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Reported in Attention's case study: calls reaching level-three discovery went from under 10% to nearly 80%, across 425K calls scored in nine months. Dandy also reports about 35,000 hours of manual work saved and sellers ramping 2.5x faster.
By Jesse Davis (Dandy), in an Attention case study, Head of Sales, Dandy · Source ↗
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Reported in Attention's case study: calls reaching level-three discovery went from under 10% to nearly 80%, across 425K calls scored in nine months. Dandy also reports about 35,000 hours of manual work saved and sellers ramping 2.5x faster.
Most coaching debates "was that a good call?" with no shared definition. Jesse Davis at Dandy already had one, and that is what made the AI useful.
By Jesse Davis (Dandy), in an Attention case study, Head of Sales, Dandy · Source ↗
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Reported in Clay's case study: accounts first found through a website-visit signal win 50% more often (1.5x) than accounts that were not. Clay also filled 88% of the data gap left by Hex's previous provider. No time frame is given.
Pricing-page traffic from a target account is one of the clearest "call now" signals we get, and most teams still find out days later in a dashboard nobody opens.
By Bryanna Clancy and Dorothy Huynh (Hex), in a Clay case study, Marketing Ops and GTM Engineering Leader; GTM Engineer, Hex · Source ↗
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Reported in Clay's case study: top-tier enterprise accounts now close at a 22% higher rate and carry a 50% larger deal size than accounts that were scored wrong. No time frame is given.
I keep seeing teams pour hours into accounts that look A-tier on paper, then find out the score was built on bad industry and location data. That is the quiet leak.
By Owen Chandler (AlertMedia), in a Clay case study, GTM Engineer, AlertMedia · Source ↗
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Reported in Clay's case study: a ranked list of about 13,000 accounts was in Salesforce within two days. Clay-built lists helped identify over 55% of Agent Analytics revenue in its first quarter and drove two-thirds of its pipeline, and the launch beat its Q1 sales target by more than 200%.
Case study: Pendo (customer) and Clay (vendor). For a new product (Agent Analytics), Pendo's GTM engineer had AI agents read job postings, company websites and 10-K filings to find which companies were building their own AI agents and what they were building. Each account got a label (AI-native, AI-forward and so on) and a fit rank, and the list went into Salesforce as the one source of truth, so BDRs walked into calls knowing what the account was building.
By Zach Baumgarten (Pendo), in a Clay case study, GTM Engineer, Pendo · Source ↗
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Reported in Clay's case study: enterprise meetings booked up about 15%; response rates about 20% better on enriched accounts than on accounts that weren't; SDRs got 10+ hours back a week. No time frame is given.
Case study: Merge (customer) and Clay (vendor). Merge sells eight integration categories into every B2B industry, so the right pitch changes when an account changes. Its RevOps lead first tagged 50,000+ accounts with industry and a suggested use case, then built an agent that checks 1,500 enterprise accounts every week for product launches, feature releases, partnerships and org changes. When something relevant appears, the account owner gets an alert with the context, the product line that fits and a drafted email.
By Tim Bourcier (Merge), in a Clay case study, RevOps and Marketing Lead, Merge · Source ↗
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Reported in Clay's case study: 60% of Saviynt reps booked more qualified meetings, and pipeline grew by $32.2M. Account research that took up to an hour per account now happens in real time. No time frame or baseline is given.
Case study: Saviynt (customer) and Clay (vendor). Saviynt's ops team put website visits to key pages, new security hires and CRM stage changes into one enriched account list, then exposed it to the AI chat reps already use. A rep asks which accounts to work this week and gets a ranked answer with the reason, the account detail and the contacts to reach. Asking for contacts runs an ops-built lookup across many data providers that returns checked emails and phones. Ops decide which reps see which workflows and set usage limits.
By Zach Matek (Saviynt), in a Clay case study, Director of Marketing Operations, Saviynt · Source ↗
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Reported by HubSpot, comparing MQL sequences in Feb 2025 with Prospecting Agent in Feb 2026: meeting booking rate 8% to 20%, opportunity creation 9% to 65%, closed-won 4% to 62%. In Feb 2026 marketing enrolled 4,337 prospects and got 61 meetings and 23 opportunities.
Case study: RevenueWell (customer) and HubSpot (vendor). RevenueWell, dental software for 11,200+ practices, replaced its product-specific sequences with AI plays: when a practice researches a related topic or visits a product page, the contact is enrolled in that product's play and the SDR gets a task. Emails started in review-first mode, so SDRs checked the agent's drafts before anything sent. They ran two motions: intent and site-visit outbound, and demo requests that never booked.
