The Official SaaStr Podcast: SaaS | Founders | Investors

There’s a growing wave of AI agent skepticism on LinkedIn right now. And some of it is earned. A lot of founders bought an AI SDR, didn’t train it, and got garbage results. Then they posted about how “AI agents don’t work.”

But here’s what we know after 8 months of running 20+ agents across our entire go-to-market at SaaStr — with just 3 humans and a dog: $4.8 million in additional pipeline sourced by agents. $2.4 million in closed-won revenue. Deal volume more than doubled. Win rates nearly doubled. And none of it cannibalized our existing inbound.

It works. But not the way most people think it does.

Let me break down what we’ve actually learned — the real stuff you won’t see in the LinkedIn posts.

The Results Are Real, But So Is the Work

Let me give you the honest numbers first.

Eight months in, our AI agents have generated $4.8M in additional pipeline and $2.4M in closed-won revenue that was first-touch sourced from an agent. Our deal volume has more than doubled. Our win rates have nearly doubled. And we’ve sent over 60,000 high-quality AI-generated emails just on the sales side — not even counting the nearly 1 million interactions through our vibe-coded apps.

Here’s what matters most about those numbers: this was all additive. It did not cannibalize our other inbound revenue sources. We didn’t drop anything when we deployed these agents. We still send marketing emails. We still do outbound ourselves. We still send gifts. We still invite people to the SaaStr house. All the things we used to do before — we still do them. The agents augmented everything.

But here’s the honest truth you won’t see on LinkedIn or X: we maintain these agents every single day. Literally every morning before anything else, we’re checking our agents. Amelia and I each spend 15-20 hours per week — that’s each, not combined — actively managing, iterating, checking responses, making sure nothing hallucinates, making sure the agents are talking to people the way we want them to.

The time we used to spend managing humans on our team? We now spend that same amount of time — if not more — managing agents. The difference is there’s no people drama, and the agents work at a much higher capacity and scale than a human ever could.

At some point, you realize you simply cannot keep up with your agents. They’re faster than you. They work 24/7/365. They can always answer a question, always book a meeting, always reach back out. The humans become the bottleneck.

The Secret Nobody Tells You: Agents That Require Deep Training Cannot Be Self-Trained

I was meeting recently with the CEO of a next-generation AI go-to-market company — they already have millions in revenue and are publicly launching soon. I asked what their secret sauce was.

The answer: they do everything. The onboarding, the tagging, the first campaigns — all of it. They do it almost to a fault. Some customers think it’s too easy and don’t even realize how much human energy is going into deployment behind the scenes.

That’s the learning. If you haven’t deployed many agents — or any for real — you need to have an honest conversation. Not with someone in sales who doesn’t know how the product works. Talk to a forward-deployed engineer. Talk to a leader. Find out what it’s actually going to take in the first 14 days, the first 30 days, and every single day after that.

Then you have to actually do it. Otherwise, it’s like going to the doctor, getting a prescription, and never taking the medicine. It literally will not work.

A lot of the agents we use are pushing downmarket to be more self-service. So far, that doesn’t work. Agents that require deep training cannot be self-trained yet. It will come — agents are getting dramatically better every quarter. But for now, be skeptical. If you buy a cheap tool that claims it’s self-trained, make sure it actually works. If you buy a more complex tool, talk to someone senior enough on deployment who actually knows the product.

The 90/10 Rule: Buy 90%, Build Only 10%

Here’s our rule of thumb: buy 90% of your AI stack. Only build the 10% where no vendor can do it well and it’s a P1 priority.

We’ve followed this ourselves. The vast majority of our agents are third-party tools that we’ve trained and customized heavily. We only built custom agents where we had a very specific use case that no vendor could handle — like our AI VP of Marketing (more on that below).

Kyle, the CRO at Owner (one of our portfolio companies), has followed roughly the same approach. He bought a bunch of third-party agents, made them work, and then hired a former founder/engineer — someone who was literally a CEO of an LLM company — to build a proprietary in-house tool for the 10% that needed to be custom.

That’s an extreme case. For most of you, building custom agents probably won’t make sense yet. Focus on making the bought tools work first.

How to Evaluate AI Agent Vendors (Don’t Skip the Basics)

I don’t know why people throw away basic evaluation practices just because something has “AI” in the name. Here’s what we do — and what you should do:

Ask for a dedicated resource. I asked every single AI tool we now deploy for help. I told them: one, I’m going to need an FDE (forward-deployed engineer), and two, let me talk to people who have actually used this.

Ask for customer references. I see too many people skip this because “it’s an AI tool.” Ask for a reference. Ask for one in your vertical if possible. If they push back, maybe don’t use that vendor. Most of these companies have at least one customer that’s somewhat like you.

Demand FDE time at deployment. Salesforce put an FDE on our success team. Qualified has an FDE on our success team. Replit has an FDE on our success team. With Artisan, anytime I have an issue or idea, I go straight to the CEO or head of product. You should ask for — and expect — some level of dedicated support to get started. You may not need it weekly later, but you absolutely need it upfront.

Trust your gut. If it doesn’t feel right, don’t buy it. If your Spidey sense says this agent isn’t going to work, it won’t work. Buy another one. Even if the brand is less well-known, even if it’s scrappier — if it feels right and the team is proud to show you their product, go with that one.

One more thing on evaluation: the best agents should take you as far down the journey as possible before you have to pay. A lot of AI go-to-market tools can’t practically offer free deployments due to the human onboarding costs. But the best ones get you as close to production as they can. Marc Benioff said on 20VC that he wished Salesforce had enough FDEs to get every customer into production on Agentforce before they had to pay. It’s not practical, but that’s the right instinct.

Multi-Agent Management Is Messy (And That’s Okay for Now)

Here’s what people always ask me: “You have 20 agents — what are you using as your MCP?”

The honest answer: we don’t have one. Not a real one, anyway.

What we have is what I’d call “MCP light” — a combination of Zapier webhooks, Salesforce as our system of record, and a lot of manual context-sharing between agents. We have so many webhooks firing into our Zapier account I can’t even count them. All of our third-party tools push data back to Salesforce, either through native integrations, APIs, or Zaps.

Sometimes I just copy-paste context from one agent and put it into another. It’s not elegant. It’s not clean. But it works.

This is the reality of multi-agent management in early 2026. It’s a lot of hodgepodging things together. But I think this is a “right now” problem, not a “forever” problem. By the second half of 2026, I believe native integrations will solve most of this.

My advice: Pick one source of truth for your data (Salesforce, HubSpot, whatever) and push everything back to it. Get used to your agents talking to each other — it happens, and it’s fine. Get used to talking to your agents yourself. And for now, get comfortable with some manual context-sharing between systems.

If you use specialized tools like we do (versus an all-in-one agent builder), you’ll deal with more of this messiness. We prefer specialized tools because the output quality is better. But you might reasonably trade some quality for quality-of-life by using an all-in-one platform. Both approaches are valid.

A Practical Go-To-Market Flow You Can Copy

Here’s an actual Zapier flow we run that you could adapt:

* Catch a webhook from a website form submission

* Push to Google Sheets (yes, I keep backups of everything in Sheets — don’t judge)

* Create/update a contact in Salesforce

* Add the contact to a Salesforce campaign (which can trigger Agentforce if you want)

* Find account-level records in Salesforce to see what this company has done with you before

* Enrich with Clay — pull LinkedIn activity, summarize context

* Send a Slack notification with all the context: contact info, account history, Clay enrichment

* Optionally generate a Gamma presentation or landing page customized for this prospect

* Draft a Gmail or push to your AI SDR platform

That’s nine steps, touching six or seven different tools. It’s not simple. But it gives you a fully enriched, context-rich go-to-market motion that would be impossible to do manually at scale.

The AI SDR Playbook: Hyper-Segmentation Is Everything

If you’re rolling out your first AI SDR, here’s the single most important thing I’ve learned: hyper-segment everything.

I see people running one campaign for 10,000 leads. That’s insane. I max each campaign at 100-500 contacts. Every campaign, every sub-agent gets highly customized training for the exact segment it’s targeting.

Don’t segment the old-school way — by geography, title, or role. None of those exist in my outbound funnel. Instead, segment by context and intent:

* Website visitors (deanonymized)

* Inbound leads who filled out a form

* Abandoned trials or carts

* Event leads from webinars or conferences

* Former customers who changed jobs

* Current customers for expansion

* Recent marketing leads from gated content

* Leads you never followed up with (we famously gave these to Agentforce)

* Lapsed contacts you forgot to talk to

* Low-scoring leads that still show intent but nobody wants to call back

* Alumni from previous events

* Warm referrals and community members

I haven’t exhausted this list after 8 months. Start here, not with cold outbound. Your AI agent has zero context for why it should reach out to a random cold lead. But it has rich context for every segment above.

The more context you give your agent, the better the output. This is no different than how you use Claude or ChatGPT every day — you talk to it, give it context, tell it about your business. Same rules apply with AI SDR agents.

And tell your agent what you can’t do. This is a nuance I only learned after months of deployment. AI agents are self-gratifying — they try to beat their own metrics. Over time, they start making promises you can’t keep. Ours started offering people speaking slots at SaaStr Annual, which isn’t how our speaker selection works at all. Once I explicitly told the agent what we don’t do, the quality jumped significantly.

Bad context equals bad emails. Period. Train on the best of everything — your best email copy, your best follow-up sequences, your best case studies — and then also define the boundaries clearly.

Why We Built Our Own AI VP of Marketing

We looked at every third-party AI marketing tool on the market. None of them could do more than content. The real problem we needed solved was orchestration — coordinating data across agents, prioritizing initiatives, and creating executable daily plans grounded in actual performance data.

We also knew from experience that every time we tried to onboard a human with all our data, they got overwhelmed. So we built our own AI VP of Marketing. We nicknamed it “10K” — because the goals were 10,000 attendees at SaaStr Annual and $10M in revenue for the year.

Here’s how we built it:

* Collected data from our agents, third-party tools, Salesforce, Zapier workflows, and internal historical data

* Fed it into Claude Opus with very clear goals and context (this required upgrading to Max — Pro ran out of memory over a weekend of heavy analysis)

* Pushed the output to Replit so the whole team could access it as a web app

* Told it to generate both high-level strategy and granular daily executable tasks

The key instruction: “Don’t give me generic strategy ideas. Give me executable tasks grounded in our data that three humans and a dog can actually do.”

What came back was remarkable. A week-by-week game plan with specific campaigns, specific timing, specific channel allocation. It told us how much to spend on LinkedIn ads and what the ads should say. It told us which campaigns to run through Artisan vs. Qualified vs. Agentforce. It literally told me what to post on social media.

Some stuff it said to blow up or abandon. Some stuff it said to bring back. And a bunch was net-new. Because it was all rooted in data, I trust it.

I talk to 10K every day. “Where are we? What should we be doing today? Where are we falling behind?” It keeps me honest and focused. Sometimes I push back — it once suggested a campaign I didn’t think was compelling enough. We debated it, it looked at the data, and agreed to change course.

It’s not always right. But it’s not always wrong either. And it keeps me more organized in this one vector than I’ve ever been.

The Maturity Curve Is Real

Here’s the honest assessment of where AI tools stand today:

Coding and support tools → Most mature. These work well today.

Sales tools (AI SDRs, conversational agents) → Getting there. Work well with significant training and maintenance.

Marketing tools → Not nearly as mature as vendors claim. That’s why we had to build our own AI VP of Marketing. Most marketing AI tools only handle content. Orchestration, campaign planning, cross-channel coordination — none of that exists in a turnkey product yet.

By the second half of 2026, I believe all of this will connect natively. Your AI VP of Marketing will integrate directly with your AI SDRs, your Agentforce, your Clay tables, your LinkedIn ads — all of it. There won’t be any excuses for shooting from the hip in B2B marketing anymore.

But we’re not there yet. For now, it’s messy, it’s manual in places, and it takes real human effort every single day.

The Bottom Line

If I had to distill everything we’ve learned into one piece of advice: find something in your go-to-market motion that just isn’t getting done, or is getting done at a mediocre level, and put an agent on it.

Don’t try to replace what’s already working well. Do that as your 10th or 20th agent. Start with the low-hanging fruit — the customers that are too small for your team to call back, the leads that take too long to respond, the contacts with lower scores that still have intent but nobody wants to prioritize.

Because even modest yield from those segments is magical. It’s entirely additive revenue that you never would have captured otherwise.

The emails our agents send aren’t the best ever written. I’d say they’re “pretty good.” But we’re getting scale — 60,000 high-quality, personalized emails that we could never have done manually. More high-quality, pretty-good interactions with more people, more often. That’s the formula.

And that’s worth $4.8 million in pipeline and counting.

This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit cloud.substack.com

If Growth Isn’t Accelerating, You’re Not an AI Company. And 9 Other Hard Truths for SaaS in 2026.

I had a great conversation with the TBPN crew the other day, and we covered a lot of ground — from the state of the SaaS market to PE exits to vibe coding to how agents are already reshaping how software gets bought and sold. I wanted to pull together the key themes here, because I think founders need to hear some of this, even if it’s uncomfortable.

Let me walk through the big takeaways.

1. If Growth Isn’t Re-Accelerating, You’re Not Really an AI Company

This is my simple rule, and I think it cuts through all the noise.

Every public company, every startup, everyone is talking about their “AI strategy.” They’ve built an agent. They’ve shipped a copilot. They’ve got an AI tab on their website. Great. But has growth actually re-accelerated?

That’s the bull case for Meta. They genuinely accelerated growth. The AI they baked into their ad-matching platform is working. Retail advertisers generating ads with AI tools — it’s all compounding. It’s real.

Now look at the flip side. Microsoft’s AI business is still blowing up, but they missed on the software side. The Trade Desk has been destroyed. Figma is trading below 10x revenue despite essentially creating and owning a category.

