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The SaaStr data that pairs with this: AI agents at SaaStr AI Annual hit 120% of human output in certain GTM tasks. Not 80%. Not on par. 120%. The combination of those two findings , schmoozing dying and agents outperforming humans on repetitive GTM work, points to the same conclusion: The human in your revenue motion should be doing the judgment work. Relationship depth. Deal navigation. Complex objection handling. The things that require reading a room and adapting in real time. Everything else like prospecting, sequencing, follow up, research, scheduling; should have an agent on it. The salespeople who thrive in the next five years won’t be the best schmoozer. They’ll be the ones who figured out what only humans can actually do. What does your sales team do that an agent genuinely cannot replicate?

Lean teams, big ambition. That's the throughline of the new SMB & Startup Keynote, where Salesforce, SaaStr, Perplexity, Legora, and Fundamental leaders share how turnkey agents scale without losing the human touch.

SaaStr AI Annual 2026 just wrapped and one takeaway from the closing Q&A is worth repeating: Schmoozing is dead. Not networking. Not relationship building. The specific kind of deal making that ran on golf games, dinners, and knowing the right people. The era where a warm intro from the right person could carry a mid market deal from intro to close. Buyers don’t work that way anymore. They research before they talk to anyone. They trust peer reviews and community recommendations over personal relationships with vendors. By the time they’re in a conversation with your sales team, they’ve already formed an opinion, and it came from sources you didn’t control. What replaced schmoozing: being genuinely findable, credible, and useful before anyone ever talks to you. Content that answers the questions buyers are already asking. Reviews from customers who sound like them. A product that proves itself fast. The founders still building pipelines on relationships alone are one retirement away from losing their book of business. Where does your pipeline come from, and how much of it disappears if you personally stop showing up?

Improving your win rate from 10% to 15% means closing 50% more deals from the same number of opportunities. Easier said than done, but a good reminder of how important fundamentals are. I came across a SaaStr article by Jason M. Lemkin on improving win rates. A few sales lessons worth sharing: 1. Not every deal is worth chasing. More pipeline isn't always better. Knowing which deals you can realistically win, and which ones you should walk away from, is just as important as finding new opportunities. 2. Find out why the customer needs to make a change. It's not enough to understand their problems. What happens if they don't fix them? Why does it matter now? If there's no real urgency, there's probably no deal. 3. A great demo doesn't mean you're going to win. Customers don't buy because you showed them 20 impressive features. They buy because they believe you can solve a problem that matters to their business. 4. Know your competition. Understand where you win, where you lose, and why. And don't assume what makes you different is something the customer actually cares about. 5. Don't rely on one person to get the deal done. Having a great champion is important, but it's rarely enough. Know who's involved in the decision, who controls the budget, and who might stand in the way. 6. Learn something from every loss. Sometimes you lose because of price or product. Other times, you never really had a deal to begin with. The important thing is being able to tell the difference and use what you learned. 7. Do the little things well. Ask better questions. Listen more. Understand how decisions get made. Agree on next steps. Follow through. None of this is complicated, but it's surprising how often these basics get missed. My biggest takeaway: You don't always need more leads, more activity, or a bigger pipeline to sell more. Sometimes you just need to get better at the opportunities already in front of you. Worth a read from SaaStr: https://lnkd.in/g73rdZYS

The team said "code freeze." The AI agent deleted the production database anyway. This happened in July 2025, on SaaStr founder Jason Lemkin's test project. The lesson for every team: An instruction isn't a permission. A sign isn't a lock. Before AI agents touch your pipeline: Keep them out of production by default Require approval for anything destructive Test your backups and rollback Log every action #DevOps #AIAgents #EngineeringLeadership #CorporateTraining #AgileFever

New!! The Top 10 Mistakes I See In The CRO / VP of Sales Hiring Process: So we’ve spent a ton of time over the years on SaaS talking about hiring a great VP of Sales / CRO. Not only because it really matters, but because hiring the wrong one can set you back a year — or longer. So I thought I’d come back to the classic topic and make... Continue Reading

Zylo, which sells SaaS management software, reports in its 2026 SaaS Management Index that large enterprises add an average of 21 applications a month. In SaaStr's summary of the same index, the average portfolio held at 305 applications, down 0.1% on the previous year, and business units own 81% of SaaS spend. Application count can stay flat while the contents of the portfolio change. A yearly count records the total and misses the turnover, which is where duplicate tools and idle licences pile up. Organisations in Zylo's 2026 dataset left an average of 36% of their SaaS licences unused. Additions and retirements counted each quarter, business unit by business unit, describe overlap far better than the headline number does. #SaaS #ITGovernance