By Jennifer Craig (RevenueWell), in a HubSpot case study, Senior Growth Systems Manager, RevenueWell · Source ↗
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Reported in Retool's case study: 3x higher meeting booking rate and 2x more opportunities created from the AI inbound BDR. Research that took a rep 10 to 15 minutes per lead takes seconds. A separate Lead Console lifted lead-to-opportunity conversion by more than 25%.
Case study: SafetyCulture (customer) and Retool (vendor). SafetyCulture gets about 500,000 free sign-ups a year and about 100,000 need sales attention, far more than BDRs can research. Its AI inbound BDR starts from a Salesforce lead, pulls the pages the person viewed before signing up, checks their job history for past use of SafetyCulture at another company, picks two customer examples that match their industry and country, writes the email and adds the lead to a Gong Engage flow.
By Josh Young (SafetyCulture), in a Retool customer story, Senior Manager, Marketing Technology, SafetyCulture · Source ↗
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Reported by LangChain (Dec 2025 to Mar 2026): lead-to-qualified-opportunity conversion up 250% and 3x more pipeline dollars in the same period. Follow-up on lower-intent leads up 97%, higher-intent leads up 18%. Reps reclaimed 40 hours a month each (1,320 hours across the team); 86% weekly active use.
Case study (the vendor's own team). LangChain's GTM agent triggers on every new Salesforce lead, first looks for reasons not to email (a fresh support ticket, a teammate who already reached out), then researches the CRM record, Gong calls, LinkedIn and the web and drops a draft in the rep's Slack with its reasoning and sources. The rep sends, edits or cancels. When a rep edits, an LLM compares the two versions and saves that rep's style notes, so the next draft starts closer to how they write.
By Vishnu Suresh and Jess Ou (LangChain), Engineer and GTM team, LangChain (vendor: LangChain; this is its own sales team) · Source ↗
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Reported by monday.com's head of AI for GTM: average response time to inbound leads went from 24+ hours to under 2 minutes, and conversion to meeting improved 25%. The agent handles over 2,000 leads a month in 7+ markets.
Oran Akron's RevAI team at monday.com built Amanda, an agent that calls inbound leads, has a real conversation, qualifies them and books a meeting with the right rep. He started with one RevOps person and one developer, picked this use case partly because a recorded call would win over leadership, and gave the agent more leads only when it matched or beat the humans.
By Oran Akron, VP of AI for GTM at monday.com (at the time) · Source ↗
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Reported by Monte Carlo: in a recent 30-day window, 323 agent runs across the GTM team cost $251 in total (about $0.78 a run), 29% of field sales and engineering ran at least one agent, and 56% of those came back and ran another.
Monte Carlo's GTM team built a hub where AEs, SDRs and CSMs run shared agents from the account or deal they are working: Account Research, Pre-Call Prep, Cold Outreach and Account Health Audit. Every run uses the rep's own login, reads the same data, logs what it did and its cost, and reps upvote the workflows that help.
By Dave Leyden, Jayne Tamboia and Hector Jarrin, Monte Carlo GTM team (blog authors) · Source ↗
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Reported by Sequel.io's VP of Marketing: the BDR who built the app had a 46% conversation-to-demo booking rate last quarter (46% of calls where he reached someone became a booked demo), against a 10-12% cold outbound benchmark their dialer vendor cited.
At Sequel.io, a BDR built his own Claude app that enriches trade show exhibitor lists against the company's ICP, divides the floor between him and the other BDRs, and tracks who each one has pitched. A second BDR built "Radar", an app that shows how hot each prospect is by how close it sits to the center of a radar screen. Their VP credits list quality, not better cold calling, for the result.
By Kathleen Booth, VP of Marketing at Sequel.io (her BDR team built the apps) · Source ↗
as reported
Reported by 30MPC's founder: 6,092 personalized cold emails reached a 1.9% reply rate by the end of the campaign, against the 1% benchmark he uses. He says 40% of the negative replies came from stale data a 100-contact test batch would have caught.
Armand Farrokh took a list of 224,000 contacts and used AI columns in Clay to filter out duplicates and keep only sales leaders with 10+ reps, at companies where 3+ reps had used 30MPC content. An AI prompt then drafted each email from four inputs. They sent from 20+ warmed-up domains.
By Armand Farrokh, Founder of 30 Minutes to President's Club (30MPC) · Source ↗
as reported
Reported: win rate went from 18% to 31% after the team moved from 1-2 generic follow-ups to a 7-touch personalized sequence. The post gives no time frame or deal count.