The point is: AI talk is cheap. Revenue acceleration is the only metric that matters now. And I’ve lost patience — with founders at $1M, at $10M, with public companies — who haven’t seen the lift. ElevenLabs just crossed $350M. MongoDB dramatically re-accelerated. Show me the money.

2. The Transition from “Deeply Tough Love” to Just “Tough”

Last year, my advice to founders was deeply tough love. The market was brutal, and most people hadn’t adjusted.

Now it’s just tough. Here’s why: you’ve had time. Claude got really good at 3.7 — that’s why Replit and Lovable blew up. That was a year ago. Whether you’re Agentforce or one of my portfolio companies, you had a full year to re-accelerate growth. Some did. Most didn’t.

And honestly? Salesforce is doing better than some startups I work with. We’re actually probably the only organization of our size using Agentforce for real, every day. It works. I can’t tell you how many startups whose “agentic product” is still basically a copilot.

3. 80% of Your Team Wants to Work Like It’s 2021

There’s a narrative that AI has reinvigorated SaaS founders — that people who got to growth stage a decade ago are suddenly back in the arena, fired up, tinkering with tools, pushing teams harder.

It’s a great narrative. In the real world, it’s not that common.

I talk to public company CEOs in B2B, my own portfolio, others — a lot. Behind the scenes, off the record. And since our agents blew up, everybody thinks we’re some kind of GTM agent gurus. So they come to us.

The consistent theme: 80% of their team wants to work like it’s 2021. Everyone has to create a skunkworks team or something. And meanwhile, the AI-native companies are crushing it precisely because they don’t have to deal with 20,000 pre-AI customers who still have feature gaps, clunky software, and legacy competitors. Then you’ve got 10,000 new AI competitors who don’t carry any of that baggage.

I’d love to tell you I know dozens of companies that went from 40% growth at the start of 2025 to 80% or 110%. I can think of a handful.

4. The Vibe Coding Problem Is Worse Than You Think

Six months ago, the problem was 10 companies running at every exciting category. Today it’s worse.

A very successful seed investor — relatively new to the game — told me yesterday he’s giving up because everyone can vibe code something. He can’t even tell the difference between founders anymore.

Now look, I’ve vibe coded 20+ apps that have been used over a million times. I’m in the top 0.1% on Replit. I know a bit about this. You’re not going to vibe code Salesforce. But you can vibe code something for Demo Day that looks really good. Stuff that 18 months ago would have made your jaw drop — “oh my god, an agent for dental follow-ups that’s fully automated!” — that’s now table stakes. I built my own version on Replit a week ago. It was already obsolete today.

Here’s the real unlock though: if you want to build super-niche software for real — not for an hour, not one-shot, but actually commit to it — you can now do it without an engineer. RevenueCat, a company I was the first investor in, powers mobile subscriptions for 50% of mobile apps. Andreessen published data showing the number of new mobile apps basically quintupled at the end of last year because of vibe coding. And it’s just starting.

The very bottom of the market is exploding with possibility. Salesforce isn’t going away. But that middle is going to be harder.

5. Investors Want Insane Growth. And There’s No Great Answer.

The challenge isn’t just the clones. It’s that investors now expect insane levels of growth. Going from $1M to $100M ARR in a year used to be almost unprecedented. Now it’s what you need to be fundable.

HigsField for video is over $200M. But that’s not the one getting talked about every week — that’s Harvey, Lovable, Replit.

And some great businesses will compound to solid rates but remain unfundable. They would have been unfundable two years ago too. I genuinely don’t know what the answer is for these founders. A company projecting 3x growth this year? Beautiful deck. But that growth rate is now a nightmare for fundraising because it doesn’t look best-in-class.

6. PE Has Said Goodbye to B2B SaaS

I know this for a thousand percent sure, and it’s slightly inconsistent with the data Carta and others put out: these pre-AI B2B companies at $50M, $100M, $200M, even $800M — no one wants to buy them.

Hooray, you got profitable. That means you’re not going bankrupt. You have not solved your existential problem in AI.

I’m advising a company at about $140M in revenue with decent growth. We did an M&A review recently, and I was shocked at how many companies much bigger than them are also on the block. They’ve already gone to Thoma Bravo, Vista, Insight. Those firms aren’t doing a $140M ARR cram-down combo exit unless everyone else in the PE chain already said no.

Until late 2023, if you got to $20M in revenue, you were still growing, and you were efficient — someone was going to buy you. The question was whether it was 5x, 6x, or 10x. That playbook is dead for PE and it’s dead for exits.

I do think the Thoma Bravos of the world will figure out how to accelerate AI in their portfolios. If you can own the agents on your platform, that’s how you re-accelerate. But if you don’t, the agents will take away all the value. And I’ve lost patience with founders in la-la land about this.

7. How We Went from 8 Salespeople to 1 Human + Agents

We’re running four different sales agents right now: Agentforce, Artisan (hot YC company), Qualified (Salesforce just bought them for almost a billion), and Clay (just raised at $5B valuation). All of them, for different use cases. It’s work. But it works.

The specific use case where Agentforce shines for us: reactivations. Folks who used to be sponsors or attend our events that a human was simply too lazy or too busy to follow up with. We scored them, sent them out with the agent. 70% open rate.

We went from eight people on our sales team to one human plus AIs. The agents do the work humans gave up on — looking up who replaced the person at Ramp or Brex, following up without shame, closing deals on Saturday night.

An agent closed a $100K deal on a Saturday night. How many humans want to do that? They’re streaming Netflix. The agent doesn’t quit, doesn’t take vacations, doesn’t need onboarding, and doesn’t demand a raise six months in.

That’s why last June, Amelia and I just said: we’re going all-in on agents. We’re going to push every agent to its limit. Now three people are doing the work of 15, and we replaced two agencies with apps we built ourselves.

8. GEO Is Real, But Deeper Than People Think

People are underestimating how LLMs are reshaping software discovery. “GEO” — generative engine optimization — is nice as a concept, but I think it undersells what’s actually happening.

I did this experiment yesterday: went into Replit and asked, “What’s the best CRM for me to use?” It said HubSpot. That’s where the money is.

We’re not going to Google anymore for this stuff. We’re asking the agent. Or even more interesting — we’re not even asking. You say “I need a website” and the agent picks a database for you. You don’t even know what it picked.

I built a digital JSON tool that’s been used 175,000 times where people just ask “what should I use?” Why would you go to Google and pay? Why sit through endless webinars with a sales rep who doesn’t know the product?

Look at Resend vs. SendGrid. I couldn’t get SendGrid to work in Replit — the founders are long gone, they throttled the free tier into uselessness. The agent said “use Resend.” I’ve never gone back. WorkOS was around for years trying to do OAuth, going through layoffs, going nowhere. Then every agent started recommending it and the company blew up.

This is still nerdy-early, but some version of this is how everyone will pick software. The companies that win agent-driven discovery win the next decade.

9. The Only Niche AI Investments Worth Making: Show Me the 10x Pricing

Will a lot of niche AI-powered software blossom? Absolutely. Dairy farms, hydroponic operations, spa management — all great. But to make money investing in these, the question is: can the agent deliver so much ROI that you can charge 4x, 5x, 10x more than the previous category leader?

If it’s the same unit economics as deals I looked at five years ago, I’m out. Even getting to $100M is tough.

But look at the AI SDR companies — Artisan, Qualified, Clay. They’re charging $100K+ to start. The last generation (SalesLoft, $2.3B exit to Vista in December 2021) struggled to get customers to pay $100K. Now it’s table stakes.

Mangomint, a company I invested in, does software for spas and salons — a crappy, hyper-competitive category with 10 solid incumbents. After they hit $30M, they blew up with agentic and automation features. Not because it made the product marginally better, but because it made the product dramatically more valuable. An $8K/year product that can now credibly charge $80K because it genuinely eliminated ten back-office positions? That’s the math I want to see.

10. Don’t Quit Your Job

One last thing. I was at an event this week with a lot of C-level B2B executives. I’ve gone every year for a while.

This year, they finally admitted it: they can’t find jobs. People with 2021-2024 tech tool skill sets — nobody needs them anymore. It’s finally sinking in.

So whatever you do, don’t quit. If you like it at your company — stay. The wealth creation happening at the top (20,000+ NVIDIA employees have made $20M+ in the Bay Area alone) is about to explode further. The best VCs and best engineers will make money like we’ve never seen before.

But the middle is going to have a very rough ride.

The IPO market is open-ish but discriminatory. EquipmentShare crushed it. Wealthfront bombed. We’re entering an era of wealth creation from IPOs unlike anything we’ve seen — trillion-dollar exits are becoming conversational, not aspirational.

Just make sure you’re on the right side of it. Build something real, show the revenue lift, own your agents, and don’t wait for PE to save you. They’re not coming.

This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit cloud.substack.com

A SaaStr AI deep dive with Sam Gutmann, CEO of Own, on building a billion-dollar backup company by saying “no” to almost everything. He joined Harpinder Singh (Partner, Innovation Endeavors) to share the whole story — and his top mistakes.

And come hear 200+ stories like this at SaaStr AI Annual May 12-14 in SF Bay!!

Top 5 Takeaways

1. The CEO who dismissed “Backup for Salesforce” as “the dumbest idea I’ve ever heard” went on to build the category leader.

Sam literally stopped a board meeting to call out how stupid he thought the idea was. Six years later, he was running the company that would define the space. Markets evolve. Your priors can be wrong. The best founders update their views when the data changes.

2. Don’t expand until you cross $100M ARR if your core market is still only single-digit penetrated.

Own had backup for ServiceNow, Microsoft, and other platforms ready to go for years. They said no. They killed products that weren’t generating revenue. The result? 100%+ annual growth rates by staying focused on Salesforce until they had the resources to truly do multi-platform right.

3. The CEO ran the financial model himself until $200M ARR — and that’s why they hit their numbers.

When their outsourced CFO offered to run FP&A, Sam said “absolutely not.” Every investment tied back to a cell in his Excel model. The outsourced finance firm told him: “You’re the only founder where our FP&A team isn’t doing this for you. You’re also the only company actually making their numbers.”

4. “Ideas are worthless. It’s all about execution.”

Salesforce came out with a competing product. It didn’t work. They killed it. They tried again. It still didn’t work. Then they acquired Own. When you have 1,000 people waking up every day focused on being the best backup product in the ecosystem, the platform vendor with 150 other products to sell can’t match your focus.

5. The hardest leadership decision — replacing a founder or key leader who got you here — always takes too long.

At a CEO roundtable, every leader agreed: firing a founder or key leader is gut-wrenching. Then they asked who would have made that call six months earlier. Every hand went up. It’s always the right decision. It always takes too long.

The Origin Story: A Vacation That Changed Everything

The story starts in the most unlikely way possible.

Sam Gutmann was on vacation in Israel in 2014. He had zero network there. But he remembered that a former colleague who’d worked at the venture fund that invested in his first company had quit his job, traveled the world, and landed in Israel.

“Let’s catch up over a beer,” Sam said. “By the way, I’m at a venture fund now. Know any startups I can meet?”

His friend Ori said: “Yeah, I’m actually job hunting. I’m meeting with these two guys who started a backup company. Want to tag along to my unofficial job interview?”

Sam had the tour van driver pull over in a city he’d never heard of called Herzliya. They sat down at a coffee shop with Ariel (the founding CTO) and two friends who’d started a part-time project called OwnBackup.

Halfway through the meeting, they turned to Ori and said, “Please stop selling yourself. We’re not hiring a sales guy.”

Then they turned to Sam: “We actually are hiring a CEO. Are you interested?”

That one-hour coffee ruined the rest of his vacation. But ten years later, they sold to Salesforce.

The Irony: Sam Thought This Was a Terrible Idea

Here’s the thing that makes this story remarkable.

In 2008, Sam was in a board meeting for his first company — an online backup service where software ran on servers and sent encrypted data to the cloud. They were brainstorming growth ideas.

The chairman walked up to the whiteboard and wrote in red marker: “Backup for Salesforce.”

Sam stopped the meeting.

“That’s the dumbest idea I’ve ever heard.”

Six years later, he was CEO of the company that would define that exact category.

What changed his mind? Simple math and pattern recognition:

* Salesforce had 250,000 customers. Every single one should have a data protection strategy.

* The average enterprise uses 300+ B2B applications. The same vulnerability exists whether your data is on a laptop, an AWS instance, or in Salesforce.

* Every B2B provider uses the same language: “shared responsibility model.” They’re responsible for the platform. You’re responsible for your own data.

Sam likens it to an apartment building: the landlord handles the infrastructure, windows, pipes, and elevators. But you’re responsible for everything inside your unit.

The Path to Product-Market Fit

The founders of Own had an unusual origin story themselves. They ran a traditional disaster recovery lab — the kind where you bring in your water-damaged phone or server with a failed RAID array, and they recover your data.

Around 2010, customers started coming in saying: “You’ve recovered data from my devices before, but now I’ve lost something in the cloud. Facebook shut my account down. I permanently deleted something in Gmail. Can you help?”

They couldn’t. They didn’t have access to Facebook or Google servers.

After enough of those conversations, they started the first cloud-to-cloud backup in 2010. For two and a half years, it was a part-time project.

Then, about a month before Sam arrived, they won a large enterprise customer in the U.S.

That changed everything. Now it was: let’s spin this out of the services company, focus full-time, and find a management team that’s done this before.

When Sam joined, they were close to product-market fit. He spent the rest of 2014 pressure-testing it. He installed Own on his family business’s Salesforce instance. He installed competitors. He deleted data. He tested whether they could actually recover it.

His framework for evaluating the opportunity:

* Is the market huge? Yes — 250,000+ Salesforce customers, plus every other SaaS platform.

