--- title: "The Real Problem with Sales Today" episode: 64 podcast: "The LeanScale Podcast" publisher: "LeanScale" guest: "Robert Moseley" guest_title: "CEO & Founder, GTM Engine" date_published: 2026-05-15 date_modified: 2026-07-22 duration: 00:44:54 word_count: 8580 topics: ["revenue-operations", "ai-in-gtm", "forecasting", "gtm-strategy", "demand-generation", "outbound-sales"] canonical_url: https://leanscale-knowledge-hub.netlify.app/podcast/robert-moseley-real-problem-with-sales/ source: "LeanScale Podcast Knowledge Hub — https://leanscale-knowledge-hub.netlify.app" license: "Free to quote and cite with attribution to The LeanScale Podcast." --- # The Real Problem with Sales Today _Robert Moseley on why CRMs break, and how AI removes humans from the data_ **Episode 64 · The LeanScale Podcast** Robert Moseley, CEO & Founder, GTM Engine · Hosted by Anthony Enrico Published May 15, 2026 · Updated July 22, 2026 · 00:44:54 Canonical: https://leanscale-knowledge-hub.netlify.app/podcast/robert-moseley-real-problem-with-sales/ **Topics:** Revenue Operations · AI in GTM · Forecasting · GTM Strategy · Demand Generation · Outbound & Sales Development ## Executive summary Most go-to-market strategies don't fail for lack of effort — they fail because the system underneath them is broken. Robert Moseley, CEO and founder of GTM Engine, has watched that happen from the inside more times than almost anyone. This is his ninth startup and the first he's founded; before it he was a software engineer who caught the startup bug at Jobs2Web, then spent roughly 17 years as one of the first go-to-market hires again and again — at Moveworks (later acquired by ServiceNow for about $3 billion), Harvey, and Copy.ai. Each time he stood up the revenue systems from scratch, and each time he watched them fall apart as the team scaled. His conclusion is the thesis of the whole episode: process doesn't break because it was designed badly. It breaks because it depends on people, and there's no honest way to enforce it. The villain in Robert's story is the modern CRM. Salesforce, he argues, is a reporting and dashboarding tool built for RevOps and leadership — not a tool built to help a salesperson actually sell. Reps spend roughly 20% of their time on data entry, do it poorly, and produce data nobody trusts; they get 'yelled at every Wednesday' before the forecast call, update just enough to unlock a stage change, and do their real work in other tools. The 100 custom fields on the opportunity object go unfilled, opportunity contact roles go unused, and leadership can't even pull a reliable list of who its customers are — 'the C in CRM stands for customer.' GTM Engine's answer is to remove humans from the job entirely: continuously listen to every call, email, and touchpoint, extract the structured data (use cases, buying process, budget cycle, people involved, competitors mentioned), create and enrich the records, and two-way sync it all back to Salesforce or HubSpot. The middle of the conversation is a live product walkthrough that doubles as a philosophy of selling. Once reps stop producing data, they become consumers of it: a pipeline view that separates the healthy deals from the yellow ones, a color-coded calendar that flags every meeting you never followed up on, and a two-dimensional deal-health map that splits 'will it close' from 'will it close on time.' A 'Genie' agent — full reasoning, wired to every captured touchpoint plus external research tools — can build a custom ROI calculator to a prospect's own metrics, pull industry benchmarks, and draft the email to the decision-maker, work that would take a rep hours if they attempted it at all. A promoter score (-10 to +10) turns contacts into a filterable, shareable list of champions for marketing, with the exact quotes attached. From there the discussion widens into the state of go-to-market itself. Robert's sharpest claim is that AI has 'decimated every single lead generation channel' — you can't buy Salesloft and blast sequences, and cold calling is dead — so the only durable edge left is messaging: describing your buyer's pain in the actual words of your ICP, which is exactly the language GTM Engine harvests from real conversations. He and Anthony trade war stories about the marketing-to-sales disconnect (Moveworks marketing sold 'Worktopia' employee experience while IT service-desk managers only cared about resolving tickets), why product teams should listen to the field instead of their locked-in existing customers ('go where the puck is going'), and why the market is shifting toward external, fractional RevOps. The closing exchange is the LeanScale angle made explicit: RevOps is high-complexity but low-variability — a Series B sales-led company has the same problems and the same fixes as its peers — which is precisely why it outsources well, while sales and product must stay in-house as the heart of the company. Who should listen: founders designing their go-to-market from day one, RevOps leaders drowning in CRM hygiene, sales leaders who want to coach from objective data instead of interrogating their reps, and anyone trying to understand why 'more data' has never equaled better selling. ## Key takeaways 1. **Go-to-market systems break because of people, not bad design** — Robert has stood up revenue systems from scratch at company after company and watched every one fall apart at scale. His conclusion: you can put the best process in place ever, but if there's no legitimate way to enforce it, it will break. The failure point is human compliance, not the blueprint. _Why it matters:_ Stop trying to fix