Peter San Pedro's team saw most deals die at the day-4 follow-up. They pulled 3 months of closed-lost deals, marked where each one dropped off, and gave Claude their offer and buyer profile to build a 7-touch sequence. Then they added objection-handling lines from their top closers. They did not change the offer or price.
By Peter San Pedro, Co-founder and COO, San Pedro Holding Company · Source ↗
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Reported: 14 hours a week back, 0 missed "interested" replies since go-live, and meetings booked in under 60 seconds, for about $20 a month in API calls. It took 3 days to build.
Cephas Princely built an 8-node n8n workflow that reads every reply to their cold outbound. An LLM tags each one by intent. Interested replies get a booking reply in under 60 seconds, "not now" replies get a follow-up queued for 7 days later, and sensitive replies go to Slack for a person. He shared the workflow file on GitHub.
By Cephas Princely, Co-founder, Aurallon (GTM systems consultancy) · Source ↗
as reported
Reported on the GTM AI Podcast: Sendoso went from 15+ BDRs to 4-5, and those 4 BDRs now generate over 30% of pipeline, which the host says is double what the 15 produced. A separate contract agent saves account managers about 45 minutes per renewal.
Sendoso first had AI copy the email output of its BDRs, then fed the AI its own data from Snowflake and Salesforce (closed-lost deals, past champions at new companies, product usage). Once AI emails beat human ones, BDRs stopped writing email and spent all their time on calls and warm replies. Austin Sandmeyer's advice is to start with one excited BDR.
By Kris Rudeegraap, Austin Sandmeyer and Egan Callahan, Co-CEO; Director of Growth and GTM Engineering; GTM Engineer at Sendoso, on the GTM AI Podcast · Source ↗
as reported
Reported: 45 minutes of manual Monday triage became a 2-minute read.
Kevin Payne set a Claude Cowork task to run at 6:30am every Monday. It reads his Notion deal tracker and account intel board, flags accounts that moved at least one 6sense buying stage in the last 7 days and have sequence coverage, scores them on a three-part rubric, and writes a briefing back to Notion: Act Today, Monitor This Week, Watch.
By Kevin Payne, GTM operator and newsletter writer (kevintpayne.substack.com) · Source ↗
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Reported for Cursor's 400+ person sales org: SDRs book 3x more qualified meetings and AE ramp time is down by 50%+. The source gives no time frame or baseline period.
Cursor's Head of Enterprise Growth, George Hou, built ChatGTM, an internal sales AI. Each morning reps get a Slack digest covering today's meetings, overnight account news and territory trends, plus 25 drafted outbound messages, each tied to a signal and paired with a personalized landing page. It also writes call briefs and recap emails, builds business cases, maps org charts, assembles the forecast and produces a stack-ranked plan for each new quarter's book. About 70% of use is outbound and account research.
By George Hou, Head of Enterprise Growth at Cursor (as described by Brendan J Short, The Signal) · Source ↗
as reported
Reported by SaaStr's CEO after about a quarter of the agent running quote-to-cash: invoices paid late fell from 56% to 8%, average days overdue fell from 17 to 6, and the longest overdue invoice fell from 51 days to 12.
Jason Lemkin's team found that 56% of SaaStr invoices were paid late while part-time contractors billed deals by hand. They gave their AI VP of Revenue agent the whole quote-to-cash job: read the signed contract, close the deal in Salesforce, create the invoice from the real terms, send it within minutes, check open invoices daily and pull in a human at 7 days past due. It ran on 3 supervised deals before going on its own.
By Jason Lemkin, Founder and CEO of SaaStr · Source ↗
as reported
Reported by SaaStr: about a month after the renewal agent started, renewals were 60% ahead of last year year to date, and the team had passed all of last year's renewal total in roughly three months.
SaaStr used to work renewals top down and spend time only on big accounts. Its renewal agent now contacts every customer at every deal size, turns results worth reporting (leads, impressions, coverage) into monthly touches, and builds each renewal deck in Gamma from real data. It also splits the proof by audience: a single slide for the CEO, the detail for the team that runs the program, and an honest fix-it story for accounts that underperformed.
By Jason Lemkin and Amelia Lerutte, SaaStr CEO and SaaStr's Chief AI Officer (The Agents podcast) · Source ↗
as reported
Reported by the founder of Pipeline: raw scraped lists land 50-60% ICP fit, while qualifying each company's website with an AI column first lands 90-95%. On one client build, contacts taken from companies' own websites needed 54.97 contacts per positive reply, against 94.94 for contacts from paid databases (4,553 public vs 3,621 paid contacts).