* Is it a real problem? Yes — his friend Ori had personally lost data in Salesforce while consulting.

* Does the solution work? Yes — they proved it through testing.

* Can we build a company here? Yes — not just a product, but a real business.

The Power of Focus: How Own Got to $100M Before Expanding

When Sam joined, Own had products for multiple platforms: Facebook, Google, Twitter, LinkedIn, Gmail, and Salesforce. They were also close to releasing backup for ServiceNow.

His first decision as CEO: kill everything except the product generating money.

“It wasn’t until we crossed $100 million ARR that we decided we had enough resources to actually expand.” This wasn’t easy. People constantly came to him with ideas for new ecosystems. The answer was always no.

“One of the hardest things to do is say no. People go, ‘I have an idea. We want to do this ecosystem.’ No, let’s keep focusing on what we do best because it’s working.”

His logic was simple: when you’re growing 100% a year and the market is only single-digit penetrated, why dilute yourself? Why dilute the team? Why spend money in places where you don’t know the ROI when you have a proven market with massive headroom?

The metric he used to determine when to expand: $100M ARR.

At that point, the flywheel was running. He had freedom to think longer-term. He could start planning what the business should look like two to three years out.

Building the Salesforce Relationship

If you’re building in and around an ecosystem, someone’s full-time job should be managing that relationship.

Own hired someone early whose entire job was building the alliance between Own and Salesforce. Not the CEO having an occasional meeting and dropping the ball on follow-up. A dedicated person.

“If you’re building in and around an ecosystem, it’s really important that it’s someone’s full-time job. You really need to invest in these ecosystems.”

Was he worried about dancing with the gorilla?

Every board meeting, every investor meeting, the question came up: what if Salesforce competes with you?

Sam’s answer: “It was not one of the top 10 things I worried about every day when I woke up.”

His reasoning:

* Salesforce AEs have 150+ products to sell. They were never going to focus on backup as well as Own could.

* Salesforce did release a competing product. It didn’t work. They took it off the market.

* They tried again. It still didn’t work.

* Eventually, they decided to acquire the best technology.

“Ideas are worthless. It’s all about execution. We got up every day and a thousand people were trying to execute. How do we build the greatest backup product in the ecosystem?”

The Metrics-Obsessed Approach That Made Own Hit Its Numbers

Sam ran the financial model himself from day one.

“I’ll never forget this. My first trip to Israel when I officially joined the company, I had terrible jet lag. I took a monitor back to my hotel room and I was doing this gigantic Excel model — down to how much we’re going to spend on coffee every month.”

He ran that model for years.

When the outsourced CFO offered to take over FP&A and run the budget, Sam said: “Absolutely not. That’s my job. I need to understand the business. I want my leaders to understand the business.”

The CFO walked away, then came back with an observation:

“You’re the only founder where our FP&A team isn’t doing this for you. You’re also the only company that’s actually making their numbers.”

Every investment, not just coffee, tied back to a cell in the model. It was how Sam thought about the business and how he wanted his leaders to think about it.

Their board decks ran 100-200 pages by the end. The strategy was partly defensive (“If there’s enough data in there, we can just spend the entire time going through it and they don’t have time to ask questions”). But it reflected a genuine culture of measurement.

After board meetings, they’d pare down the deck to fit a 45-minute town hall and share as much as possible with the entire company.

“Did every SDR remember every metric? No. But that trust and transparency helps build trust with our employee base. It helps people think about the decisions they’re making better.”

Culture Can’t Be Outsourced to HR

Sam interviewed every employee through about 400 hires. He probably should have done it through 1,000.

At some point, he wasn’t interviewing for technical skills — he trusted the manager for that. He was interviewing for cultural fit.

“People are working their butts off, and if they’re not enjoyable to be around and good energy, it’s really hard to scale.”

His philosophy on culture:

* Culture is not something HR owns. It starts at the top.

* You can’t outsource it. Every decision should consider: how could this impact culture?

* Every new employee should understand they’re part of the culture. It’s their job to commend colleagues who uphold values and remind people when they don’t.

The reviews on Salesforce’s AppExchange tell the story. Own had five times more reviews than their closest competitor. Many weren’t just “the product works.” They said things like: “The product works, but Josh in support was awesome” or “Jane, our sales rep, wasn’t trying to sell me. I was treated as a real partner.”

That’s whole product — not just technology, but everything around it.

The Hardest Thing: Knowing When to Replace Leaders

A sales leader who can scale from zero to $200M is the exception, not the rule.

“There are great sales leaders that can go from zero to a million, or one to ten million, or ten to a hundred. That person is probably not the person that’s going to go from 100 to 200. That’s perfectly fine.”

Sam’s perspective: a leader who’s great at 1-10 should do that 5-10 times in their career. They’ll be happy and make good money. If they’re stretching way beyond their comfort zone, they’re probably miserable and you’re probably not making your numbers.

At a CEO roundtable, the moderator asked: “What’s the hardest challenge you have to deal with?”

Almost everyone agreed: firing a founder or key leader.

Then the follow-up question: “Once you’ve done it, who would have not wanted to do it six months prior?”

Everyone.

“It’s always the right decision. It always takes too long.”

The moderator, a successful entrepreneur who’d nearly taken his company public before selling, offered perspective: “I agree. When I was running a thousand-person company, it was the hardest thing to do. But now I own a professional sports team. When I make a change to the roster, there’s millions of angry fans tweeting at me that I’m a jerk. I would die to go back to a thousand-person software company and make a change like that.”

Hire People Who’ve Done It Before

Sam was always a fan of hiring people who have done it before, not just smart athletes.

One of Own’s other investors put it bluntly:

“Look, we gave you a lot of money to go execute as fast as you can. We didn’t give you a lot of money to go try. So why are you betting on someone that may be able to do it? Go buy the person that’s done it before two or three times.”

That doesn’t work in every role. You have to consider what it would do to culture to bring someone too many steps ahead. But generally: bring someone who’s done one or two steps beyond where you are today, and you’ll get there faster.

On sales leadership specifically: when you think you’ve hit product-market fit or you’re close, figuring out how to operationalize and institutionalize the sales process is critical.

“It’s great to have a lone wolf sales rep that exceeds quota, but that’s not replicable.”

Doing this too late means you might get a few sales early, but scaling becomes much harder.

The Decision to Sell to Salesforce

Own wasn’t running an active sale process. They were talking to bankers about an IPO.

But they’d known the Salesforce team for years. Salesforce Ventures had invested in their seed round alongside Innovation Endeavors and continued to support them. By the end, Own was one of Salesforce’s top five ISV partners.

The conversation started as: if there’s another round, would Salesforce participate?

At some point, Salesforce said: “Why don’t we just buy you?”

It wasn’t 2021 anymore. Markets had changed. But Sam kept coming back to the mission: every one of those 250,000 Salesforce customers should have data protection. The fastest way to get Own’s technology into every customer’s hands was through Salesforce’s go-to-market and distribution.

Was the threat of Salesforce competing a factor in the decision?

“A little bit. If they wanted to make our lives miserable, they could. But this is really going to be a strategic focus for them. They’re talking about agents and AI. I don’t think they’re going to turn the organization on a dime just to compete with us.”

It was one of ten factors they considered. But the primary driver was accelerating the mission while generating a strong return for early investors.

Why Own Worked: Whole Product Thinking

The product worked. That sounds obvious, but Sam remembers a competitor whose product could back up data but couldn’t restore it. (Backup without restore is just storage.)

But more importantly, Own thought about whole product — not just the technology, but every touchpoint.

Their reviews consistently mentioned people by name. Support was great. Sales reps weren’t pushy. Customers felt like partners, not targets.

“I think it comes down to people and culture. The product works, but Josh in support was awesome. The product works, but Jane, our sales rep, wasn’t trying to sell me.”

Expanding After $100M: Learning New Languages

When Own finally expanded to ServiceNow and Microsoft after $100M ARR, they learned you can’t just plug-and-play from one ecosystem to another.

The Salesforce ecosystem is unique. If you’ve been to Dreamforce, you know — there’s a stage where you’re learning something and stuffed animals walking around. That doesn’t exist at Microsoft or ServiceNow.

The ServiceNow ecosystem speaks a different language. What customers call things is different. Own tried to apply their Salesforce playbook directly at first and had to reboot with different people who understood those ecosystems natively.

“We screwed up at first when we thought we could just kind of plug and play exactly what we did in Salesforce to these other ecosystems.”

The No-Substitute Lesson: Hard Work

When asked what to look for in a sales leader, Sam’s answer goes beyond track record and methodology.

“The most important thing to look for is simply how hard do they work. There is no substitute for hard work when building a startup.”

When Own was based in Israel but selling to the U.S. market, everyone at Microsoft’s startup accelerator would leave at 6-7 PM. Ghost town. Except for Ori, Own’s head of sales.

“When I came to Israel, we’d work until midnight, one, two o’clock in the morning. We were the only people in the office. But we were also the only company in that cohort closing deals.”

Sam’s Top 5 Mistakes (With the Benefit of Hindsight)

1. Trying to plug-and-play the Salesforce playbook into new ecosystems

When Own finally expanded to ServiceNow and Microsoft after $100M ARR, they assumed they could apply the same go-to-market approach. Wrong. Each ecosystem speaks a different language, calls things different names, and has different buyer personas. They had to reboot with new hires who were native to those ecosystems. Should have known sooner that ecosystem expansion requires starting fresh, not copying what worked before.

2. Not interviewing every employee all the way to 1,000

Sam interviewed every hire through about 400 employees, focusing on cultural fit. He says he probably should have kept going to 1,000. The cultural foundation gets set early, and every hire that doesn’t fit the culture dilutes it. By the time you realize there’s cultural drift, you’ve compounded the problem across dozens of hires.

3. Taking too long on difficult leadership transitions

Every CEO at that roundtable said the same thing: when they finally made the hard call to replace a founder or key leader, they wished they’d done it six months earlier. Sam admits Own was no different. The instinct to give people more time, to hope they’ll grow into the role, to avoid the discomfort — it always costs more than it saves. The decision is always right, and it always takes too long.

4. Not investing in multi-platform earlier (maybe)

This one comes with a caveat: Sam isn’t sure it was a mistake. Their singular focus on Salesforce until $100M ARR drove incredible growth. But he acknowledges they might have expanded sooner given how large the other ecosystems turned out to be. The ServiceNow and Microsoft opportunities were real. The question is whether starting earlier would have accelerated or diluted them.

5. Underestimating the power of in-person events

Own’s lead generation engine was built heavily on in-person events. Their largest investor at Insight Partners called in March 2020 with alarming news: “We’ve done an analysis of our portfolio. You’re in the top three… of companies most exposed to getting all their leads from in-person events. The world just shut down, so you’re kind of screwed.”

They pivoted to online fast, but the wake-up call was harsh. A more diversified lead generation strategy would have reduced that risk. Though in fairness, the in-person approach worked extraordinarily well when events were happening.

The Bottom Line

Own’s story isn’t about backup technology. It’s about focus, measurement, and execution.

The market was huge. The problem was real. The solution worked. But plenty of companies have all three and fail.

What made Own work:

* Saying no to expansion until they’d dominated their core market

* A CEO who ran the financial model himself and tied every investment to a cell in a spreadsheet

* Culture that started at the top and permeated every customer touchpoint

* An ecosystem partnership strategy where someone’s full-time job was building that relationship

* Hard work — being the only company in the accelerator still in the office at midnight, and being the only company closing deals

When Salesforce finally decided they needed data protection in their platform, they had one obvious choice: the company that had spent ten years becoming the best in the world at exactly that.

Sam Gutmann is the former CEO of Own (OwnBackup), which was acquired by Salesforce. He’s currently taking time off while his kids lobby for a full year sabbatical, running out of home renovation projects, and waiting for his next company to find him — ideally on vacation.

This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit cloud.substack.com

Paul Adams is Chief Product Officer at Intercom, leading Product Management, Product Design, Data Science, and Research.

He joined when Intercom was just a 14-person company after first advising the startup, and has been on the executive team ever since.

Before Intercom, Paul held leadership, product, and UX roles at Facebook (Ads, Platform) and Google (Gmail, Docs, YouTube)—he was on Google’s mobile team when the iPhone launched. He’s the author of the best-selling book Grouped on social software design and co-hosts the podcast Intercom on Product with co-founder Des Traynor.

When ChatGPT arrived in late 2022, Intercom was struggling—five quarters of declining revenue growth, a failed IPO attempt. The leadership team bet the entire company on AI within two weeks of ChatGPT’s release. That bet produced Fin, Intercom’s AI agent for customer service, which now resolves over 1 million customer problems per week with a 65% average resolution rate across 6,000+ customers.

The Top 5 Takeaways from Intercom’s AI Transformation

1. If it doesn’t feel brutal, you’re not going deep enough

Paul is blunt about this: transforming a SaaS company into a real AI company is painful. Intercom wasn’t in a great spot when ChatGPT arrived—they’d had five quarters of declining revenue growth and had abandoned an IPO process. But that pressure became an advantage.

The leadership team made the call in one to two weeks. They ripped up their strategy. Ripped up their roadmap. Told the company it was happening and it wasn’t a choice.

“If you’re a SaaS company who thinks you’re an AI company and you’ve not gone through brutal transformation, you’re not there yet.”

The mistake most companies make? They do the easy, fun stuff—building AI features, experimenting with models, talking to customers about AI—but avoid the hard, messy decisions. Like parting ways with a third of the company because they’re not fit for the new world. Like deleting the marketing calendar and rebuilding from scratch.

Paul took over two-thirds of marketing six months ago and immediately blew the entire thing up. Teams, roadmaps, calendars—gone. “The only way I knew how to build a marketing org fit for this age is to build it from scratch.”

