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Peter Kuhn posted thisLots of B2B SaaS GTM teams buy AI tools that listen to sales calls and tell the VP which deals are qualified. I’ve already seen multiple times that they will confirm things the buyers never actually confirmed. This is very problematic and can make you lose your next deal: A buyer saying "we'd probably find the money for this" and a buyer saying "I have $100K approved for this project" are not the same thing. One is confirming their budget. The other is suggesting they think they could potentially find it. Most of these tools will mark both as Budget: Confirmed, and the GTM team never notices because the answer goes straight to the CRM. Now your next step in this deal will be off and your pipeline forecast will be biased too. This is basically what qualification frameworks like BANT and MEDDIC are supposed to help with: separating what you actually know about a deal from what you're still assuming. When AI collapses that into a CRM field like “Budget: Confirmed,” you lose the distinction that matters. Give the rep the context instead: - What the buyer said: "we'd probably find the money." - What the CRM says: budget confirmed. - What we still need to learn: how much? Is it approved? Who controls the budget? Now the rep walks into the next call with a job. Get the number. Find out who owns it. That's the work that moves a deal forward instead of it sitting for a month because nobody realized the budget was still unverified. If you have ten minutes today, go try it on one deal. Take any opportunity marked "budget confirmed" and find the sentence in the transcript where the buyer actually confirmed it.
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Peter Kuhn shared thisBack in May, Deel shut down an AI customer service agent. It was supposed to handle the customer success workflow end to end, from client emails to internal messages. Deel's COO said it failed for three reasons: 1 - the compute wasn't fast enough 2 - the model wasn't capable enough 3 - the volume of context it needed (customer history, past tickets, account details) couldn't be pulled up and processed quickly enough to answer well. I’m not sure I fully agree with these. The first two are going to keep getting better on their own. Compute is getting faster and models keep getting more capable. It’s already night and day from three months ago. Give it another year, never mind five. But even if Deel rebuilt the agent with the models we have today, I’d bet it would still fail because of the third reason. If your agent needs to go fetch a bunch of customer context, put it into the prompt, and then figure out what matters, a better model doesn't solve how that information gets there in the first place. That gets slow and expensive fast, and there's no guarantee you pulled the right information. Better models can do more with the context you give them but you still have to get the right context there. Deciding what the model sees, when it sees it, and how that context gets packaged is part of the system you have to build. That's the part you own, and it's the same problem whether you're automating support or helping a rep prep for a call.
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Peter Kuhn shared thisI worked with an enterprise sales org closing six- and seven-figure deals, and their pipeline review was useless (the reps’ words btw). Here's how we rebuilt it: The review ran twice a week with fifteen people on the call. Each rep went deal by deal reading what they'd typed into Salesforce: stage, probability, close date, and one line for next steps. Nobody learned anything they couldn't have read themselves, and the CRO left each call without really getting context on their pipeline health or which deals were slipping. They'd tried to fix it once already by bringing their Gong recordings into Snowflake and querying them back out through Salesforce, but sellers said it wasn't giving them anything they needed and they stopped using it. We did two things: 1 - Made capturing the deal zero effort. Every call gets recorded and transcribed automatically, so no rep has to upload anything or write notes afterward. If they had to, most wouldn't, and whatever happened on that call would never make it into the system. The transcripts then get organized so that when someone asks about a deal, they get back the parts of its history that matter for that question. 2 - Made it useful for a rep's daily work before we need it in the meeting. That meant drafting follow-up emails from the call history and figuring out the next move on a stalled deal. The meeting only works if reps already use the system every day, and they only do that if it actually helps them every day. The pipeline review now runs on that. For each deal, the rep brings three sentences on what's happened and been decided, the open risk mapped against their qualification framework, and what they're working on over the next week to move it forward. The CRO either signs off or flags it for a deeper strategy session. She now calls it her operating system for the whole revenue org, and account management runs on it too.