adoption with more mandatory fields and stage-gate warnings. The durable fix is to remove humans from the data-capture loop so the process doesn't depend on discipline at all. _For:_ RevOps Leaders, Founders, Sales Leaders 2. **Salespeople should never be doing data entry** — Reps spend roughly 20% of their time inputting CRM data and do it poorly — they're hired to be in front of people, not to type. Even when they comply, the data isn't trusted because it isn't their job. Automating the capture hands 10–20% of selling time back to the field. _Why it matters:_ Treat CRM data entry as a cost to eliminate, not a behavior to coach. The ROI shows up twice: better data for the company and more selling hours for the rep. _For:_ Sales Leaders, RevOps Leaders, Founders 3. **The CRM is a reporting tool, not a selling tool** — Salesforce is built for RevOps and leadership to see dashboards, forecasts, and pipeline health — not for a salesperson to actually sell. Reps go in only to update fields before the Wednesday forecast call and do their real work elsewhere. GTM Engine flips reps from producers of data into consumers of it. _Why it matters:_ Design the seller's surface separately from the system of record. If the interface exists to input data and the rep no longer inputs data, rebuild it to serve their next action, not leadership's report. _For:_ RevOps Leaders, Sales Leaders 4. **Fix the data model first — records, relationships, and roles** — Getting fields populated isn't enough; the relationships have to be right. Who is the champion, who's the blocker, what role is each contact playing in procurement — that lives in the rep's head, not in Salesforce's opportunity contact role object, which almost nobody uses. Companies often can't even produce a reliable list of their own customers. _Why it matters:_ Auto-create a contact record for every person on an opportunity, classify their role, and enrich it. Everything downstream — coaching, handoffs, reporting, marketing lists — depends on getting this foundation right first. _For:_ RevOps Leaders, Sales Leaders 5. **Deal health is two-dimensional: 'will it close' vs. 'will it close on time'** — GTM Engine plots opportunities on a two-axis map — the further right, the healthier; the further up, the more likely to close when the rep expects. That yields safe bets, acceleration opportunities (real deals slipping to next quarter), and a firm-decision-date-but-may-not-pick-us category worth fighting for. _Why it matters:_ Separate the two questions when you review pipeline. To pull in your number, hunt for deals just below the 'this-quarter' line early enough to accelerate them — not on the last day of the quarter when you can't do anything. _For:_ Sales Leaders, RevOps Leaders, Revenue Executives 6. **Coach from objective data instead of interrogating reps** — Managers burn their time asking 'what's going on in this opportunity?' one deal at a time and never actually coach. With health scores, win rates, and coverage benchmarked against peers, you start from objective truth — where a rep is strong and weak — and coach from there. _Why it matters:_ Build the manager's cadence around performance signal, not status extraction. Reps, especially the good ones, want to self-coach and know where they stack up in a competitive room. _For:_ Sales Leaders, RevOps Leaders 7. **Pipeline coverage without conversion is just burning leads** — SDRs promoted to AE are great at generating pipeline — it's all they've done — but often can't manage a complex deal cycle they've never run. You can build 1,000% coverage, but if your conversion is a quarter of everyone else's, it doesn't matter; you're incinerating leads. _Why it matters:_ Diagnose reps on conversion and deal management, not just pipeline generation. The fix for a low win rate isn't more top-of-funnel; it's the deal skills the number is hiding. _For:_ Sales Leaders, RevOps Leaders 8. **AI agents work only when they have context, not just reasoning** — The 'Genie' agent can build a custom ROI calculator to a prospect's own sales metrics and growth targets because every touchpoint — team size, projected size, pricing discussed, how the CFO will account for value — has already been captured and associated with the record. A rep with all the skills but none of the assumptions still can't do the work correctly. _Why it matters:_ The unlock for GTM agents is the underlying unified data model, not the model's IQ. Invest in continuous, associated context capture before expecting agents to execute real deal work. _For:_ RevOps Leaders, Founders, Revenue Executives 9. **AI has decimated the old lead-gen channels — messaging is the new moat** — You can't buy Salesloft and blast sequences anymore, and cold calling is dead; AI has flattened every channel that used to work on volume. The way back is old-fashioned marketing: talk about the pain your product solves in the actual words of your ICP, and you resonate more than anyone. _Why it matters:_ Stop hiring SDRs to manage templates or AI-written sequences. Mine real conversations for your buyers' language and get your messaging dialed in — it's the highest-leverage way to win in 2026. _For:_ Marketing Leaders, Founders, Sales Leaders 10. **Product should listen to the field, not just to locked-in customers** — Sales talks to ICPs every single day; product too often over-indexes on what customer success and its biggest existing customers say — and those customers are biased because their problem already feels solved. To 'go where the puck is going,' listen to what the market is telling the field, not only where demand is