2. The only way to know if you’ve gone far enough is to go too far

Intercom operates on a simple principle: the only way to find a boundary is to cross it.

This shows up everywhere:

Every single designer at Intercom now ships code to production. Zero did 18 months ago. The mandate was clear: this is now part of your job. If you don’t like it, find somewhere that doesn’t require it, and they’ll hire designers who love the idea.

Engineering is on a path to 2x productivity—not through incremental improvements, but by declaring it non-negotiable.

Paul constantly asks: “What would a brand new startup incorporated today do here?” Would they have separate product marketers and content marketers? Or is that the same job now? Would they have both product managers and product designers as distinct roles?

The answer usually points to consolidation, not specialization.

3. How you build software has completely changed

Intercom had principles for building great SaaS products that they’d refined over years. They’d train every new designer and engineer on these principles. They were proud of them. Des had given talks about them.

They had to ban all of it.

The old way: Pick a job to be done → Listen to customers → Design a solution → Build and ship. Execution was certain. Technology was stable. Design was the hard part.

The new way: Ask what AI makes possible → Prototype to see if you can build it reliably → Build the UX later → Ship → Learn at scale. Execution is uncertain. Design is now cheap. The hard part is the AI infrastructure—the RAG system, the custom models, the empirical evaluation.

“This AI layer, our RAG system, has been 3 years in the making by a very talented team. It’s complicated. I do not understand the depths of that RAG system at all.”

The visible UI is now the small part. The invisible AI infrastructure is where the real product lives. That’s a complete inversion of how SaaS products were built.

4. AI products compound—every tiny improvement multiplies

When you’re building workflows that chain multiple AI steps together, success rates multiply. If you’re at 99% accuracy on each of 10 steps, you’re at 90% overall. If you’re at 95% on each step, you’re at 60%.

This is why Intercom obsesses over incremental improvements at every point in Fin’s system. They’ve run hundreds and hundreds of experiments, many of which fail. Sometimes they see an improvement in one part that degrades another.

They’ve built custom models pointed at very specific customer service tasks—not general-purpose, but targeted at discrete steps in the workflow.

“Each single tiny incremental improvement in each of these steps adds up to the highest performing product, adds up to something people can trust, adds up to something people will replace their humans with.”

This compounds into what Des calls the “marketing overhang” problem: lots of companies can demo an AI product, but a demo isn’t a product that works at scale. Intercom has a rule that they won’t do a product launch that isn’t a real demonstration of something they know works at scale.

See: Apple Intelligence, announced June 2024, still waiting on delivery in spring 2026.

5. You now have two companies—and two completely different buyers

If you succeed at the transformation, you end up with a new problem: you’re running two companies.

Intercom the SaaS product: Easy product domain, predictable metrics, clear differentiators, customers who know how to talk about what they need.

Fin the AI product: New product domain, chaotic metrics (“Fin’s grown 300% year-over-year—is that bad?”), customers who don’t know what they need, everything changing so fast that nobody knows what the future looks like.

The buyer has changed too. In the old world, Intercom sold to customer service leaders. Simple.

Now the buying committee includes: the customer service leader (influential but doesn’t make the decision), a C-level executive whose job is AI transformation across the company, and an AI-fluent technical evaluator who can assess whether the product actually works.

These people live in different universes. CEOs and customer service leaders don’t go to the same events, read the same things, or operate at the same zoom level. Paul does dinners with CEOs and trade shows for customer service leaders—the Venn diagram barely overlaps.

Intercom’s Top 5 Mistakes You’ll Probably Make Too

Mistake #1: You won’t reimagine your product—you’ll just add AI to it

Adding AI features to your existing product is not transformation. Fin is a completely different product from Intercom. They have almost nothing in common. You design them totally differently, think about them totally differently.

“Fin usage is eating Intercom usage at times. They’re totally different products.”

Mistake #2: You won’t make self-harming decisions to win

You’ll protect revenue. You won’t want to upset the board. You won’t want to upset your sales leader. You’ll avoid taking a 10% revenue hit to do the right thing for the medium or long term.

But these self-harming decisions are critical. “If it doesn’t feel really painful, you’re not deep enough.”

Mistake #3: You’ll dilute the vision, delay, and convince yourself you’ve done enough

“Q1 is important, let’s do it in Q2.” “Let’s dial down the vision a little.” You’ll do the easy stuff and skip the hard stuff and tell yourself you’ve transformed.

Big company habits and slowness will creep in. You’ll listen too much to customers who say no to AI—the ones who said “we’re going to differentiate on human service forever.” (Those customers now use Fin.)

Mistake #4: You won’t refine your company to fight

You need people who are going to fight for success, fight for these outcomes. There’s tension. There’s disagreement. People fight with each other—positively fighting, not actually fighting.

“If we can do it, any SaaS company can do it. There’s nothing special about us. We just decided to do it.”

Mistake #5: You’ll make these mistakes and deny it to yourself

Intercom’s exec team has the most honest, soul-searching conversations—and they still do every day. Some version of an existential question that might determine whether they succeed greatly or not.

“You really have to look each other in the eye, build deep relationships with your peers and colleagues.”

The companies that don’t transform will slide into irrelevance slowly. New competitors will eat their lunch. Their best people will quit to work at the real AI companies. Eventually, the company will die.

Three years in, AI is inevitable. Whatever you loved about B2B, SaaS and the last decade of mobile and social—it’s gone. Will you change fast enough?

Thanks for reading SaaStr AI: How To Sell, Scale, and Win! Subscribe for free to receive new posts and support my work.

This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit cloud.substack.com

Ryan Anderson, CEO of Filevine, shared their AI transformation playbook at SaaStr AI London. Here’s the thing: their new AI revenue now exceeds their SaaS revenue on a quarter-over-quarter basis. This is the roadmap.

The Filevine Story: 10 Years of Grinding, Then AI Changed Everything

Ryan Anderson didn’t set out to build a $3 billion legal tech company. He set out to stop waking up at 3am in a cold sweat.

As a young trial lawyer in the early 2010s, Anderson was drowning. Deadlines piled up. Assignments disappeared. He’d lie awake convinced he’d missed something critical. “I’m not a naturally organized individual,” he’s said. “I’m naturally anxious.”

So in 2014, he started building. First a Google spreadsheet — his “PI checklist” — at the law firm he’d founded with Nate Morris. Then a meeting over lunch in Las Vegas with Jim Blake, an engineer who asked the right questions: What’s breaking? Why is it so hard to keep track of work?

That conversation became Filevine.

For the next decade, they ground it out. Started with personal injury firms. Expanded into every legal practice area. Grew from task management to a full legal operating system: document management, demand generation, analytics, the whole lifecycle. By 2022, they’d raised $108M in a Series D — one of the largest legal tech investments ever at the time.

Good company. Solid growth. But not a rocketship.

Then AI happened.

In September 2025, Filevine announced a $400M raise at a $3 billion valuation. The round was led by Insight Partners, Accel, and Ryan Smith’s Halo Fund. Smith — the Qualtrics billionaire and Utah Jazz owner — had been trying to invest for years. Anderson kept saying no. But after Filevine’s strongest quarter in company history, Smith called again: “You’re not getting your due.”

What changed? AI revenue is now growing 130% year-over-year. Their AI chat product is growing 20%+ week over week. And as Anderson shared at SaaStr, their new AI revenue now exceeds their SaaS revenue on a quarter-over-quarter basis.

Today: $200M+ ARR, growing 50-60%, 6,000 customers, 700 employees, 96% GRR, 124% NRR.

This is what it looks like when a decade of building the system of record meets the AI moment.

Top 5 Takeaways

* “Sprinkling AI on top” is fundamentally wrong. You can’t just connect to OpenAI’s APIs and call it an AI product. That won’t cut it in 2026. You have to change your architecture.

* Nothing is sacred. You will have to tear down meaningful components of working, revenue-generating code. Use the 4-quadrant framework: map every system against “competitive advantage” and “speed.”

* Your SaaS is the closet, not the clothes. AI agents need context (your system of record), not just documents. This is your moat against AI-only competitors.

* Protect your data and price to dominate. Move from open APIs to personal access tokens. Your high SaaS gross margins let you undercut AI-only competitors on blended margins. Be savage.

* Obsess over usage, not revenue. No AI product goes beyond beta without audit trail logging. If customers aren’t using it, it doesn’t matter.

The Wake-Up Call: “We Get to Sprinkle AI on Top”

Ryan opened with a story that will sound familiar to many B2B and SaaS leaders:

“I had an engineer say to me just a few months ago with a ton of pride, mind you: ‘We have built an incredible SaaS application that makes tons of money, grows fast, customers never leave it. We have almost 96% gross revenue retention, 124% net revenue retention.’ He has every reason to be prideful. And he said, ‘The great news is now we get to sprinkle AI on top.’”

Ryan’s response? That is fundamentally incorrect.

Connecting to OpenAI’s APIs isn’t going to cut it in 2026. To be AI-native, you have to change the architecture of your system. It has to flip.

The Proof Is in the Numbers

The transformation is real and measurable. As Anderson put it at SaaStr AI London:

“It is very plain to see that the numbers back up that we are now doing far more revenue on a new quarter-by-quarter basis in AI products than in our SaaS product. Now, that’s not to say that the SaaS product is in any way less successful — in fact it’s still growing at 35-40% year-over-year. We are just growing so much faster on the AI side of the house.”

This isn’t a pivot away from SaaS. It’s SaaS + AI compounding together.

Framework #1: The “Nothing Is Sacred” 4-Quadrant Matrix

The hardest part of going AI-native? Telling your teams that some of what they’ve built — things that work, that make money — has to be torn down.

Ryan introduced a simple 2×2 matrix:

Y-Axis: Critical to competitive advantage → Not critical to competitive advantage

X-Axis: Keeps you moving fast → Slows you down

The Four Quadrants:

Upper Right (Keep & Fortify): Critical to your moat AND keeps you fast. This is the cornerstone of your AI-native movement. Don’t tear it down — make it better.

Bottom Left (Tear Down): Not critical to your moat AND slows you down. This is logically easy but emotionally brutal. These have to go.

Upper Left & Bottom Right (Judgment Calls): More nuanced. Evaluate coldly, not based on feelings.

The key insight: Someone who worked 5 years building a microservices architecture that doesn’t serve your AI needs will fight to keep it. You’ll have to be disagreeable as a CEO or technical leader making these calls.

Framework #2: Content to Context (The Clueless Analogy)

Here’s Ryan’s memorable way of explaining why “SaaS is dead” is wrong:

“Imagine I came to you and said, ‘Hey, good news. We have an AI agent that can pick out your outfits in the morning.’ You’d be like, ‘Awesome. Done. Sign me up.’ In fact, Cher in Clueless already had this.

But if you then said, ‘Oh, by the way, now that you have this AI agent, you don’t get to have your closet anymore. We’re not going to show you your closet. You can’t see it. It’s just a bunch of unstructured data and clothes and a mess.’

You’d be like, ‘Hold on. I would actually like to have my closet AND the AI agent. Can I have both?’”

Your SaaS application is Cher’s closet. The agent helps take action based on the content inside the closet.

This is why SaaS companies have a significant advantage over AI-only competitors. You have:

* The system of record

* Audit trails (who did what)

* User identity data

* Deadlines and calendars

* Contact information

* Structured workflows

To answer a simple question like “What should I do next on this case?” — you need ALL of this context. Documents alone give you an incomplete answer. And in most domains, an incomplete answer is actually worse than an inaccurate answer because the customer doesn’t know what they didn’t see.

The Architectural Flip: AI Data Layer

The old architecture: AI layer sits on top of your core services, AWS, data, codebase, calendar, etc.

The new architecture: AI Data Layer sits RIGHT NEXT TO the AI Application Layer.

Why? Because your ML engineers need to tune and change how data flows into AI applications on nearly a daily basis. They can’t be going to your traditional tech team asking “Hey, can you please change how the API provides me this data?”

The AI Data Layer owns:

* How information is prepared for AI

* How you ingest, process documents, emails, messages, and events

* The graph structure of your domain (for legal: people, events, claims, outcomes)

When ML teams own this data layer, Ryan says the results are “dramatically better, dramatically more reliable, higher context, more complete, more accurate.”

This layer powers core AI applications:

* Co-pilot

* Search (semantic + traditional, without forcing users to choose)

* Summarization

* Recommendations

* Reporting

Hiring AI Natives: The Data & Distribution Pitch

Here’s the problem: AI natives don’t want to work for “old SaaS companies.” They want to work for AI companies.

Here’s the good news: The best AI natives actually want to work where they have more access to data.

Ryan’s pitch to AI talent:

* Data Access: “In legal tech, there are hundreds of competitors saying ‘give us your documents and we’ll run AI on them.’ But we know that to actually answer a legal question, you need way more than documents. You need audit trails, user identity, deadlines, calendars, conflict checks, contact information. We have ALL of that.”

* Distribution: Show them what happens when you ship to an existing customer base. Filevine launched a product that went from 5-10 users/day to hundreds of users/day in just a few months. “Your AI team will love building products that absolutely rip because you have distribution and data.”

The Acquisition Option

Ryan’s confession: “We had an ML team. It was fledgling. Now we simply bought a company.”

Filevine acquired Parrot, an AI-native company, and merged the teams. AI natives want to work next to other AI natives. Acquiring gives you critical mass fast.

Rebrand With Intent

Filevine changed their logo from a military/legal vibe to something bolder, “up and to the right.”

But the real audience wasn’t customers — it was internal.

“This change in our mark has told the people who work at Filevine every single day: the old mark is from a traditional SaaS era and the new one is from the AI era. It is highly symbolic. You should have no problem telling your team ‘we are moving’ — and you need to give them a symbolic thing to look at for that change.”