Christian, who runs the outbound agency Pipeline, starts by having Claude read 20 sales call transcripts and 10 closed-won accounts to write the ICP, the buying signals and a list of hard disqualifiers. Then every scraped company's website goes through an AI column checked against that ICP before any credits are spent on finding emails. For local markets he also pulls the contact details companies publish on their own sites.
By Christian (@coldemailchris), Founder and CEO of Pipeline, an outbound agency (per his X profile) · Source ↗
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Reported by CloudZero's VP of Finance and RevOps: the bot saves the GTM org 17-30 hours per week. It was built for about $500 in Claude tokens and costs tens of dollars a month to run.
At CloudZero, RevOps lead Miguel Valdez built CZBot, a Slack and Salesforce bot that pulls from six sources (including Salesforce, Gong and public Slack channels) and runs about a dozen background analyses every day. Reps use it to check deal and pipeline health, research prospects, prep for discovery calls, get feedback after calls, debrief wins and losses and find warm intro paths.
By Dan Carducci, VP of Finance and RevOps at CloudZero (describing a build by his teammate Miguel Valdez) · Source ↗
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Reported by SaaStr: adding Sumble as the second step after ZoomInfo recovers roughly 50% more people than the first pass found, before Clay cleans up what is left.
SaaStr has more than 500,000 people in its database and they change jobs constantly. Instead of picking one enrichment tool, it built a Claude skill that runs any list through three before it goes to an outbound agent: ZoomInfo first because its credits are cheapest, Sumble to catch who ZoomInfo missed, and Clay last for whatever is left because it is the most expensive.
By Jason Lemkin and Amelia Lerutte, SaaStr CEO and SaaStr's Chief AI Officer (The Agents podcast) · Source ↗
as reported
Reported by Vercel's COO: lead qualification went from a 10-person SDR team to about 1 person (plus 20% of a person for EU and APAC), at about $5,000 a year to run, with a 32x ROI. After the same method was applied to 30 SDR workflows, SDR quotas rose 30% that quarter.
Jeanne DeWitt Grosser had a GTM engineer sit with Vercel's best SDR and write down every tab she opened to qualify a lead. Each tab became one fixed step in an agent workflow. The agent ran in shadow mode for 6 weeks while that SDR checked every output, and a person still reviews the output; the agent does not act on its own.
By Jeanne DeWitt Grosser, COO at Vercel (her talk written up by Jason Lemkin on SaaStr) · Source ↗
as reported
Reported by Owner.com's CRO: built with about 2 weeks of one engineer's time. 15 BDRs now book 85% more opportunities, and the BDR team averages $100K+ in closed-won ARR per BDR per month.
Kyle Norton's team batch-researches every lead overnight and puts the notes inside Salesforce and Salesloft before the morning cold calls. Reps do not run any agents themselves, and a central data team owns list building. Kyle keeps chains of AI steps short and puts a person in the loop: for enterprise reps, a person picks which scored accounts go into outreach.
By Kyle Norton, CRO at Owner.com at the time (now CRO at Lassie), at SaaStr AI 2026 (written up by SaaStr) · Source ↗
as reported
Reported by Personio's CRO: expansion SDRs' account research went from about 2 hours a day to 15 minutes, and pipeline per FTE roughly doubled.
Philip Lacor's team mapped what reps do by hand (a GTM engineer shadowed account managers for 2 weeks), then a GTM engineer built an assistant inside Salesforce for expansion SDRs. A rep types an account name and it pulls data from 10 to 20 systems (usage, contracts, product analytics, Gong calls) and returns a cross-sell recommendation: green, yellow or red.
By Philip Lacor, CRO at Personio, at SaaStr AI London (written up by Jason Lemkin) · Source ↗
as reported
Reported: in Q2, 20% of their pipeline came from accounts the CRM had marked cold 30 days earlier, after new triggers fired on them. Claude Code tiered 110 companies in under a minute, about 80% less sorting by hand.
Kenny Damian writes an icp.md from the last 30 to 50 won deals, has Claude tier accounts against it and give each one a status (proceed, hold, exclude or suppress) with a reason, and checks a library of buying triggers every day. Because accounts are rescored daily, a cold account moves back up the list as soon as something new happens.
By Kenny Damian (@Kenny_GTM on X), Co-founder of Frontal, posting an article on X · Source ↗
as reported
Reported: no-shows on booked sales calls went from 25% to under 10%.
Cristian Frunze's cold email agency had 1 in 4 booked prospects miss their call. They added a short nurture between booking and the call, an AI voice agent that phones each prospect 1 hour before to confirm, and a text the morning of.