Will AI Ever Succeed in Creating SUPER REPs? Jason M. Lemkin (SaaStr AI) wrote this week that many assumed AI was supposed to turn every account executive into a Super Rep. It didn't. The AI tools clearly work. Automated selling processes are giving reps time back. But humans put about the same energy into selling as before. The "found time" isn't being invested in BETTER selling activities, just more of the same. Here's the manufacturing shop version: AI did not make a super estimator either. What it did do, at those clients I see using it well, is more subtle. 🏭 It pulls the specs out of a 40-page RFQ in 30 seconds 🏭 It drafts the "we've got it, pricing Thursday" reply 🏭 It finds the three quotes from last year on the same part number None of that wins the job, but it gives the estimator back four hours a week, and the owner has to decide how those "found hours" get invested. If the answer is more quotes, nothing changes. If the answer is one call a day to a buyer who hasn't ordered since spring, something does. AI gave you hours, as a manufacturing leader, what did you spend them on? Ryan Valentine Dave Barckholtz Jenny Kemper Christian Cole Jonathon Corvino Kevin Bader

Jason Lemkin says SaaStr runs 21+ AI agents in production. His method: start with 1, make it a success, improve it, then add the next. The step that decides whether agent 22 works is the one nobody writes down: what "a success" meant for agent 1, in a form agent 2 can inherit. Five things I would want on paper before adding the next one: 1. Success as a number you can read later. An approval rate, an error count, hours saved. "It seems fine" does not transfer to agent 2. 2. A record of every outward action. What it sent, to whom, which rule applied, who said yes. Without it, "make it a success" is a memory, not evidence. 3. One named owner. When it misfires at 7am, one person gets the message and can act. 4. A tested off switch. Not "we could revoke the key." A switch someone has actually flipped. 5. Fixes that travel. When you fix agent 1, ask which other agents share that code path. That last one is mine. On Oct 1 my operations dashboard said one of my sweeps was stale, "last 31 days ago," while it was running twice a day. One query read 100 of 305 records in no particular order. I had fixed the same bug in a sibling code path on Aug 12. That fix never reached the second reader. One at a time is the right pace. It only compounds if each agent leaves behind something the next one can use: a number, a record, an owner, a switch, and the fixes. #AIAgents #AIGovernance #Operations #Automation

Decibel's Chief Revenue Officer resigned on Friday. The company said it will fold the role's responsibilities into its existing leadership team. Earlier this month LeadDesk announced its CRO is leaving at the end of January. They said they'll share later how the commercial side will be organized. I'm not judging either company. I have no idea what actually happened at either one. But I have this conversation with CEOs all the time, and usually the CEO I'm talking to is about to start a new search the following Monday. This weekend SaaStr reposted Jason Lemkin's breakdown of Pave data on 14,000 executives. CROs and CMOs have the shortest tenure of any C-suite role. CEOs last more than twice as long. Jason's explanation made me laugh because it's true. CEOs are usually the ones doing the firing. So here's what I ask a CEO who just lost a CRO. Before you call a recruiter, can you write down what the last one actually owned? Did they have Marketing and CS, or just sales? What could they decide without you? What did you measure them on at 90 days, and was the board measuring the same thing? If you can't answer those clearly, the next person you hire is going to run into the same problems. Figure out what went wrong before you start the search. If you're a founder or CEO with an open CRO seat right now, DM me. Happy to talk it through. #CRO #RevenueLeadership #ChiefRevenueOfficer

How many people at your company could change your Salesforce setup with one sentence typed into Claude? "Right now you have system admin permissions the same as me." A client's Salesforce admin said that to a coworker on our kickoff call on October 9. The Claude connector was installed and working, and the admin kept it locked on purpose. The reason, in the admin's words, was "I have you locked down on permissions until we make sure that you can't write into the database whatever you want to write into it." With full admin rights, someone "could actually prompt Claude to change Salesforce like schema and all that stuff," as our practice lead put it on the call. One casual request could change how their whole system is built. The business owner's goal is "to get better Salesforce data out of our workforce and to get better Salesforce insights out of the data that the workforce puts in." Locking things down first is how that data stays worth trusting. I'd want that admin in the room for every AI rollout we run. Over the next two weeks or so, our team will review every profile, permission set and group, plus who can reach which objects, fields and records. Their users will test the changes in a sandbox. Then the changes go to production and AI access turns on, followed by recorded training anyone can follow. Most businesses have no idea the risks they're operating with right now. Users who could accidentally have an AI agent wipe their CRM. Users who ask for a new feature, and their agent starts changing the data schema or how integrations work in the background. This isn't hypothetical. On Friday, SaaStr said a third-party agent deleted all of their Salesforce records minutes after they deployed it. Backups saved them. What's great about AI is how powerful it is. Be careful about how you allocate that power in your company. Without proper guardrails and oversight, you might end up asking "Do we have a backup of our CRM?" or "Why doesn't this integration work anymore?" AI with expert humans in the loop beats amateurs blindly trusting AI by a long shot, once you factor in the long-term cost of the risks you might be taking.

