Six months of continuous learning and improvement

The magic isn’t that AI SDRs work without effort. The magic is that once you invest the effort to train them properly, they scale your best practices infinitely.

The ROI is clear if you do the work. Our inbound agent alone generated 10X ROI in one quarter. Even cutting that in half or by 75%, the math works overwhelmingly.

But there’s no shortcut. You can’t buy an AI SDR and expect it to magically work. You have to train it like you’d train your best human SDR—except this one never sleeps, never forgets, and gets better every day.

Your Next Steps

If you’re considering AI SDRs:

Week 1: Assess readiness

• Do you have something working that needs scale? (If no, stop here)
• Do you have data to train on? (6+ months minimum)
• Can you commit 10-20 hours weekly for 90 days? (If no, wait)
• Do you have $50-100K budget? (If no, wait for self-serve versions)

Week 2-3: Vendor selection

• Narrow to 1-2 vendors based on your primary use case
• Talk to technical teams, not just sales reps
• Get them to assess your data and confirm viability
• Check references from similar companies

Week 4: Start with one use case

• Outbound if you have proven messaging and contact lists
• Inbound if you have traffic and clear qualification criteria
• Follow-up if you have unconverted lead database

Months 2-3: Train and iterate daily

• Review every conversation initially
• Refine training based on real results
• Add proof points from what works
• Remove messaging that fails

Month 4: Start empowering

• Let it run autonomously for low-stakes interactions
• Keep human oversight for high-value prospects
• Measure results vs. benchmarks

Month 5-6: Scale or add second use case

• If first agent is crushing it, add a second
• If first agent is struggling, go deeper before expanding
• Never deploy more agents than you can actively manage

And remember: AI SDRs scale what works. They don’t fix what’s broken.

Get your fundamentals right first. Then let AI take you to the moon.

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We’ll be covering our RevOps, customer success, and marketing AI deployments in Part 2 next week. You can see all our tools and specific use cases at saastr.ai/agents.

Or come see it all in action at SaaStr London on December 1-2, where you can interact with our AI agents live and see exactly how we’ve built this.