By Cristian Frunze, Founder of Ken (ken.so), a cold email agency, posting on LinkedIn · Source ↗
as reported
Reported: the old 5-step sequence had a 15% open rate and a 1.2% reply rate after 6 weeks. The rebuilt sequence reached a 6.8% reply rate in three weeks.
Jack Banister pasted a 5-step cold email sequence one of his BDRs had run for 6 weeks into Claude and asked how a cynical, time-poor VP of Sales would react to each step. Then his team rebuilt it with one rule: every email gives something before it asks for anything.
By Jack Banister, Managing Director at Kluvo, a UK outbound agency, posting on LinkedIn · Source ↗
as reported
Reported: an audit on Aug 4 found 25 of 43 qualified leads who booked a call had never reached his CRM, and 4 of those 25 paid him anyway. With Claude writing pre-call briefs, prep went from 45 minutes to 4.
An audit showed Neeraj Shah that most qualified leads who booked a call never reached his CRM. Now every booking becomes a CRM row with the intake answers, a stage moves only when the email is in his Sent folder, Claude drafts a four-step follow-up from his call notes, he sends each one himself, and a reply switches the templates off.
By Neeraj Shah, Founder and CEO of Titan Mastermindz, coach and solo seller, posting on LinkedIn · Source ↗
as reported
Reported for client UNIQ Supply (custom disposables manufacturer, 5 reps, 100k+ emails a month): 1,061 leads in 70 days, 86% qualified in HubSpot, 8 deals closed.
Instead of guessing an ICP, start from your product's volume: who goes through enough of it to need a supplier like you? Ask AI to list every business type that fits, then build lists for the odd ones nobody else is emailing. In his words: "This is how we found megachurches."
By Christian (@coldemailchris on X), Founder and CEO, Pipeline (@Pipeline_tech), cold email agency · Source ↗
as reported
Reported: "booked a vertical SaaS 9 calls in its first 6 days" and "53 meetings in 50 days". Day one booked 2 calls. info@ emails got a 5.6% reply rate and 6.12% interested. Week one: 1 interested per 576 emails; 800 dials per booked meeting.
Owners of 11 to 50 person local businesses are not in the usual databases. Get them from Google Maps, let AI clean names and find the owner, write a plain four-part email with no personalisation, and call the same list.
By Christian (@coldemailchris on X), Founder and CEO, Pipeline (@Pipeline_tech), cold email agency · Source ↗
as reported
Reported for one week: 60 contacts, 40% positive reply rate, 24 interested, 17 booked calls so far, about 2 hours of human time.
Feed Claude three real discovery-call transcripts, pull out the pains in the buyer's own words, and write the sequence with those words. Keep the list small and tight, and let Claude flag the positive replies so you only spend time on those.
By Ilya Azovtsev, SaaS growth advisor (LinkedIn headline: "I help with SaaS Growth") · Source ↗
as reported
Reported: "Last month: 20 calls, 6 new clients, 30% close rate. Not one from a purchased list."
Skip bought lists. Take the warm names you already have, write an ICP that rules people out, and have Claude score each one with the signals behind the score. Then send ten personal notes to the best.
By Neeraj Shah, Founder (LinkedIn) · Source ↗
as reported
Reported: "I use Claude to help me book like four to six outbound meetings every single week on top of my normal AE work", and he can "sequence 20 accounts and ... get everybody sequenced in like 90 minutes".
Every Friday he exports his CRM, drops it into a Claude Project with his process written as playbooks, and asks for his hottest accounts. He checks each pick, has Claude research them, verifies contacts, and sends the touches himself. At the end he has Claude update the playbooks so it doesn't repeat mistakes.
By Alex Murphy, Senior AE, 30 Minutes to President's Club · Source ↗
as reported
Reported: "When I first joined Just Works, our average time to ramp for a new sales hire was 18 months ... We've gotten it down to eight months and we have a pretty clear path to get it under six" (about 300 revenue-facing hires last year). Also reported: AE bookings up 28% last year, 44% over two years.
Her team records ride-alongs across roles, has Claude turn the notes into rep and buyer personas, checks them with the people they rode with, and uses a shared Claude skills library to build each launch's plan and training content. A human reviews everything, and new hires are released early when quizzes and AI role plays show they're ready.
By Lauren Hughes (interviewed by Kyle Norton), VP Revenue Effectiveness, Justworks · Source ↗
as reported
Reported: "our original call to decision-maker connect rate was like 3 to 4% ... if your BDRs now talk to 20 decision-makers a day and are booking those at like a 14 to 16% rate". High-scored leads "pick up at like a 2.3x rate". ACV about $10-12K.