They also created a new category: LOIS — Legal Operating Intelligence System. Not SaaS. Not AI. A blended category.

Obsess Over Usage

“We do not let our teams roll out applications beyond beta without audit trail logging to know exactly who’s doing what.”

Filevine’s real numbers:

* AI Fields product: 150 million actions in just a few months

* Docker View product: Growing extremely quickly

* Chat with your case (co-pilot): Their blockbuster product, growing 10% week over week in usage

The pattern they watch: A customer uses it 5 times, then 8 times the next day, then 20 times. Now they have customers using it 2,000 times a day.

This is how you know if your AI product is any good: Are customers using it?

Leverage Your Data: The API Negotiation

AI-only competitors will come demanding your data “like it’s their moral right”:

“I can’t believe you have all this data and you won’t give it to me for free whenever I want it. It’s your customer’s data. How could you possibly be acting this way?”

Ryan’s response:

* Control your APIs. Filevine moved from open API access to personal access tokens. They know exactly who’s accessing, what they’re doing, and how often.

* Review every request. They’ve never said “no” to a competitor, but they always say “let’s have a conversation about that.”

* Flip the script.

“When they demand access, say ‘Okay sure. But of course it goes both ways, correct? We can take the AI outputs you get from our data and we’ll get them right back into our system. Correct? Isn’t that how it’ll work?’ All of a sudden, the shoe doesn’t feel so good when it’s on the other foot.”

* Watch the API traffic. It reveals promising areas for new development. You’ll see which products are gaining traction. Copy those products and build them into your system. You have the right to do this.

Price to Dominate

Your SaaS application likely has very high gross margins (Filevine: ~80%). Your AI-only competitors struggle with margins badly because of LLM costs.

This means you can sell AI products at a lower price point than competitors.

Why? Blended gross margins. Even if your AI gross margin drops to 30-40%, your blended margin might go from 80% to 60%. That’s still way better than an AI-only competitor whose margin is driven down to 10%.

“Your investors might say ‘Why are we selling cheaper than AI-only competitors?’ Your answer is: because we’re gaining market share. And our blended gross is still higher than their blended gross.”

Be savage on pricing. The AI-only competitors will cede you no ground.

Build One Product, Sell to AI Customers Only

The boldest move Filevine made:

“We no longer sell to customers who won’t buy the AI products.”

Why?

* Architecture simplicity: If you assume AI is implicit in everything you build, you don’t have to maintain two paths.

* Team morale: How do you tell your SaaS engineers “You work on the old stuff while the ML team works on the cool AI stuff”? That doesn’t work. One company, one product.

* Customer quality: “Show me the lawyer that doesn’t want to use AI and I will show you the lawyer that’s about to get his butt kicked.”

They’re also moving from subscription/user-based pricing to usage-based pricing (per “matter” or project). More revenue from usage-based customers than traditional subscription customers.

The 5 Biggest Mistakes SaaS Companies Make Going AI-Native

* “Sprinkling AI on top” — Connecting to APIs without architectural change doesn’t make you AI-native. The AI data layer needs to sit next to the AI application layer, owned by ML engineers who can tune it daily.

* Being too agreeable — You have to be disagreeable as a CEO when telling teams their working code has to go. Evaluate what to tear down logically and coldly, not based on somebody’s feelings.

* Thinking documents are enough — AI-only competitors claim they just need your documents. Wrong. Documents alone give incomplete answers, and incomplete is worse than inaccurate because customers don’t know what they didn’t see.

* Giving away API access freely — Move to personal access tokens. Review every request. Watch the traffic to see which AI products are gaining traction — then copy them and build them into your system.

* Maintaining two products (SaaS + AI) — Build one product. Sell to customers who will come with you on the AI journey. If they won’t buy AI, they’re not worth selling to.

The Bottom Line

Filevine’s story is proof that SaaS companies can win the AI transition — but only if they treat it as a true transformation, not an enhancement.

The companies that succeed will:

* Tear down what doesn’t serve AI, even if it’s working

* Shift from content systems to context systems

* Flip their architecture so ML teams own the AI data layer

* Acquire AI talent fast (even through M&A)

* Obsess over usage, not just revenue

* Protect their data advantage

* Price aggressively using blended margins

* Sell one integrated product to AI-ready customers

As Ryan put it: “At the end of the day, it has always just been about a customer with a problem. That’s what animates us. Can you solve my problem? We can solve it with technology.”

The question isn’t whether you’re a SaaS company or an AI company anymore. It’s whether you can solve customer problems better than anyone else — using everything you’ve built plus everything AI enables.

That’s the new game.

Thanks for reading SaaStr AI: How To Sell, Scale, and Win! Subscribe for free to receive new posts and support my work.

This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit cloud.substack.com

Philip Lacor is the CRO of Personio, a $3B+ HR and payroll platform with 1,500 employees, 15,000 customers, and a 400-person sales team. He shared their AI transformation journey at SaaStr AI London — and the learnings are a masterclass for any revenue leader trying to figure out how to actually deploy AI in GTM.

We’re all hearing about AI-native companies crushing it. Replit, Gamma, Harvey.

But what if you’re running a real B2B company? One with 400 salespeople, 15,000 customers, and years of accumulated process debt?

That’s exactly where Personio was in May 2024 when their CEO kicked off an “AI Surge Week” — and what happened next is one of the most practical AI transformation stories I’ve heard.

In just six months, they went from “90% of our team uses LLMs weekly” (which sounds good but isn’t transformation) to building 400+ AI assistants, cutting research time from 2 hours to 15 minutes per rep, and booking 140 meetings in 7 days through their AI SDR.

Here’s what Philip learned — the stuff that actually worked, and the mistakes you should avoid.

The 5 Lessons: What Actually Works

Lesson #1: You Need Both Top-Down AND Bottom-Up Motion

Here’s the trap most companies fall into: They give everyone access to ChatGPT, run some training, and call it an AI initiative.

Personio did that too. Their AI Surge Week was a huge success — speakers from OpenAI, Mistral, AWS. Project teams building agents. Company buzzing with excitement.

But then Philip noticed something: High usage isn’t the same as transformation.

“After the AI Surge Week, we felt that although usage was high, this is maybe not enough to reach true transformation and to really fundamentally change the way we go to market.”

The problem? Bottom-up motion alone can’t make the hard decisions:

* Resource allocation — Who’s going to spend 40% of their time on AI initiatives?

* Permission — Can people actually stop doing their old workflows?

* Budget — Which tools do you actually buy vs. just test?

* Prioritization — Of the 50 possible use cases, which 3 do you build first?

This is why Philip started the “AI Powered Go-To-Market” working group in June — a top-down initiative to complement the bottom-up energy.

The takeaway: Bottoms-up gets you experimentation. Top-down gets you scale. You need both.

Lesson #2: Cross-Functional is Non-Negotiable

This one seems obvious but almost nobody does it right.

Personio built a working group with three distinct capabilities:

* Data & Systems Team — Owns infrastructure, Snowflake, the technical backbone

* Revenue Operations + GTM Engineers — The bridge between tech and business (they have 2 dedicated GTM engineers now)

* The Business — Marketing, Sales, Customer Success, the actual users

Why does this matter? Philip saw both failure modes:

“We have seen cases where our data systems team built things with LLMs but it was lacking the business context and therefore the models didn’t work very well.”

And the reverse:

“We had sales people who wanted to do something but originally they did not have the support from either data or systems or RevOps.”

They deliberately made the working group large — 15 people — to get broad coverage across functions and build cultural buy-in.

The takeaway: AI in GTM isn’t a sales project or a data project. It’s a cross-functional transformation. Build the team accordingly.

Lesson #3: Use Jobs-To-Be-Done to Prioritize Ruthlessly

Here’s what happened after they launched the Slack channel and started working on use cases:

“People started to share opportunities, raising their hand, and the problem was that as people started to work on these new ideas, we hadn’t finished the first one. At one point it started to spiral a little bit out of control.”

Sound familiar? Everyone gets excited, ideas flow, and suddenly you have 20 half-built things.

Their solution: Jobs-to-be-done mapping.

One of their GTM engineers literally shadowed account managers for two weeks. What she found:

* AMs were working in 7-8 different systems to perform simple tasks

* Constantly switching contexts, pulling information together

* Losing 2.5 hours per day on one activity, 3 hours per week on another

They mapped every role’s jobs-to-be-done:

* SDRs

* AEs

* Customer Success

* Solution Engineers

Then they overlaid these jobs onto the customer journey to see how they fit together — and where the biggest pain points were.

The takeaway: Don’t just chase shiny AI use cases. Map your roles’ actual jobs, quantify the time waste, then prioritize based on where you have the biggest P&L challenge or customer experience gap.

Lesson #4: Building an AI Culture Requires Leading, Sharing, and Celebrating

Philip has a formula he uses for transformation:

Effect = Quality of Plan × Acceptance

5 × 5 is way bigger than 10 × 1.

So how do you build acceptance? Three things:

Lead It: Philip does deal reviews where AEs used to show up with big PowerPoints. Now:

“I would always go like, okay, please go to Gong, open up your account. There’s this little AI sign. Go in there. Now you look for the account brief and everything is there. And in real time, we would do away with PowerPoint.”

The next time? Reps already know to use Gong’s AI. Leaders have to model the behavior.

Share It: They put their own teams on stage to share what they’ve built:

* An assistant to personalize customer decks

* An assistant to answer RFPs

* The expansion SDR assistant

Internal success stories inspire more adoption than any training program.

Celebrate It: This one’s clever: They announced that President’s Club will have 2-3 seats reserved for best AI contributions.

Not sales performance. AI contribution. And next year? Even more seats.

The takeaway: The #1 trait for an AI-powered GTM org? Curiosity. Hire for it, reward it, model it.

Lesson #5: Great AI Comes From Your Stack + Your Context

Here’s an insight most people miss:

“Let’s not go out and buy all these tools because usually the tools are not the panacea. There’s usually a lot of work that you need to do in your workflows, in your data.”

Personio’s approach: Start with what you have, add LLMs on top, iterate from there.

Their core stack:

* Salesforce (or HubSpot)

* Gong — They made a big bet here because “for a go-to-market organization, the customer conversation is obviously a very important source of data”

* Qualified — Started with fast meeting booking, then layered on AI

* Snowflake — Both structured and unstructured data

* Amazon Bedrock — LLM layer

But here’s the work nobody wants to do:

* One-third of their Salesforce data were duplicates — They installed automatic de-duping

* Months cleaning the prospect database — Buying external data, connecting sources

* Loading 5,000 Gong calls into Snowflake

* Adding emails, connecting everything together

Then — and this is critical — they added Personio-specific context:

* ICP definitions

* Pitch decks

* Onboarding processes

* Product training materials

“This is really critical to really train the LLMs and to make it specific for your go-to-market, for your customers, and for your products.”

The takeaway: The AI is only as good as the data and context you feed it. Clean your data. Connect your systems. Add your tribal knowledge. This is the work that makes AI actually useful.

The 4 Use Cases That Actually Worked

Use Case #1: Win/Loss Intelligence

The problem: Reps fill out Salesforce after winning or losing deals, but 30% of reasons were “Other” and even good data wasn’t deep enough.

The solution: They loaded all conversation data, emails, and Salesforce data into Snowflake and built a GPT for go-to-market.

The results:

* Added 10-15% new insights to competitive battle cards

* Created dynamic, continuously-updating battle cards (instead of static docs that go stale)

* Data-driven product feedback: “Based on 10,000 calls, this is where we have weaknesses”

This is evolving into a “go-to-market brain” — rep coaching, marketing campaigns, product prioritization, all from the same foundation.

Use Case #2: Expansion SDR Assistant (2 Hours → 15 Minutes)

The problem: Expansion SDRs were spending 2 hours per day researching customer information before making cross-sell calls. They’d check account health in Amplitude, contract details in another system, usage data somewhere else…

The solution: A GTM engineer built an assistant embedded directly in Salesforce. Type in an account name, and it pulls data from 10-20 systems, formats it for cross-sell, and provides a recommendation (green/yellow/red).

The results:

* Research time: 2 hours/day → 15 minutes

* Pipeline per FTE: ~2x increase

* SDRs love it — “they’re using it every day and it makes the job better”

Use Case #3: Intent Scoring for Outbound

The problem: Finding the right accounts, right people, right message is solved. But right time — knowing which prospect is actually in a buying cycle — is incredibly hard.

The solution: Their data science team built a dynamic intent score based on multiple signals:

* Website visits

* Former users who moved to new companies

* G2/Trustpilot activity

* And other signals they continue to enrich

The score shows up directly in Salesforce with flame icons (🔥🔥🔥 = start here) and refreshes daily.

Key learning: “We saw that initially the model was not great. It was picking up some signals that we didn’t think were good. We changed it and then it got better and better.”

Use Case #4: AI Chat/SDR (“Nia”) — 140 Meetings in 7 Days

The problem: Demo requests are your best leads. Waiting a week to book a meeting is crazy in a real-time world.

The solution: They deployed Qualified’s AI chat (“Nia”) on their website. When prospects request demos, Nia books meetings immediately — 24/7.

The results:

* 140 meetings booked in 7 days

* 200,000 website sessions processed

* Deep insights into what customers actually ask about (pricing, product questions, etc.)

But here’s what Philip found most valuable:

“When you start reading the chats, you see all of a sudden customers have questions about your product. They want to know what your minimum price is. You’re getting very, very rich insights in what is top of mind for these customers. I got totally hooked on it.”

The 24/7 reality: “People at 11 PM on a Friday evening, they’re thinking about requesting a demo. Why? But they do it.”

The 5 Mistakes: What NOT To Do

Now for the part that matters most — what went wrong and what to avoid.