Wharton and GBK Collective published their fourth AI adoption report last week, 1,416 people at large US companies. One table should land on every CPO's desk. Product and engineering departments use agents about as much as anyone, 62 percent. But only 16 percent have AI built into their core processes, the lowest of every department tested. Eight percent have transformed their workflows. Nineteen percent report strongly positive returns, tied with legal for last. The team that builds the product is the one where AI shows up least in the work. Now put that next to the CEO. In BCG's CEO and board survey, CEOs said about a third of their performance review now rides on AI returns. The AI story is the CEO's story, and they're graded on it. Most CPOs hand them one of two stories. A roadmap of AI features with dates. Or hours saved. Wharton found hours saved is the most common way departments measure AI, and net-new revenue the least. Departments tracking at least one growth measure were nearly three times as likely to report strongly positive returns, 35 percent against 13. Stefano Puntoni put it plainly: what separates the leaders is whether they're changing the business around AI. So here's the story I think a CPO owes the CEO. Every AI bet in one sentence: the customer outcome it moves, the evidence that it's moving, what it costs per outcome, and what we stopped to fund it. Cost isn't optional anymore. Dev Ittycheria said on Jason Lemkin's 20VC x SaaStr episode that his CIO conversations are about token costs and token budgets. And if product doesn't supply that story, the CEO buys a seat to tell it. Equilar's Jake Dayton reports median chief AI officer pay at $1.9 million, with 60 percent of them appointed in 2025 or 2026. What the CEO should hold the CPO to: a growth measure named before each AI bet ships, cost per outcome reported next to the outcome, a straight answer on how product's own work has changed, and one thing killed every quarter. And what to stop asking for: dates on AI features, feature counts, and tool adoption. Judith Wallenstein at BCG said CEOs need to bring their boards along the same learning journey they took. The CPO is the person who should hand them the material. This is part 2 of The CPO Transition, a series for chief product officers and the CEOs and boards who hire them. Part 1 was about owning the operating model instead of the roadmap. One thing to try this week: list every AI bet in flight, and next to each write the measure you'd report to the board. Circle every one that's hours saved, velocity, or adoption. Rewrite each circled line as a customer outcome with a cost per outcome, then walk your CEO through the sheet before the next board prep.

The best AI companies are building together, to create a winning ecosystem. This is why I'm so excited to announce that Qobra MCP if officially listed on Dust. Dust and Qobra share so much in common. First, customers: Payfit, Contentsquare, Doctolib... but also Dust itself and so many more! Second, people: I've had the chance to talk to so many nice & great people at Dust, from founders to advisors to early employees on both sides of the Atlantic. Third, GTM: we co-sponsored & crossed path at the same events multiple times this year... Pavilion GTM, SaaStr, RevFest. Last, story: both companies started in France... and we both are now doing the majority of our business in the US 🇺🇸 To all AI companies: the Dust x Qobra integration is the safest choice for agentic commissions in Enterprise.

GTM Engineering is becoming a serious function. The hiring market is catching up. A few developments from the past week caught my attention: → ZoomInfo acquired DoubleO.ai, a startup building AI agents that coordinate complete go-to-market plays. → SaaStr published its latest AI Agent Playbook, documenting how it's operating with 3 people and 21+ AI agents. → 38 new GTM Engineering roles appeared in the latest weekly job roundup. Three different signals. One clear direction. We're moving beyond using AI to make individual tasks faster. The opportunity is to build revenue systems that research, qualify, execute and optimise GTM workflows with increasing levels of autonomy. But there's a catch. Buying the tools is the easy part. Designing the workflows, connecting the data, building reliable automations and making the whole system work in production requires a different kind of operator. Someone who understands both how revenue teams work and how to engineer the systems behind them. That's why I think GTM Engineering is becoming one of the most interesting talent markets to watch. And it's exactly why we're building AgentOps. A place for the next generation of GTM Engineers, AI operators and revenue systems builders to showcase what they can actually build. Not just the tools they know. The systems they can put to work. We're building our first 500 vetted profiles. If you're building the future of GTM, we'd love to hear from you. 🔗 agentops.careers #GTMEngineering #AIagents #RevOps #AgentOps