Ask your best reps what tells them a deal will close, find those signals online, and enrich every account you can win today. AI does the pre-call research and hands the BDR just two or three things to say. Then test openers and call times, and back-test your call-scoring prompts on old calls.
By Kyle Norton (on Topline with Sam Jacobs, AJ Bruno and Asad Zaman), CRO, Owner.com · Source ↗
as reported
Reported: "I've closed $1,556,541 (thanks Claude), from deals I had initially lost ... ~16 months from closed lost to eventually take the customer to closed won but that's ~300K per year (for every year I've been at Clozd)".
Go back through calls on deals you lost, pull the problem each person described in their own words, and send each stakeholder a short, personal note asking if it ever got solved. No pitch.
By Austen Schaefer, Enterprise Account Executive III, Clozd · Source ↗
as reported
Reported: "$425K deal. 4 months in" and single-threaded; after the outreach the "CFO joined the final call. Closed 5 weeks later at $475K", larger than the original scope "because the VP of Ops added a use case."
When a big deal runs only through your champion, ask AI who the other likely stakeholders are and draft short intro emails that don't feel like you're going around her. Then loop the champion in.
By Idan Baum, Enterprise sales leader (third-party profiles list Senior Director, Enterprise & Strategic Sales, KnowBe4; ex-Slack, Salesforce, Bloomberg; not confirmed on his LinkedIn page) · Source ↗
as reported
Reported: agent worked ~1,000 warm leads left untouched in Salesforce: 72% open rate, 10%+ response rate, $2.7M in deals closed that reps never touched and $3.5M pipeline nurtured. Separately: 15% of SaaStr's London event revenue came from this ignored segment.
Your reps force-rank leads in their heads and the B leads (ghosted contacts, old event attendees, people who asked for something and never heard back) get nothing. Point AI there first: it uses the CRM history so each email reads like a colleague following up, and humans take over the moment someone replies with intent.
By Jason Lemkin (@jasonlk on X; SaaStr @saastr), CEO and founder, SaaStr · Source ↗
as reported
Reported for one event: 614 meetings booked at ~$85K average ticket, from ~2.25M sessions and ~402,000 interactions. In one month 71% of closed-won sponsorship deals came from AI-qualified inbound, vs a historic 29-34%. $1M+ closed-won sourced by the agent.
A form plus round robin took SaaStr 2 to 3 days to reach a buyer. An agent trained on each buyer type answers instantly, books the meeting, sends the rep a full context brief, routes to the rep who closes that deal type best, and follows up with people who drop off.
By Jason Lemkin (@jasonlk; SaaStr @saastr), CEO and founder, SaaStr · Source ↗
as reported
Reported after 30 days: account research time down 56%, outbound volume up 18%, meetings booked up 22%. Prep went from about 12 minutes to 3 minutes per account.
Most AI pilots "prove curiosity". Kill every idea but one, write a hypothesis with a number, get v1 live in 10 days inside the tool reps already use, track three outcomes only, and have managers ask about it in pipeline meetings. His acronym: PLAN (Prioritize one lever, Launch in weeks, Adoption in live workflows, Normalize only after ROI).
By Jake Dunlap (LinkedIn /in/jakedunlap), CEO, Skaled Consulting; host, AI-Powered Seller podcast · Source ↗
as reported
Reported: stalled pipeline from missed meetings fell from 39% to under 26% in two weeks; conversion on recovered deals nearly doubled; about $100K pipeline from this group in under a month.
Reps handle demo no-shows inconsistently: some chase, some ping twice and quit. Make the no-show itself trigger a same-day, AI-drafted note that the rep tweaks and sends, so nobody has to remember.
By Jake Dunlap (LinkedIn /in/jakedunlap), CEO, Skaled Consulting · Source ↗
as reported
From 297 of her own calls: on won calls, customers told stories 70% longer (117 seconds vs 69), asked 25% more questions, and her talk ratio was 9 points lower. Her win rate went from 38.7% in 2024 to 45.8% in 2025.
Nevena exported her call stats with outcomes, added a few full transcripts, and had Claude compare won vs lost calls and the old her vs the new her. The numbers show the trend; the transcripts show what she actually does differently.
By Nevena Tomovic, Lead Account Executive at Tenderly, posting on LinkedIn · Source ↗
as reported
25,763 companies checked and 14,710 kept as the market. 198 of 200 in the final spot check were correct (99%). 1,591 shops showed a hiring signal and 743 made the first campaign list. Tool cost was $230.