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Peter Kuhn shared thisJake Wood and team are killing it. This is the real deal! I’ve seen the funnel/ sales cycle. This is a very sweet opportunity. DM if you want an intro! Cc GroundswellPeter Kuhn shared thisDo you know what's fun? Having so much inbound pipeline that your sales team can't effectively manage it. Groundswell is hiring its next amazing seller. Are you that person? You might be if: 1️⃣ you're an epic human 2️⃣ you've sold midmarket and enterprise deals in the social impact sector 3️⃣ you're allergic to the status quo 4️⃣ you eat, sleep, and breathe competition 5️⃣ you have a history of success I don't need a coin operated AE. I need a mission-driven teammate. Someone willing to leave it all on the field for the team. If you want to know what being on this #GTM is like, reach out to Seth Thompson, Ashley Early, Kassie Gilly, or Sarah Kuntsal. LFGroundswell. #hiring #job #enterprisesales #CSR #socialimpact #corporatesocialresponsibility https://lnkd.in/gP8GJZfx
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Peter Kuhn shared thisThere are really only three things that determine whether an LLM is actually useful in a GTM workflow: 1 - which model you use 2 - what prompt you give it 3 - what context you give it You’ll read 100 posts on LinkedIn and X talking about model quality, but that’s the one that matters the least. The best models are getting closer in quality, and they’ll all keep getting better. There are differences between them obviously, but switching from one model to another is usually a much smaller change than people make it out to be. Prompt matters more. A better prompt can absolutely produce a better answer. The model companies also know this, which is why what you type is often rewritten or expanded before the model actually responds. Sometimes that improves the result, sometimes it doesn’t. But context is the one I’d spend the most time on. Give a model one call transcript and ask, “How do I close this deal?” You’ll probably get a plausible answer. It’ll be relevant, well-formatted, and maybe even useful. But it only knows what happened on that one call. Now give the same model access to the rest of the deal: previous calls, emails, product usage, who has talked to whom, and the qualification facts your team cares about. You’re asking basically the same question, but the model has a much better picture of what’s actually happening. Building that is also much harder. You have to figure out which information matters, retrieve it reliably, and get it to the model in a form it can actually use. Getting that to work for one deal is manageable. Getting it to pull the right context every time across your whole pipeline is where it gets reaaally tough. If I were ranking the three today: context first, prompt second, model third.
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Peter Kuhn shared thisI worry that sales teams are getting wayyy more comfortable asking AI what to do next on a deal than I think they should be, given how much of that deal the AI may never have seen. Whatever model your team uses, it's piecing together the deal from what your systems captured: calls, emails, CRM data, and internal threads. If an important fact never makes it in, or gets attributed to the wrong person, or has changed since it was recorded, the model is still going to try to make sense of what it has, and this is where things get messy. - Maybe someone said “we have budget” in the transcript of your latest sales call, but if it’s not attributed correctly, you can’t tell whether it was the CFO or an intern who said it. - Maybe the buyer explained all of their constraints on a Teams call that never got recorded. - Maybe something was true three weeks ago and everyone inside the deal knows it changed, but the CRM still says otherwise. So before spending another minute debating which model to use, I'd look at how context is captured: what's actually being recorded, where the gaps are, and how fast stale information gets corrected. The teams getting good recommendations out of AI mostly just got this part right first.
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Peter Kuhn shared thisCEO: "We doubled the AI budget this year. Where are we seeing its impact?" CRO: "So good news and bad news. Good news is pipeline is up, reps have more qualified opportunities than last year. Bad news is revenue barely moved compared to what we're spending." CEO: "How is that possible?" CRO: "Because we don't win deals any more often than we used to. We close about 3 out of every 10. AI helped us get more deals to work, but we still lose 7 of every 10 we touch." CEO: "So what should we do differently?" CRO: "Honestly, I'd stop focusing on adding pipeline for a minute and work on how many of those deals we actually win." CEO: "Why? More pipeline means more revenue too." CRO: "It does, just less of it. Take one of our reps. Say they work 20 deals a year and close 6 of them. At $200K a deal, that's $1.2M." CEO: "Okay." CRO: "Option one: AI in prospecting gets them 5 more deals to work on, and they keep closing at the same rate. They end the year around $1.5M." CEO: "And option two?" CRO: "They work the same 20 deals, but instead of closing 6, they close 8. That's $1.6M. Two more wins out of deals we already had beat five extra deals at the top. And they’re still losing 12 of 20." CEO: "So you’re suggesting we pull AI budget out of prospecting?" CRO: "I'd keep it in prospecting, that part works. But I'd put the next dollar into helping close the deals we already have open. If the AI can see the calls and emails from a live deal, it can help the rep spot risk earlier and figure out the next move, which will make a big difference in how likely they are to close."