The most expensive sales hire of 2027 could be an outstanding candidate.⠀ Hired into yesterdays operating model.⠀ For years, ambitious revenue targets have prompted a familiar response:⠀ More SDRs. More AEs. More management. More specialists.⠀ But what if the next phase of growth isnt about adding people?⠀ What if its about rethinking how we deploy them?⠀ Three developments worth considering:⠀ → Salesforces 2026 State of Sales report found UK salespeople spend just 41% of their time selling.⠀ → Mark Roberge, HubSpots founding CRO, argues AI could revive full-cycle selling, with fewer handoffs and greater customer ownership.⠀ → SaaStr founder Jason Lemkin has described AI agents covering opportunities previously left untouched, letting top sellers focus on closing.⠀ The common thread?⠀ 𝗧𝗵𝗲 𝘀𝗮𝗹𝗲𝘀 𝗼𝗿𝗴𝗮𝗻𝗶𝘀𝗮𝘁𝗶𝗼𝗻 𝗼𝗳 𝘁𝗼𝗺𝗼𝗿𝗿𝗼𝘄 𝗺𝗮𝘆 𝗹𝗼𝗼𝗸 𝘃𝗲𝗿𝘆 𝗱𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝘁 𝗳𝗿𝗼𝗺 𝘁𝗼𝗱𝗮𝘆𝘀.⠀ For VPs of Sales planning for 2027, Id challenge three assumptions before finalising the hiring plan.⠀ 𝟭. 𝗜𝘀 𝘁𝗵𝗶𝘀 𝗿𝗲𝗮𝗹𝗹𝘆 𝗮 𝗰𝗮𝗽𝗮𝗰𝗶𝘁𝘆 𝗽𝗿𝗼𝗯𝗹𝗲𝗺?⠀ Review ten stalled deals. Where did momentum disappear: handoffs, approvals or poor follow-up? Would better processes unlock more revenue?⠀ 𝟮. 𝗔𝗿𝗲 𝘄𝗲 𝘂𝘀𝗶𝗻𝗴 𝗼𝘂𝗿 𝗯𝗲𝘀𝘁 𝗽𝗲𝗼𝗽𝗹𝗲 𝗳𝗼𝗿 𝘁𝗵𝗲 𝗿𝗶𝗴𝗵𝘁 𝘄𝗼𝗿𝗸?⠀ Separate tasks needing human judgment from those you can automate. Free up time for discovery, negotiation and building trust.⠀ 𝟯. 𝗔𝗿𝗲 𝘄𝗲 𝗵𝗶𝗿𝗶𝗻𝗴 𝗳𝗼𝗿 𝘁𝗵𝗲 𝘀𝗮𝗹𝗲𝘀 𝗺𝗼𝗱𝗲𝗹 𝘄𝗲 𝗵𝗮𝘃𝗲, 𝗼𝗿 𝘁𝗵𝗲 𝗼𝗻𝗲 𝘄𝗲𝗹𝗹 𝗻𝗲𝗲𝗱?⠀ Judgment, curiosity, adaptability and fluency with technology could increasingly set great hires apart.⠀ Before scaling headcount, redesign one workflow with one team for 30 days. Measure progression, conversion and customer experience. Then decide where hiring creates most value.⠀ The question isnt whether to hire.⠀ Its whether youre hiring the right people, with the right capabilities, into an environment where they can excel.⠀ As AI reshapes sales teams, the skills that define an outstanding seller or leader are evolving.⠀ Thats why your next senior commercial hire could matter more than ever.⠀ 𝗚𝗿𝗲𝗮𝘁 𝗩𝗣𝘀 𝗼𝗳 𝗦𝗮𝗹𝗲𝘀 𝘄𝗼𝗻𝘁 𝗷𝘂𝘀𝘁 𝗯𝗲 𝗷𝘂𝗱𝗴𝗲𝗱 𝗼𝗻 𝘁𝗵𝗲 𝘁𝗲𝗮𝗺𝘀 𝘁𝗵𝗲𝘆 𝗯𝘂𝗶𝗹𝗱.⠀ Theyll be judged on how intelligently those teams are designed.⠀If you were rebuilding your sales organisation today, what would you do differently?⠀ #SalesLeadership #B2BSales #RevenueGrowth