Database filters miss real customers and let in look-alikes. Jan’s team searched wide (every label their best customers use, ZIP code by ZIP code on Google Maps, plus manufacturer partner pages), removed duplicates, then had AI read every company’s website and answer one yes/no question with a quoted line of proof. Only after that did they look for signals and contacts.
By Jan Berning, Head of Growth and GTM engineer, writing an X article · Source ↗
as reported
Their AI-written personalized first lines got a 0.27% reply rate. A campaign built on one real detail from the prospect’s business (their project and location) got 14.5%.
Everyone has the same AI now, so prospects get dozens of cold emails a day with the same fake-personal opener. His team still lets AI draft every email, but a person checks every line, and the message is built around one true, specific fact about the prospect’s business instead of a generated compliment.
By Christian (@coldemailchris), founder and CEO of Pipeline, posting on X · Source ↗
as reported
In September his LinkedIn messages got 80x the response rate of his emails.
Give AI only verified facts about the buyer and what you can truly offer. It drafts a short connection note and a follow-up; you review them and send them yourself.
By Carson V. Heady, sales leader and author, posting on X · Source ↗
as reported
Over 4 months: Claude-written first lines got 6.8% replies (3.1% positive); templated problem-frame openers got 7.2% (3.4% positive). AI personalization cost 2 to 3 hours per 1,000 leads vs about 20 minutes per campaign for the template.
Before spending hours on AI-personalized openers, split-test them against one sharp problem-frame template. In his data the template won; his takeaway is better lists, not better openers.
By Jason Park, founder of Frostmailer (cold email agency), posting on X · Source ↗
as reported
AE win rate rose 8 points, from 29% to 37%, in under a year. 212 BDR transcripts were analyzed in about 3.5 minutes.
Pull won and lost call transcripts, have Claude find what winners do differently, turn it into one script, and make reps pass an AI role-play on it before they get leads.
By Marchelle Mooney, VP of Sales at Mangomint, interviewed on the GTM AI Podcast · Source ↗
as reported
ARR per rep 3x, average deal size up 40%, mid-market win rate doubled, selling time from about 25% to nearly 50%, and reps carrying about 40 deals instead of 20.
First ask each rep to walk you through yesterday and automate the non-selling work. Then have an AI agent draft short emails to the CFO or CEO on every live deal; reps check the facts and leaders send, so you hear no on day 3, not day 73.
By Ryan Milligan, CRO of QuotaPath, interviewed on the GTM Science podcast · Source ↗
as reported
Connect-to-conversation rate went from under 30% to over 60% (82% one week); 33 phone meetings booked over about 5 months.
After every connect that didn’t book, ask AI three questions about the transcript. Volume plus direction moved his numbers; he later scaled it into a team coaching agent.
By Won You, BDR, then Growth Lead at Great Question, posting on LinkedIn · Source ↗
as reported
Win rate went from 38% to 52% in about one quarter (the host says 53%) after training and daily scoring of end-of-call skills.
Score calls with AI against many skills and find which ones go with wins. For Owner.com, 10 of 12 were in the last 5 minutes of the call: pricing, value stack, next steps, asking for the business. Then train and coach one skill at a time.
By Kyle Norton, CRO of Owner.com, interviewed on the GTM Science podcast · Source ↗
as reported
From one call transcript: a scored MEDDIC scorecard, manager-style commentary, ranked next steps and a draft mutual action plan email in under 2 minutes. On one portfolio company’s call it caught that the rep heard who the real decision-maker was and never asked how to reach him.
Reps tend to fill in MEDDIC optimistically, and managers don’t have time to listen to every call. Stage 2 drops the transcript into Claude with a debrief skill that scores each element as Strong, Partial or Missing with evidence from the call, names the moment the seller should have pushed, and drafts the follow-up. Their short prompt: “Analyze this discovery call against MEDDIC and give me my next steps.”
By Mandy Cole, writing for Stage 2 Capital’s GTM newsletter · Source ↗
self-reported
10–12 meetings a month for a Series A cybersecurity client, from Claude reading the job postings of a 5,000+ account list. He says the same system books 30+ meetings a month for a software development firm.
He pulls every job posting since January 1, 2026 for a 5,000+ account list, and a Claude skill decodes each one: the tools named in the requirements, the pay, who the role reports to and what it’s meant to fix. Accounts get ranked on fit, movement and budget, and each comes with a draft first message that opens with what the hiring says, not with a pitch.