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Peter Kuhn posted thisI was just reading a Substack from Mark Roberge on how GTM teams can quantify the ROI of their AI layer. TL;DR: He argues the easiest place to see AI pay off is in how many deals each rep can handle at once, and that close rate and sales cycle are tougher because the buyer still has to compare vendors, line up stakeholders, find budget, get an exec to sign, etc. I agree with almost all of it. But I don't agree that those are buyer problems. I've worked with 500+ reps on enterprise deals and lining up the buyer's stakeholders is the rep's job: you map who influences whom, you write the one-page memo your champion forwards internally, you get the right people in the room before the big meeting. Finding budget is the rep's job too: discovery tells you whether it exists or has to be created, and from there you build the ROI case and the plan the exec signs off on. AI can help you draft all of that with one condition – it has to actually know the deal. Who the stakeholders are, what each of them said, where budget stands, what was promised. That's spread across calls, emails, and Slack threads, and if the model only sees one call summary, it will write you a very confident but very wrong stakeholder plan for something that doesn't exist. Most teams are getting that version right now and saying they’re “AI-enabled.” If the model has that context, the stakeholder work and the budget work get done earlier in the deal, and that shows up in your close rates. I'd measure the return there before adding more deals to each rep. I’ll link to the original piece in the comments!
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Peter Kuhn shared thisSales reps: your manager can tell you didn't read the deal review Claude wrote for you. They can tell because THEY had to read it. Clay wrote a whole company policy about this exact thing. It started with an engineer there who kept getting documents that took longer to read than they took to write since they were (clearly) done with AI and there was no real thought behind it. The policy was written for just her department, but the rest of the org adopted it too. The TL;DR is that: you own the thinking in whatever you send, AI or not. Writing is how you figure out what you think. When you write out a recommendation on a deal for your sales manager to review, you're forced to work through which facts matter, where you stand, and what you think the next best step is. That’s the job. Give that part to AI and you will move faster, but you’re skipping the thinking and the judgment. And then, that thinking gets pushed to your manager. Now they're reading YOUR five pages, figuring out what YOU actually mean, and checking whether YOUR recommendations make sense for the deal. The time Claude saved you shows up on their calendar instead, and I promise you they notice! I’m one of the biggest supporters of AI in B2B GTM. Nobody's asking you to hide that Claude helped. But you have to do the thinking and be able to stand behind what you send. #sales #b2bsales #gtm #AIsales
Peter Kuhn replied to a comment
1w
Mark Kosoglow it’s such an important point man. Because if you haven’t seen the rocket ship before (they fly straight up) like what’s happening with CO’s like Rillette right now, then you’re really on a glider.
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Peter Kuhn liked thisPeter Kuhn liked thisMeet Greenfield AI AI is transforming every part of go-to-market, from finding customers to closing business and growing long-term revenue. The teams that get this right will define how revenue teams operate. But AI tools and licenses alone won’t move revenue. That’s why we built Greenfield. Revenue teams bring us a number that won’t move. We find what’s holding it back, redesign how the work gets done, and build the AI workflows to move it. Getting this right takes five disciplines working together: - Strategy: finding the bottleneck that’s holding revenue back. - Data: giving AI the context to do useful work. - Tooling: choosing and connecting the right technology. - Expertise: understanding how revenue teams actually operate. - Execution: making it work in practice, every day. Joseph Zito and I have each spent 20 years carrying a revenue number. Neither of us has seen a bigger opportunity to change how revenue teams hit their number. We started work in July. Just three months later, we’re already at capacity. Thank you to the clients who trusted us early. Joe, I couldn’t ask for a better partner. LFG. We’re adding capacity for new clients in Q4. If you’re ready to put AI to work on your revenue number, let’s talk. greenfieldai.io
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Peter Kuhn liked thisLots of B2B SaaS GTM teams buy AI tools that listen to sales calls and tell the VP which deals are qualified. I’ve already seen multiple times that they will confirm things the buyers never actually confirmed. This is very problematic and can make you lose your next deal: A buyer saying "we'd probably find the money for this" and a buyer saying "I have $100K approved for this project" are not the same thing. One is confirming their budget. The other is suggesting they think they could potentially find it. Most of these tools will mark both as Budget: Confirmed, and the GTM team never notices because the answer goes straight to the CRM. Now your next step in this deal will be off and your pipeline forecast will be biased too. This is basically what qualification frameworks like BANT and MEDDIC are supposed to help with: separating what you actually know about a deal from what you're still assuming. When AI collapses that into a CRM field like “Budget: Confirmed,” you lose the distinction that matters. Give the rep the context instead: - What the buyer said: "we'd probably find the money." - What the CRM says: budget confirmed. - What we still need to learn: how much? Is it approved? Who controls the budget? Now the rep walks into the next call with a job. Get the number. Find out who owns it. That's the work that moves a deal forward instead of it sitting for a month because nobody realized the budget was still unverified. If you have ten minutes today, go try it on one deal. Take any opportunity marked "budget confirmed" and find the sentence in the transcript where the buyer actually confirmed it.