Mistake #1: Endless Tool Testing Without Going Deep

This came up multiple times:

“I would not endlessly test tools. You got to dig in and go deep, learn from it.”

Everyone wants to try Clay, then Artisan, then the next hot thing. But surface-level testing teaches you nothing. Pick a few tools and go really deep with them.

“The point is not to test every single one of them. You got to pick a couple and go really deep with them. It’s all about the training. It’s all about the data.”

Mistake #2: Learning AI vs. Doing AI

“If you try to read all the papers and not do anything, I don’t think you’ll move fast.”

The insights come from deploying, breaking things, and iterating — not from another podcast episode or Twitter thread.

Mistake #3: Not Having Dedicated People Monitoring Agents Daily

When they rolled out Nia, the AI chat:

“There were definitely like maybe four weeks where we didn’t do enough and I don’t know how many demos we wasted by not training Nia really.”

Now they have a dedicated person (“Ami”) who looks at output every day, applies feedback, tests in real time.

Things they discovered only through daily monitoring:

* Nia started giving legal advice (“Better not”)

* Nia started bashing competitors (“That’s not us”)

* When customers ask multiple things at once, Nia would answer the product question but forget to book the demo

“You only learn that when you really start doing it and when you see where the AI stops.”

Mistake #4: Building Without Business Context

Their data systems team built LLM-powered tools, but:

“It was lacking the business context and therefore the models didn’t work very well.”

You can have perfect data infrastructure, but if the people building don’t understand the sales motion, ICP, or customer journey, the output won’t be useful.

This is why the cross-functional working group matters — and why GTM engineers need both business and technical backgrounds.

Mistake #5: Expecting AI Tools to Be Plug-and-Play

“Usually the tools are not the panacea. There’s usually a lot of work that you need to do in your workflows, in your data.”

Personio spent months:

* De-duping Salesforce (1/3 of records were duplicates!)

* Cleaning prospect databases

* Loading conversation data into Snowflake

* Adding company-specific context

The tool vendors won’t tell you this. The AI is maybe 30% of the work. The other 70% is your data, your context, and your workflows.

The Big Question: Can You Double Revenue Without Doubling Headcount?

When asked about next year’s planning:

“Managers say, ‘Hey, I need like 30 more people.’ That default should be, ‘Can I solve this with AI?’ The big question is: can we double the business with the same headcount?”

That’s the real question every CRO should be asking.

Philip’s honest take on where things stand:

* Spending multiple six figures on AI tooling

* Each SDR agent costs about $100K

* Some teams will get smaller, others bigger (channel/partner teams can use more people)

* “We will reallocate people” — it’s not about cutting heads, it’s about growing faster

A 6 Month Surge Is All It Took (To Really Get Going)

Six months. That’s all it took for Personio to go from “AI Surge Week” to 400+ assistants, 2x pipeline per SDR, and AI booking 140 meetings a week.

But here’s what actually made it work:

* Top-down support for the hard decisions

* Cross-functional team bridging data, systems, and business

* Jobs-to-be-done to prioritize ruthlessly

* Culture of AI — leading it, sharing it, celebrating it

* Stack + context — not just tools, but your data and tribal knowledge

And what to avoid:

* Testing tools endlessly without going deep

* Learning AI instead of doing AI

* Not monitoring agents daily with dedicated people

* Building without business context

* Expecting plug-and-play magic

Philip’s final advice:

“The best career advice I can give you is lean into AI. What we do know is that everybody’s jobs will evolve, including mine.”

The AI-native companies are moving fast. Your job, if you’re running a real SaaS company, is to move faster than they expect — and now there’s a playbook.

Philip Lacor is the CRO of Personio. He flew from New York to London specifically to share this at SaaStr AI London, submitted through our AI speaker form (scored yellow the first time, had to resubmit to get green 🔥), and yes — the irony of an AI-powered GTM leader being evaluated by our AI speaker scorer was not lost on anyone.

This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit cloud.substack.com

Jason Lemkin led the seed round via SaaStr Fund in unicorn Owner.com, an AI solution revolutionizing how small restaurants manage their business. Kyle Norton joined shortly thereafter, and after a slow few months, Kyle rocketed the org to almost $100m ARR in just a few years -- with growth accelerating at scale. Both Kyle and Jason have shared AI agents, learnings, and more on their AI agent journey and Kyle sat down with Jason on the very latest in AI for GTM.

Kyle now manages a 100+ human AI-infused sales team and Jason and Amelia at SaaStr have deployed 20+ AI Agents.

Top 10 Takeaways:

* AI agents are now better than mid-pack AEs and SDRs. Not better than the best. But better than average. And that’s enough to fundamentally change how you build a GTM team.

* The first agent is YOUR job. If you’re a CRO or CMO and you haven’t personally trained and deployed at least one AI agent, you will become obsolete. No agencies, no consultants. You. 30 days of work.

* Pick one tool, not ten. The biggest mistake executives make is running 8-10 vendor bakeoffs. You can’t train 10 agents. Pick two—one incumbent, one startup—and go deep.

* Salesforce is back—but not because of Agent Force. It’s because when you have 20 agents running autonomously, they need a hub. And Salesforce is that hub.

* The middle is gone. You either work harder than ever to hit 10x5x5x5x growth rates, or you join a slow-growth company at 15-20%. The magical 2021 middle where you could have lifestyle AND exceed quota? That’s over.

* Forward Deployed Engineers > Features. Don’t sign a contract until you’ve talked to the person who will actually deploy your agent. The best vendor isn’t the one with the best demo—it’s the one that will help you get into production.

* Every agent takes 30 days to train. No shortcuts. You upload data, review outputs daily, correct mistakes, iterate. The agents that “don’t work” are the ones nobody trained.

* Fix what breaks your heart first. Go to your website in incognito mode. Try to buy something. Try to get a question answered. Whatever breaks your heart—fix that with AI first.

* AI-infused teams are 3x more productive. Kyle’s team at Owner is booking 3x revenue per AE compared to any team he’s ever managed. But that doesn’t mean fewer reps—it means higher quotas and more hiring.

* The $250K SDR is coming. The elite folks—not the ones who think they’re elite on LinkedIn, but the ones who are genuinely 5-10x more productive—will earn 2-3x what they used to. But they’ll be expected to deliver 10x the output.

The Backstory: Why SaaStr Went All-In on Agents

It started with frustration.

We had two salespeople making high market, six-figure salaries. They just quit going into our biggest event. No notice. No reasons. Just ghosted.

I turned to Amelia, our Chief AI Officer, and said: “We’re done with this. I am done paying an SDR $150,000 a year or an AE $300,000 a year for basically inbound, spoonfed leads and renewals—and then having them quit on me.”

Maybe you can be critical of me as a boss. Fair enough. But I’m pretty loyal. I pay people well. Do your job with me, and I’ll stick with you for 20 years. I just couldn’t do it one more time in my career.

So we went all-in. Started in May with 1 AI Agent. Today we have 20+ agents running in production. They’re generating over $1 million in revenue. And here’s the scary part:

Our AI agents are better than a mid-pack AE or SDR.

Not better than the best. But better than the 50th percentile person I’ve worked with over my career. And that changes everything.

The New Reality: Mid-Pack Sales Execs Are in Terminal Decline

Let me be blunt: if you’re a mid-pack GTM professional who doesn’t want to work harder and smarter than a year ago, these jobs are in terminal decline.

We sent 70,000 hyper-personalized emails for SaaStr London using AI agents. They were better than the 7,000 emails humans sent before that. 10x the volume. Slightly better quality.

And here’s what happened when we asked our highly-paid SDR to follow up on a lead I spotted on LinkedIn:

“I’ll add it to my list and get to it when I can.”

Half the time, they didn’t even follow up.

The agent? The agent doesn’t argue. The agent just follows up.

But We’re Still in Inning One

Here’s what most people don’t understand: what we’re doing today with AI GTM is just step one.

Right now, “hyper-personalization” means maybe three dynamic fields in an email. One, really. Maybe we know your company name and your title.

But imagine when AI really pulls in:

* Every competitor you’ve ever used

* Every page you’ve visited on our website for 10 years

* Every interaction you’ve had with our brand

* Every adjacent tool in your stack

Imagine when AI can send an email as good as the one that got me to invest in Owner—an email the founder probably spent several hours crafting with a top 0.1% IQ.

AI should be able to do that. It’s just not there yet in GTM.

When it is? Buy that product immediately.

The Real Reason Agent Deployments Fail

The failures of AI SDRs in 2024 were all LLM-based. The products literally didn’t work before Claude 4. It was slop.

Now? They’re all above the line.

So why do agents still fail today?

Because people don’t roll up their sleeves and train them.

We bought a RevOps tool. Didn’t train it. Didn’t pay attention. Thought it didn’t work. Then we got on a Zoom and I asked our highly-paid AE why we weren’t seeing any data.

He said: “The app doesn’t work.”

I said: “Do you see that Google link in the bottom left? You have to link up your account.”

He linked it up. It showed he’d done nothing for 30 days. He quit that day.

The tool worked fine. We just never put in the 30 days to train it.

The 30-Day Rule

Every agent requires weeks of training before you can go live. Here’s what that actually looks like:

Day 1-7: Ingestion

* Upload your prospectus

* Upload documentation

* Connect to your database (or just your website as a base case)

* The agent creates a list of questions—10, 20, 30 sample outputs

Day 8-21: Iteration

* Read every output. Every single one.

* Correct what’s wrong

* “Owner is great for 100-chain high-end restaurants” → Wrong. Fix it.

* “Owner scales much more now, but our core audience is single-location restaurants with significant to-go business” → Correct.

* The agent remembers. Every day it gets better.

Day 22-30: Production

* Hallucinations become a minor issue

* You’re ready to scale

If you don’t do this? You’ll say the agent doesn’t work. But the agent was never the problem.

Do It Yourself, Dude

Here’s my strongest advice for CROs and CMOs:

If you don’t roll up your sleeves in the age of AI and AI GTM, you will become obsolete.

This is not an agency game. Not today. Maybe in 24 months.

I’m watching executives bring in their 11 agencies from their last job—their Salesforce agency, their outbound agency, their leftbound and rightbound agency from 2015—and they’re all failing.

You are the agency. For now. At least for the first one.

Here’s the brutal truth: if you haven’t trained an agent yourself, you have no idea what you’re talking about. Literally. You will be utterly ignorant in the age of AI.

I did it. Even at this point in my career. I trained the first agent myself—every single day for a month. First thing in the morning. See what the agent said, see what’s wrong, start editing and changing.

Then I proved it to Amelia. Then she did all the rest. She’s better and smarter than me. But I had to do the first one to even know what I was telling her to do.

How to Pick Your First AI Agent

Step 1: Pick one tool that solves a medium or higher-ranking problem.

For many folks, it’s AI SDR. But it doesn’t have to be. Could be RevOps. Could be support. Pick something you’re passionate about—or pick whatever breaks your heart when you go through your own customer journey.

Step 2: Find the right vendor partner.

Don’t sign the contract until you talk to your Forward Deployed Engineer. I literally did this the other day: “I want to talk to my FDE. I don’t even need a demo. Who’s going to help me deploy this agent?”

Still waiting to hear back. So that tool won’t be our first agent.

If the vendor won’t connect you with your deployment team, find another vendor. I would rather have a worse vendor and know who’s deploying my agent than the world’s fanciest brand where I don’t know who’s going to do it.

Step 3: Budget for $50-100K, not headcount.

The first one is not about headcount. It’s about budget for one tool. You need 50K, 60K, or 100K—which is not nothing, but find the budget.

Then you deploy it yourself, prove the ROI, and walk back to your CFO with data:

“We just sent 70,000 automated emails. They’re better than humans. It generated 15% of the revenue for SaaStr London. Can I have some more budget?”

Sure.

The Two-Vendor Bakeoff

We talk to so many folks doing bakeoffs of 8-10 vendors.

In the old days, pre-AI, you could kind of do this. Buy SalesLoft, let the reps figure it out.

Now? Each agent takes 30 days to train. You literally cannot do 10. It’s impossible.

Do two.

Pick one you already use (Intercom’s Finn, Zendesk’s AI, Salesforce’s Agent Force) and one hot startup that someone like you is using successfully.

Ask for references. Send an email. Kyle from Owner will respond. Marshall from Manglement will respond. They’re all getting 50 reference requests a month. Just don’t ask for a call—send an email and they’ll give you a one-word honest answer.

That’s enough.

Because here’s the honest truth: all the leading agents are good. They’re all so much better than pre-AI that what matters is can you get it into production?

These tools are evolving so quickly that during 2026 they’re going to converge. Feature parity used to take years. Now it takes weeks.

So just pick the one that’s the right match for you. The one with the best deployment support. And go.

The Lowest Hanging Fruit: Fix Your Inbound

If you want the single easiest win, it’s adding AI to inbound.

There is no excuse today for prospects not getting instant answers to their questions.

There is no need for some 21-year-old SDR to qualify whether I’m worth their time to talk to an AE.

Go to saastrannual.com. Talk to the digital Amelia—video, text, audio, however you want. She’ll answer your questions. If you want to sponsor, she’ll qualify you instantly. If you’re not a fit, she’ll tell you.

No waiting. No scheduling calls. No hoops.

I tried to buy a 10K product recently. Emailed the rep. They passed me to someone else. That person wouldn’t answer a single question until I got on a call.

If they’d had an AI bot, I would have bought it in real time.

That process is just not OK anymore. It’s no longer necessary. There’s no need to have high-friction sales that benefits some team’s funnel in theory.

AI can score a lead better than a human. Today.

Why Salesforce is Back

Kyle and I are both leaning into Salesforce more than ever. Here’s why: when you have 20 agents working autonomously, they need a hub. Somewhere for their data to meet. Somewhere to resolve conflicts.

Salesforce is that hub.