By Jainendra Ojha, outbound and GTM builder, posting on LinkedIn · Source ↗
self-reported
On one client account with 300,000+ emails sent, a single Claude pass over every campaign showed that pain-point openers and certain calls to action were behind most of the booked meetings. Nobody on the account knew until then.
Instead of guessing at new copy, he has Claude pull every campaign from the sending tool (the copy plus sent, replies and meetings booked), including the flops. Claude maps copy to outcomes with four questions, must cite the campaigns behind every claim, and turns what worked into 3 to 7 named frameworks. The next campaign tests one change at a time.
By Kenny Damian, Head of GTM at Frontal AI · Source ↗
as reported by the vendor
About 90 minutes a day saved from small scheduled tasks, including a pre-call brief for every customer meeting, plus about 3 hours a week on the Friday forecast, for a leader running a 4,000-account book.
Each morning a scheduled skill reads his calendar and builds a brief before every customer meeting, pulling spend data and pipeline status from Salesforce, so it’s waiting when he opens his laptop. He says the schedule mattered more than the skill: once prep runs on its own, he stops forgetting it.
By Travis Bryant, Head of US Mid-Market GTM at Anthropic · Source ↗
self-reported
Building a prospect-specific demo went from 3 to 4 days to 3 to 4 hours. Across his whole AI setup, his two-person team managed $31.8M in pipeline over seven months, tracked 199 demo meetings and closed $1.95M.
Every healthcare prospect had different systems, payers and pain points, so generic demos fell flat. He built a demo skill that takes the prospect’s context from discovery (specialty, systems, deal stage, the workflows they care about) and produces a tailored demo package plus a test checklist. If the demo can’t pass its own tests, he knows before the call, not in front of the CIO.
By Dan Mathieson, Director of Sales Engineering at Smarter Technologies (healthcare software) · Source ↗
self-reported
Proposal sent 2 hours after the discovery call instead of her usual 2 to 3 days. The prospect, who was comparing three agencies, replied that afternoon (“This is exactly what we need”) and signed by end of day.
She pulled the call recording into Claude and asked for every objection, pain point and quick win, plus the exact words the buyer used. Then she built an interactive proposal page mapping their challenges to her plan, with proof, pricing and timeline, and a comparison built from the objections they raised. She sent both with a short walkthrough video while the call was still fresh.
By Romana Kuts, at the B2B SaaS marketing agency SaaStorm · Source ↗
self-reported
A $490K deal closed at full price, with zero discount, 24 hours after he countered procurement’s 20%-off demand with a 3-year price lock and net-60 payment terms.
Procurement showed up at the end asking for 20% off, and the champion said they were “firm.” Before replying, he gave Claude the full deal history, the procurement contact’s public LinkedIn and the champion’s exact words, and asked whether this was a real budget limit or a tactic. The read: low internal pressure and no budget ceiling ever mentioned. So he offered cash-flow and budget certainty instead of price.
By Idan Baum, posting on LinkedIn · Source ↗
self-reported
450 companies cut to 73 in one afternoon (about 3 hours), with every remaining record given an owner, and two companies with open deals caught just before they would have been deleted.
His HubSpot had 450 companies, lots of duplicates and no owner on half of them. He connected Claude Code to HubSpot and worked through it in order: summarize the mess, merge duplicates, check deal links before flagging anything, fill gaps and assign owners, then delete the junk. Claude stopped him on two “dead” companies that still had open deals.
By Matteo Lombardi, founder of the consultancy Stratega · Source ↗
self-reported
12 meetings booked from LinkedIn in 7 days, from an account with under 1,100 followers.
Thirty minutes a week of raw input (voice clips on walks, sales call recordings, screenshots of posts that made her stop scrolling) goes into Claude Code, which already holds her positioning, buyer, objections and best past posts. It comes back as 10 ideas; she picks 4 or 5, writes for four different buyer stages, uses a useful giveaway to turn comments into conversations, and spends 30 minutes commenting on other people’s posts before each one goes out.
By Kanchan Bhatta, distribution and GTM, posting on X · Source ↗
self-reported lesson
After running AI outbound at about 1,000 emails a day, his company’s own invoices, password resets and deal confirmations started getting rejected by Gmail. Live emails were also citing things that never happened, like a prospect’s “recent migration to Salesforce.”
The first month looked great: lots of replies. Then a customer asked why they’d never been billed. The outbound ran from the same domain as their invoices, and once spam complaints climbed, Gmail started turning that domain away. When he finally read the live sent emails (not the approved samples), he found confident, perfectly written claims that weren’t true.
By Ashutosh Singhal, founder of Veriprajna, writing on Substack · Source ↗
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