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Peter Kuhn liked thisBack in May, Deel shut down an AI customer service agent. It was supposed to handle the customer success workflow end to end, from client emails to internal messages. Deel's COO said it failed for three reasons: 1 - the compute wasn't fast enough 2 - the model wasn't capable enough 3 - the volume of context it needed (customer history, past tickets, account details) couldn't be pulled up and processed quickly enough to answer well. I’m not sure I fully agree with these. The first two are going to keep getting better on their own. Compute is getting faster and models keep getting more capable. It’s already night and day from three months ago. Give it another year, never mind five. But even if Deel rebuilt the agent with the models we have today, I’d bet it would still fail because of the third reason. If your agent needs to go fetch a bunch of customer context, put it into the prompt, and then figure out what matters, a better model doesn't solve how that information gets there in the first place. That gets slow and expensive fast, and there's no guarantee you pulled the right information. Better models can do more with the context you give them but you still have to get the right context there. Deciding what the model sees, when it sees it, and how that context gets packaged is part of the system you have to build. That's the part you own, and it's the same problem whether you're automating support or helping a rep prep for a call.
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Peter Kuhn liked thisI worked with an enterprise sales org closing six- and seven-figure deals, and their pipeline review was useless (the reps’ words btw). Here's how we rebuilt it: The review ran twice a week with fifteen people on the call. Each rep went deal by deal reading what they'd typed into Salesforce: stage, probability, close date, and one line for next steps. Nobody learned anything they couldn't have read themselves, and the CRO left each call without really getting context on their pipeline health or which deals were slipping. They'd tried to fix it once already by bringing their Gong recordings into Snowflake and querying them back out through Salesforce, but sellers said it wasn't giving them anything they needed and they stopped using it. We did two things: 1 - Made capturing the deal zero effort. Every call gets recorded and transcribed automatically, so no rep has to upload anything or write notes afterward. If they had to, most wouldn't, and whatever happened on that call would never make it into the system. The transcripts then get organized so that when someone asks about a deal, they get back the parts of its history that matter for that question. 2 - Made it useful for a rep's daily work before we need it in the meeting. That meant drafting follow-up emails from the call history and figuring out the next move on a stalled deal. The meeting only works if reps already use the system every day, and they only do that if it actually helps them every day. The pipeline review now runs on that. For each deal, the rep brings three sentences on what's happened and been decided, the open risk mapped against their qualification framework, and what they're working on over the next week to move it forward. The CRO either signs off or flags it for a deeper strategy session. She now calls it her operating system for the whole revenue org, and account management runs on it too.
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Peter Kuhn liked thisPeter Kuhn liked thisAI can help brands create more content, more quickly, for less money. But more creative doesn’t automatically mean better creative. My latest guest, Carly Crittenden, CRO at Vidmob, believes the industry is heading toward a reckoning as brands use AI to produce massive amounts of content without understanding why any of it works. In this week’s episode, Steve Olenski explores why data should inform creativity—not dictate it—and why human judgment, intentionality and trust matter more than ever. Read the newsletter first. Then listen to Steve’s full conversation with Carly.
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Peter Kuhn liked thisPeter Kuhn liked this96,000 volunteer hours. That’s the goal set by the team at Under Armour, a nod to their 30th anniversary of their 1996 founding. That’s a lot of hours. I was thrilled to join them in Baltimore to help kick it off… and in two capacities. First as chairman of one of their marquee funding and volunteer activation partner, Team Rubicon. And also representing Groundswell, which powers social impact across their global workforce. It’s always fun when those worlds collide. Plus, always fun to hang with Lynn Quayle, Kristen Ensor, and Flynn Burch! They might be the most fun team in the space! #csr #socialimpact #corporatesocialresponsibility
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Peter Kuhn liked thisNYC. Two days. Leaders from Product, Sales, Marketing and CS came together in one room. Our goal: To sharpen how we solve one of healthcare's most stubborn problems: Provider and Facility data you can actually trust. What stuck with me: - The pain is real. Wrong directories and outdated rosters cost health plans, health systems, and employers time, money, and patient trust. - Candor Health is built differently. Foundationally on AI-native data, not recycled claims, so it's accurate when it counts. The best is yet to come. What we mapped out for the roadmap has me genuinely excited about what we're delivering next. If provider data accuracy is on your mind, let's talk.
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