We use three different AI SDRs working with three different segments of our base. They all push their learnings back into Salesforce. They all share data. It’s a virtuous circle.

The potential conflict? We pay more for those agents than we pay for Salesforce. Which is an existential question for Marc and his team. The agents are extracting the majority of the value.

That’s why AgentForce has to win. That’s why they have 2,000 people working on it.

But here’s my take after being one of the few in production with both Agent Force and competitors:

* Agent Force is more work to set up

* But it’s as good or better in production

* The native integration with Salesforce data actually makes a difference

* The emails are pretty similar across all the leading tools

If Salesforce can make Agent Force more turnkey, it’s really going to win.

The 3x Productivity Question

Kyle’s AI-infused team at Owner is 3x more productive on a per-AE basis than any team he’s ever managed.

So does that mean hire fewer reps? Not exactly.

If it were truly 3x, you could probably get away with a 30-40% smaller team and still hit your growth numbers. Not a third—but 30-40%. But the hottest AI companies can’t find enough people to hire. The quotas at OpenAI for the enterprise team are really high. And they still don’t have enough people.

What’s changing is leverage.

Traditionally in sales, there’s been no leverage. Every year you need to add more reps. In fact, there’s anti-leverage—it gets harder to get that incremental dollar. For folks that crack the code with AI, there may be leverage for the first time. And that means the elite folks—the genuinely 5-10x more productive ones—should be paid 2-3x more than before. No problem paying someone who manages 20 agents two or three times more than a traditional outbound director.

But we’re expecting 10x the productivity. It’s not charity.

Triple Triple Double Double Isn’t Enough Anymore. At Least Not for VCs.

Let me be clear: triple triple double double is plenty good to build a multi-billion dollar company. Put it in a spreadsheet. 2→6→18→36→72. You’ll build something real.

Three or four years ago, 50% of VCs would fund you at that growth rate.

Today? Maybe 10%.

It’s not 0%. But you have to find someone who really believes.

The same amount of money is going into venture as the peak of 2021, but to less than half the companies. For startups, this means:

* Whatever capital you have needs to last

* Recruiting is harder because the best people want to work at the fastest-growing companies

* You need to be ruthlessly honest about your fundability

Go to saastr.ai/aivc. Upload your investor deck. It will tell you your exact percentage odds of getting funded based on all the benchmarks. A founder just below triple triple double double was confident they could raise. The AI said 38%. It changed their perspective.

Pick Your Path

Here’s the bifurcation that’s happening:

Path 1: Work harder than you ever have.

If you want venture-scale outcomes—10x 5x 5x 5x growth rates—be ready to work harder than ever. Kyle is working the hardest he ever has. I’m working the hardest I ever have. At Owner, even with all the tailwinds, even with an awesome team, Kyle’s still grinding.

This is what it takes now.

Path 2: Join something slow-growth.

The folks growing 10-15-20% a year need people too. Good cash comp. The right amount of pressure. Taking a company from 20% to 25% growth is a big win for the year.

If you don’t want to be thinking about work constantly, if you want to log off at 4 or 5pm—I’m not mocking that. We’re human beings. But pick your path.

The middle is mostly gone.

In 2020-2023, you could have it all. Lifestyle, work from home, 20-30 hours a week, exceed quota because of structural tailwinds. That doesn’t exist anymore.

Even the fastest-growing startups are either lean, back in office, or high intensity. There is no magical middle.

Be honest about what you want. Then pick.

Top 5 Mistakes Execs Make with AI GTM Agents

1. Running 8-10 vendor bakeoffs instead of picking two and going deep.

You can’t train 10 agents. You don’t have 300 days. Pick your incumbent option and one hot startup, do real deployments of both, and decide.

2. Outsourcing deployment to agencies or consultants.

There are no AI GTM agencies that understand your business well enough to train your agents. Not yet. Maybe in 24 months. Today, you are the agency.

3. Buying a tool and putting it on the shelf without 30 days of training.

Every agent that “doesn’t work” is an agent nobody trained. You have to upload data, review outputs daily, correct mistakes, and iterate. Every single day until it works reliably.

4. Not talking to the Forward Deployed Engineer before signing.

The best vendor isn’t the one with the best features. It’s the one that will help you get into production. Don’t sign until you’ve talked to the person who will actually deploy your agent.

5. Giving AI tools to individual reps to “figure out on their own.”

A CMO at a $10B company told Amelia they were going to buy an AI SDR tool and just hand it to each SDR to figure out—no training, no centralization. That old paradigm of “buy SalesLoft, let reps run their own cadences” doesn’t work with agents. You need a nerdy GTM person at the top of the stack managing the whole thing.

Quotable Moments

From Jason Lemkin

“I am done paying an SDR $150,000 a year for basically reaching out to with mediocre emails to leads that are already high qualify—and then having them quit on me.”

“Our AI agents are better than a mid-pack SDR, and in part, AE. Better than the 50th percentile people I’ve worked with over my career. And so we just don’t need them.”

“If you don’t roll up your sleeves in the age of AI and AI GTM, you will become obsolete.”

From Kyle Norton, CRO Owner

“We’ve got nine high-impact production use cases of AI. Our booked revenue per dollar spent, on a per-AE basis, is 3x any team I’ve ever managed before.”

“We built our cold outbound email program infrastructure in three weeks. Everybody else told us that was a multi-month project.”

“Even at Owner, even with all the tailwinds, even with an awesome team—I’m still working the hardest I’ve ever worked.”

Try our AI tools at saastr.ai — AIVC for fundability analysis, AI agents directory, and more.

This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit cloud.substack.com

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.

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.

This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit cloud.substack.com

I did an open AMA at SaaStr London last week, a classic part of each SaaStr AI event. But this one was different. It was urgency to the max.

The room was packed with founders, CROs, and marketers who all seemed to be wrestling with the same existential questions: Is inbound dead? Is the GTM playbook broken? Will AI agents replace my entire team? Should I just give up and become a forward deployed engineer?

Most of the anxiety I’m seeing in the market right now is based on a false narrative. A dangerous “woe is me” narrative that’s been accelerating since late 2023. And I think it’s time to get honest about what’s actually happening—and what you need to do about it.

The “Woe Is Me” Narrative Is Killing Your Growth

Let me start with the question everyone’s asking: “Is inbound dead? My traffic is down 50% in the last 12 months.”

Here’s my honest response: Woe is you. Your SEO is harder. Woe is you. You don’t have as many leads as you had during a lockdown during a global pandemic. Poor you.

This leads to a narrative that I think is quite dangerous: that the go-to-market playbook is broken and doesn’t work anymore.

It’s just not true.

Yes, the playbook that some folks are running from 2021 doesn’t work as well today. But here’s what I say: the plays all work. Webinars, inbound, outbound, leftbound, rightbound—it all still works.

The Same CROs, CMOs, Etc. Are Running the Hottest AI Companies

Here’s what’s fascinating: if you look at the hottest AI companies right now, you’ll see a cast of characters from the 2010s. B2B leaders you know from SaaStr 2017 and 2018 are running today’s AI rockets.

* Vercel (just raised at $10B): Their COO? She was the Chief Business Officer at Stripe.

* Replit (0 to $250M this year in vibe coding): Their CRO? He’s from ZoomInfo.

* Bolt (one of the vibe coding leaders at $60M): Head of sales? He was on our old SaaStr sales team.

That wouldn’t be possible if the plays don’t work. These leaders are using different tools. They’re using more AI things. But it’s the same playbook. Same demos. Same everything.

The biggest real difference? There’s just so much demand. Tools like Cursor, Replit, Lovable, 11 Labs—they’re so disruptive that everyone is in market simultaneously. 11 Labs went from almost nothing to $300M this year. Bolt has so much inbound they can’t service it. At $60M ARR, Brian has maybe four people on his sales team. How many of thousands of leads can they follow up on?

“They’re all classic B2B sales reps—just instead of calling every lead and trying to convince them their fungible product is the exact same as another product, they have insane demand and are servicing it. But it’s the same playbook.”

The AI Budget Paradox: Record Spending, Record Cuts

Here’s the thing that can feel like a paradox but isn’t:

According to Gartner, overall enterprise software is going to grow the fastest it ever has—15% a year at $400 billion. It’s never grown this fast ever. But of that 15%:

* Almost half is taken up by price increases from existing vendors (everyone’s raising prices)

* About half of the remaining half is new AI budget

That means if you’re not one of the vendors getting price increases or new AI budget, everyone else has to get cut.

Vendor count is getting stable or shrinking to make room for new AI offerings and price increases from select vendors. CIOs have gone around the room and said: “Give me an app. Give up an app. You want to add a couple AI apps next year? I’ll find you budget—but you got to give up two. You have 100 marketing tools? Maybe 96 is enough.”

I just got an investor update yesterday from a pretty successful company at mid-eight figures in revenue. They had $1.5 million in churn last month. From happy customers. No CSAT issues, no other problems. They literally said: “We’re cutting apps next year. We’re an attachment to CRM and we got cut.”

The CEO failed because they didn’t get above the cut line. It was great to have, but not mission critical. And it got cut to make room for an AI app.

Our SEO Is Down 8%. But Our Traffic Is Up 50%.

Is SEO dead? Let me give you our real numbers.

At SaaStr, our blog is like our core—it’s home base. Our blog traffic was fairly flat for about four years. Going into this year, our SEO is down 8%. Not 50%, but 8% for real.

But here’s the thing: our traffic is up 50%. We will end this year at saastr.com with twice as many readers as last year, even though our SEO is down.

Why? Because people all want to read about AI GTM content. They don’t want to read about classic SaaStr themes about CS teams and CROs unless there’s an AI angle. But because we have some of the best AI GTM agent content out there, people are just devouring it.

Meanwhile, G2 says their SEO is down 30-40% or more. So if that’s your only play and you haven’t changed your product or GTM since 2021, yes—it’s probably going to feel 8-50% harder.

You’ve got to find your tailwind. That’s your job right now.

AI SDRs Didn’t Work Until Claude 4. Now They Do.

Someone asked me about AI SDRs: “You said they didn’t work, but now they do. Can you elaborate?”

Really, none of these SDR products worked before Claude 4.1 Sonnet or Claude 4 this year. Replit didn’t work. Lovable didn’t work. Base44 didn’t work. They were out there—they just weren’t very good.

Then the magical moment when Claude 4 came out and everything was kind of magical.

Look at Gamma—they do AI presentations. Founded in 2020. It was five years to $1M, and then 1 to $80M this year. Replit was founded almost 10 years ago. It was 10 years to $1M, then $1 to $250M. It’s not a coincidence that all of these apps took off January, February, March of this year. That’s when the LLMs got better.

If you had a bad experience with almost any AI LLM GTM product before March of this year, write it off. It was a different time. Different LLMs. Different world.

The Two Failure Modes of AI Agents

I’ve identified two main failure modes for AI agent deployments:

Failure Mode #1: Pre-Claude 4 LLMs

If you bought overhyped GTM SDR tools before February/March/April of this year, they just didn’t work well. The LLMs weren’t good enough.

I was with the CEO of Qualified—they’ve been trying to use AI to qualify inbound leads since 2019. I asked him: “This really took off around February or March this year, right?” He said: “Yeah, that’s when it finally actually worked after 5 years.”

Failure Mode #2: “Just Turn It On”

The second failure mode: so many folks just told someone on their team to deploy it, or hired an agency that didn’t know what they’re doing.

We were on a call with a global technology leader—pretty AI-forward, impressive company—and they said: “We’re thinking about buying our first AI SDR. We’re just going to buy it and hand it to our SDRs and have them figure it out.”

It’s not going to work. You’ve got to train it. You’ve got to train it for a month. You’ve got to train it every day. You’ve got to iterate the onboarding.

I’d say 80% of the conversations Amelia and I have are a version of this: “I bought a tool and I have no leads. I bought a tool and I have a lot of leads, but I didn’t connect it to my leads. It didn’t magically work.”

The Right Way to Deploy AI Agents

Here’s the captain obvious thing for any AI SDR, BDR, even AI AEs, CSMs:

* First, you’ve got to have it work in the real world with humans. Figure out what actually closes deals.

* Then you tell the AI what worked. There’s no magical prompt. The prompt is: “Here’s the script that I use with a customer that I closed.”

* Then you iterate that prompt.

* Then you hook it up to data—Salesforce, HubSpot, Snowflake—and have it ingest the data to keep working with that prompt to make it better.

* Read every email it sends. Read every response it ingests. Some will be dumb. Some will be wrong. You say: “Hey, that was wrong. SaaStr AI London is not December 4th.” Do it every day for about a month. The mistakes start to go away.

“If you can’t sell it yourself, the AI can’t sell it for you. It’s that simple.”

The #1 Skill for 2026: Become an Agent Deployment Expert

Someone asked about the most important skill to build for 2026. Here’s my answer:

Right now, if you are world-class at Agent Force or Clay or Qualified or Artisan—pick two or three leaders with decent revenue that people have heard of—and you go through the deployment yourself, you do the training, you onboard it, you get it working in your company, and you can point to the metrics…

You will be infinitely hireable for the next 18 months because only like 2% of marketers have those skills.

Become the expert in deploying agents. It doesn’t need to mean you know every agent or even needs to be technical, but you need to:

* Pick a few leaders and deploy them yourself (not tell someone on your team to do it)

* Watch it, iterate it, train it, iterate every day

* Spend a week non-stop deploying it, then work on it every single day for a month

You will learn so much and become infinitely deployable.

The Benioff Insight: Deliver Insane Value Before They Pay

On the first podcast that Mark Benioff did with us earlier this year, I asked him what he thought of Palantir. He said: “I’m jealous of how much they charge.” Pretty funny—they charge more than Salesforce does.