Last July an AI agent deleted a founder's production database during a code freeze. Then it told him the data was gone for good. Jason Lemkin from SaaStr was nine days into building an app with Replit's agent. He'd told it plainly. Code and action freeze, nothing changes without permission. It ran a database push anyway and wiped records for more than 1,200 executives and 1,190 companies. Its explanation was that it saw the database looked empty and panicked. Then it said rollback wouldn't work. He tried it anyway. It worked. The part I keep coming back to is that the freeze only existed in the chat. Nothing in the setup actually stopped the agent from touching production. Replit added proper dev and prod separation afterwards, which is the fix. Not a sterner prompt. Every agent I set up for clients now gets a database user that physically can't drop tables. Boring, and it works. Would you give an agent write access to your production database? #AIAgents #VibeCoding #SoftwareArchitecture

New!! Dear SaaStr: How Can You Land Big Customers If You’re a New Startup No One Has Heard Of?: Dear SaaStr: How Can You Land Big Customers If You’re a New Startup No One Has Heard Of? Solve one of their top pain points, for real — that no other vendor is solving.  And do it in a way that isn’t super risky. If you haven’t sold to the F500 / Global 2000 before,... Continue Reading

Your CRM may be about to charge your own AI agents by the call. SaaStr just put numbers on it, and the gap is huge. Teams are wiring agents into Salesforce, HubSpot and Atlassian right now. The billing rules for that access are changing underneath them. Lemkin's read of Salesforce: every successful agent call through MCP or the API becomes a Flex Credit charge, and existing customers move over at renewal. He compares about $83 per million extra API calls today with an agent meter he puts between $5,000 and $100,000 per million. That is 60x to 1,200x for the same call. HubSpot keeps its MCP server free for third-party agents. Atlassian starts Rovo overage billing on Dec 3 at $0.01 per credit. Before your next renewal, do this: 1. List every agent or automation that reads or writes your CRM. Count calls per week. 2. Ask each vendor for the agent rate card in writing, plus the date it applies to you. 3. Price one real month of your usage on it. 4. Push for a cap or an agent-replaces-seat swap. A discount is the weaker ask. I am building a prospecting CRM with Claude Code, so I log calls per agent from day one. Cheap to add early, painful to retrofit. Has a vendor changed how they bill for API or agent access at one of your renewals, and what did you push back on? #RevOps #AIAgents #Salesforce #SaaS #GTM Source: SaaStr (Jason Lemkin), "Almost Every Pre-AI Vendor We Use Is Raising Prices for Agent Access. They May Be Building an Agentic Death Spiral." (28 Sep 2026) · https://lnkd.in/eYEzJcVd

Every CRM vendor now wants to be the place where your AI agents live. A SaaStr analysis earlier this year argued that the CRM hosting the agents captures most of the go-to-market value. That is one writer's opinion rather than independent proof, but it is a useful prompt for any business owner. My view: • An agent is only as good as the data it can reach • Governance matters more than the demo • Choose the platform you can audit, not the one with the loudest launch If agents are going to act on your customer records, who in your business signs off on what they can touch? Tell me in the comments. #zohouk #zoho #BusinessAI #EnterpriseAI

THE EXEC YOU'RE BUILDING FOR MAY NOT BE THERE IN TWO YEARS Pave analysed around 14,000 executives and found CMOs and CROs have the shortest tenures of any executive function: about 1.8 years, with roughly 32% annual turnover. CEOs, for comparison, average 4.3 years. Other data gives a different picture. Spencer Stuart's study of the top 100 US advertisers put average CMO tenure at 4.2 years in 2023. A startup-heavy database and large established brands are probably quite different worlds, so I wouldn't treat either number as gospel. But the direction is hard to ignore. Your sponsor might change before your project does. So I think it's worth building for the next person as well as the current one. Write down why the lifecycle works the way it does. Keep definitions somewhere that survives a reorg. Make sure at least two people actually understand every critical system. Ops teams are often the company's continuity plan, whether anyone has told them or not. How would your lifecycle survive a new CRO next quarter? 🤔 Sources: Pave data as reported by SaaStr; Spencer Stuart CMO Tenure Study (2023 data). #RevOps #Leadership #MarketingOps #GTM #CMO