But what he said next was even better: “I wish everyone at Salesforce could go live before they even pay.”

Think about this. The way we used to buy B2B software for years: I need a CRM. Your friends use it. You saw it at your last company. You talk to a sales rep that could answer six questions. You’d buy it. And if you’re lucky, by the end of the year, you might deploy it.

In the old days, you might have customers that were new for year two that never even deployed in year one. That doesn’t fly today.

Mark’s point was: “I know I can’t deliver this today—it’s not feasible—but the technology is there. I wish every single customer would be live on Agent Force before they gave me a dollar.”

Get as close to that today as you can for your customer.

When you look at the companies that explode—Cursor, Gamma—think about how much value you get in the free program. Think about how much value you get in the first month. What can you do with AI to provide that much value to customers before they even pay you?

If you provide insane value with an agent before they pay you, they’re going to be kind of happy to pay you—rather than the sucker bet we all made for two decades: “I think it works. Someone gave me a demo. The demo kind of works. I can’t use it myself, but I need that.” And then half the time it’s a horror story.

Your Moats Are Shrinking From Years to Months

Someone asked: “Some AI experts say that with AGI in two-three years, SaaS will be dead because you can literally just talk to OpenAI and say ‘write code that does that.’ What do you think?”

Listen, if you’d asked me six months ago, I’d say the odds that’s true are zero. That’s ChatGPT nonsense. You cannot reproduce the amount of workflows, corner cases, and complexity that complex B2B software does.

But you can chip away at it.

I’ve vibe-coded 11 apps in 90 days that have been used 800,000 times. I haven’t stolen revenue from anybody, but that’s a bit of an existential threat to somebody, isn’t it?

And the agent has gotten so much better. When I built my first app on Replit 170 days ago, it blew up. It deleted my database. Those posts went viral on Reddit. We got death threats from Redditors. Millions of views.

Now? When Replit launched V3 about 45 days ago, there’s multiple agents that talk to each other and check the work. Before it deleted my data, now it has an architect. When there’s a tough problem, it doesn’t just go off the rails—it says: “Hold on, I’m not sure. I’m going to call in another agent.” They bring in help on security. Now they have a design specialist.

Don’t underestimate the rate at which this stuff is getting better.

Competitive Edges Are Now Measured in Months

When I started in SaaS, you’d build something slick and you’d have about 12-18 months until you were copied by a startup. Then you’d have like 5 years until a big company copied you.

When we launched EchoSign, DocuSign (now an $18B company) only worked in Windows and was partially web-based. We had like 18 months before they decided to copy us. Then it took 5-6 years for Adobe to decide to copy it. Then Google just launched a clone last year—like a decade later.

Not too recently, I invested in a startup I love. Incredibly powerful. Within two weeks, they had four clones. And then a massive company is building a clone that’ll be out this year.

It’s not that we’re going to rebuild Workday and Salesforce and Oracle. But our competitive edges are going to be measured in months when they used to be years. And that compounds.

Look at Google—they just launched a Replit/Lovable clone. Replit gets to $250M in one year, of course Google’s going to clone it. It used to take everything 5-7-10 years to get to $100M. No one would even bother to compete.

“I don’t want to be one of these guys on X saying the only moat is speed and working 996. But there’s a lot of truth in it. If you don’t like it, you might go into terminal decay.”

The Pager Duty Warning: Two Times Revenue at $500M ARR

I wrote up PagerDuty today. It got about 500,000 views on X and LinkedIn.

PagerDuty missed its last quarter. They just crossed $500M in ARR. They’re worth $1 billion. Two times revenue.

Their customer count is flat—15,000-something customers for four years. DataDog came out with a clone. Atlassian’s product is better. Startups are better. They just didn’t move fast enough.

And I think it’s only going to accelerate. Everyone will build clones faster. It took DataDog 15 years to decide to do this. It might take them 8 months to do it next time. And 90 days the time after that.

Value Selling Is Dead Without Product Expertise

Someone asked about value selling: “Why is it so hard for sales teams to speak the language of CFOs?”

Let me be honest in the age of AI: Most sales reps have no idea how to sell value.

You cannot sell value unless you’re a product expert. Most sales folks want to talk with a war sheet, a tear sheet. They know six things. You cannot sell value if you’re not a product expert.

We were with an AI leader the other day. They closed a seven-figure deal while we were there. The solution architect left the sales team behind. Closed it without them. Didn’t want to talk to the sales team. Sales team didn’t know the product. They added no value.

Value-based selling means providing value. You cannot provide value in the age of AI if you do not know the product cold. Ideally, how to deploy it, how to get it going, how to deploy real value—not just having a valuation calculator on your website that says 18 months down the road the product will work.

“The worst sales rep you can hire today is the one that tells you they’re a ‘great people person.’ Who cares? I want an AI SDR deployed in 30 days. I want it to get me this amount of quota. I want to do this workflow. The rep who says ‘Oh, it sounds good, yeah, we can do that’—it’s not going to work.”

AI is not going to replace the AE the way it is already replacing the SDR, support, and customer success. But it is going to replace a lot of AEs that don’t know the product cold.

I really think 70-80% of the sales executives I’ve worked with over the last five or six years don’t know their product cold. If you look back at your top couple sales reps—whatever era, pre-AI, doesn’t matter—the top ones weren’t just good schmoozers. They knew the product cold. I call them “sales magicians,” but they’re not magical. They just know how every nook and cranny works.

We’re just not going to tolerate mediocre sales reps in the age of AI. We’re going to buy from somebody else.

Sales, Marketing & Support Are Converging Into One Agent

Something fascinating is happening in e-commerce that’s relevant to everyone: In the space of just a year, three categories of B2B e-commerce have all combined—sales, marketing, and support.

Think about it: when you go to a shopping cart, when you want to buy a new phone, what might you be doing? You might be buying (that’s sales). It might have a problem (that’s support). Or you might be interested but not ready today (that’s marketing).

Those used to be different apps. In e-commerce, you had Klaviyo for marketing, Gorgias for support, separate apps for remarketing. About a year ago, you’d go to e-commerce sites and they’d have five different agents: “Talk here for support, talk here for marketing…”

Now they’ve all combined because AI can easily understand: Is this a sales call? Is this marketing? Is this remarketing? Is this support? Is this customer success?

In most B2B, we’re still running multiple apps. But that’s not going to last. People don’t want 28 agents on your website. They just want one agent to solve their problems.

At SaaStr, we’ve gone from one to 20 AI agents ourselves this year. But it’s at the edge of too many—not only for us to process and manage business process change, but when you go to saastr.com or SaaStr Annual, we don’t want you to see 11 AI agents either.

These discrete walls between categories—AI is going to break them down because we just want to talk with one agent as customers, prospects, and users.

Outbound Still Works. Here’s Why.

Someone asked about outbound: What’s changed? What stayed the same?

From our AI outbound: geometrically more volume, same results. By training the AI with the best scripts and ideas that humans did, we basically saw the same results—in some cases a little worse, some cases a little better—but same as humans for the most part with much higher productivity.

Outbound isn’t dead. And if you’ve never been a buyer at a big company, you don’t get outbound.

Let me tell you a story. Back in the day at Dreamforce, a guy from Success Factors (a large SaaS company SAP bought) turned to me on a panel and said: “Yeah, I bought your product, Jason.” I said: “Wow, great. Why?” He said: “Well, it was the end of the quarter, our fax machines were broken, and you said you’d figured out e-signatures and I needed that problem solved today. I bought 300 seats.”

One of his top problems at a given time. High ROI. Immediate value proposition. He read a cold email from a company at $2M ARR.

I asked Yamini at SaaStr Annual this year: “Do you read your cold emails?” She said: “I read them every day. Email is the best. Even today—it’s the best thing in the world. Everyone reads it. It’s an open medium.”

Your job with outbound is to solve one of the top three problems of your buyer.

Whether it’s CEO, SVP, VP—if it’s a top three problem, you’re going to get a meeting. Whether it’s a top three pain point or a top three initiative. AI SDR, BDR, Agent Force, Artisan, Qualified—I don’t actually know if this is a top three problem for a lot of buyers, but I know it’s a top three thing on their mind. Everyone wants to figure out how to do an AI SDR.

If you’re in the top three, you’re going to see super high open rates if you have a differentiated product and crystal clear value proposition. This has not changed in the age of AI.

The Enterprise Budget Reality: VP Slush Funds

Let me tell you how it really works in big companies. When I was a VP at Adobe, there were three types of budget: unbudgeted, discretionary, and budgeted.

Budgeted money: Big dollars but really hard to get. Multi-million dollar contracts that often take 1-3-5 years of discussing and planning until you close the deal. Maybe get a pilot.

Discretionary/Slush budget: I was the most junior VP of all 40-50 VPs. Even I had a $500K slush budget. Today it might be a million bucks. Everyone had more.

What was my slush budget for? My top three needs that I just couldn’t get elsewhere in the organization or elsewhere budgeted through the CFO.

If you solved one of my three needs sitting in my corner tower office and it was some fraction of that $500K-$1M—I had budget. I didn’t even care. It’s use it or lose it in big companies.

I didn’t have one more dollar than that. And I didn’t care about my eighth problem. I just didn’t care. But selfishly, if you solved one of my problems, I had half a million to a million bucks to buy.

Those are the budgets you’re fighting for. They haven’t changed in the age of AI, but our priorities have changed.

It was really hard for me to get a million dollar piece of software—it probably would have taken me years as the new VP. But I had almost a million bucks in aggregate to spend on whatever I needed to get the hundreds of folks that worked for me to solve their problems. I could do that in a week or two. That was up until that line, and then I was tapped out for the year.

The Bottom Line

Stop with the “woe is me” narrative. Stop saying the GTM playbook is broken. The plays all work. The same players from 2017-2018 are running the hottest AI companies—that wouldn’t be possible if the playbook was broken.

What’s changed:

* AI agents actually work now (post-Claude 4)

* Moats are shrinking from years to months

* You need to deliver value before you get paid

* Product expertise is mandatory for sales

* Multiple agent categories are converging into one

* CIOs are cutting apps to make room for AI

Your job right now is to find your tailwind. There is AI budget. There is AI budget for new vendors at the CIO level. If you have something that truly changes the game for efficiency—if you can truly replace lots of humans with your agent—your customers are going to want to meet with you and they’re going to want to buy.

But if you’re running the exact same playbook as 2022, nothing’s changed, you’re an attachment that’s nice to have but not mission critical…

You’re going to get cut.

This post is based on the SaaStr London 2025 AMA. Join us at SaaStr AI Annual 2026 in May for more sessions on AI GTM, agent deployment, and the future of B2B SaaS.

This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit cloud.substack.com

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 completely different than what 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.

TLDR and Top 5 Learnings After Six Months of AI SDRs

1. AI SDRs Scale What’s Already Working—They Can’t Fix What’s Broken

* If your outbound isn’t working with humans, AI won’t save it

* You must have proven messaging, defined ICP, and working processes before deploying

* AI amplifies your best practices infinitely—but you need best practices first

* We had to fix our broken RevOps processes before AI could help scale them

2. They Require Massive Human Oversight (15-20 Hours Weekly)

* These agents consume the signficiant amount of Amelia’s and Jason’s time to run successfully

* Performance ebbs and flows directly with human attention—more time invested = better results

* Weeks I’m busy with other work, agent performance noticeably dips

* This is not set-and-forget technology; it’s coaching five SDRs simultaneously who work 24/7

3. Specialization Beats All-in-One. For Now.

* We run 5 different AI SDRs, each trained for specific use cases (cold outbound, lapsed customers, active nurture, inbound qualification, ghosted lead recovery)

* Even within one platform, we have sub-agents with completely different training

* Specialized tools go deeper than all-in-one platforms—we’ll take three A+ tools over one B+ tool

* The training specificity for each use case matters enormously for results

4. The Unexpected Direct-Selling Capability

* AI got surprisingly good at closing deals directly, not just booking meetings

* For sub-$1K products (event tickets), our AI now closes deals autonomously

* For higher ASP deals ($50-100K+), it qualifies and books meetings, then hands to humans

* 20% of our event ticket revenue now comes from AI agents selling directly

5. Budget $50-100K Per Platform + A Lot Of Your Time

* Effective AI SDRs cost $50-100K+ annually per specialized platform

* But the bigger investment is your time: 15-20 hours weekly managing them

* We reallocated budget from two human SDR roles instead of finding new budget

* ROI is clear (our inbound agent: $1M revenue in 90 days on ~$100K investment) but only if you commit

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:

* Lapsed Sponsors: “We worked together on SaaStr Annual 2023, here’s what’s new...”

* Current Sponsors: “You’re sponsoring Annual, have you considered London?”

* Previous Attendees: “You attended last year, early access to this year...”

* Engaged Non-Converters: “You’ve opened our last 5 emails about speaking...”

* 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):

* Prospect fills out contact form

* Goes into queue for me to round-robin to rep (delay: minutes to hours)

* Rep gets assignment, responds (delay: hours to 24 hours)

* Back-and-forth to find meeting time (delay: days)

* Meeting finally happens, first 10 minutes wasted on basic discovery

* Deal cycle starts

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

After AI (The New Way):

* Prospect visits website, AI engages instantly

* AI qualifies, understands needs, books meeting—all in real-time

* 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:

* Prospect asks for discount

* AI: “Absolutely! Here’s code LONDON25 for 15% off. You can use it at checkout.”

* Prospect leaves without buying

* 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?”

* 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:

* Talk to the actual implementation specialist or technical person before signing—not just the sales rep

* 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”

* Ask them how many deployments they’ve done personally and what success rate looks like

* 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:

* Outbound first (May)

* Inbound second (August)

* Follow-up third (October)

* 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.

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 in December, where you can interact with our AI agents live and see exactly how we’ve built this.

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