today. _Why it matters:_ Wire product's roadmap signal to live sales conversations — use cases, friction, competitors mentioned and the use cases behind them — not just CS tickets and analytics. It's the least biased, most forward-looking feedback you have. _For:_ Founders, Marketing Leaders, Revenue Executives 11. **The marketing-to-sales disconnect starts with not hearing the customer** — At Moveworks, marketing pushed 'Worktopia' employee-experience messaging while IT service-desk managers cared about exactly one thing — resolving tickets while drowning in them. The SDRs who won simply offered to analyze a prospect's ticket data and show how many they could resolve. Sales never got leads from that marketing. _Why it matters:_ Close the loop by handing marketing a live list of what real buyers actually say — with quotes — so campaigns speak the customer's language instead of the company's aspiration. _For:_ Marketing Leaders, RevOps Leaders, Sales Leaders 12. **RevOps is high-complexity, low-variability — which is why it outsources well** — A Series B sales-led company faces essentially the same problems as its peers, and the same recommendations tend to fit. Internal RevOps is one of the hardest jobs — you inherit a 20-year-old Salesforce you can't touch without breaking something, and have no time to research new tools. Sales and product, by contrast, are the heart of the company and must stay in-house early. _Why it matters:_ Outsource the function whose answers transfer across companies and where market-facing tool knowledge is the edge; keep the functions you can't outsource the accountability for. Channel-only motions are for behemoths, not early-stage. _For:_ Founders, RevOps Leaders, Revenue Executives ## Frameworks ### Remove Humans to Enforce Process (02:05) **Definition:** Since go-to-market process breaks whenever it depends on human compliance, the fix isn't more enforcement (mandatory fields, stage gates) but removing people from the data-capture loop entirely — letting AI listen and populate the system automatically. Robert's core operating belief after ~17 years: 'You can put the best process in place ever, but if there's no way to legitimately enforce that process, it's going to break.' Enforcement via required fields backfires, producing untrusted data reps invent to unblock themselves. ### Reps as Consumers of Data, Not Producers (10:26) **Definition:** Once AI captures CRM data automatically, the salesperson stops being a producer of data (data entry) and becomes a consumer of it — served a prioritized view of what's healthy, what's slipping, and what to work on next. This reframes the seller's software surface: the input-oriented CRM UI no longer makes sense, so the interface should present pipeline health, follow-up gaps, and next actions rather than forms to fill out. ### Crawl, Walk, Run Rollout (08:03) **Definition:** Adopt in stages: crawl (RevOps connects CRM, call recorder, Slack/Teams, and email, sets team structure and field mappings for a two-way sync), walk (auto-create and enrich records, remove humans from data entry), run (deal-health analysis, agents, and cross-functional data products). Robert recommends starting with the RevOps 'boring stuff' of connecting systems so the background sync and record creation are trustworthy before layering on scoring and agent-driven execution. ### The Two-Dimensional Deal-Health Map (17:33) **Definition:** Plot every opportunity on two axes: horizontal = how healthy the deal is (likelihood to win), vertical = how likely it is to close when the rep expects. The quadrants surface safe bets, acceleration opportunities (will close but not this quarter), and firm-decision-date deals where you may not be selected. Separating 'will it close' from 'will it close on time' tells a rep where to look first to pull in the number — near the this-quarter line, early enough to accelerate — instead of discovering slippage on the last day of the quarter. ### The Genie Agent (Context-Grounded Execution) (23:27) **Definition:** A full-reasoning agent wired to every captured touchpoint plus external research tools that executes deal tasks — building a custom ROI calculator to the prospect's own metrics, pulling industry benchmarks, and drafting the decision-maker email. The agent works because the unified data model already holds current and projected team size, discussed pricing, decision-makers, pain points, and how the buyer will evaluate ROI. Reasoning without that context can't do the work correctly. ### Promoter Score (32:27) **Definition:** A per-contact score from -10 to +10 that identifies champions and blockers at a glance, with the reasons and specific quotes behind each. Filtering contacts by ICP persona and a high promoter score produces a live, shareable list of advocates. Instead of marketing chasing salespeople for a case-study contact, the score yields a real, filterable list of who loves the product, their persona, and exactly why — routed to the record owner for a warm introduction. ### Listen to the Field, Not Just Customers (Go Where the Puck Is Going) (37:56) **Definition:** Product roadmap signal should come from live sales conversations with the market — use cases, friction, competitors mentioned — not primarily from customer success and existing customers, who are biased because their problem already feels solved. Robert invokes the Wayne Gretzky line: talking to current customers tells you where the puck is now; listening to what the field hears tells you where it's going. Sales is the one function in daily contact with ICPs. ### High Complexity, Low Variability (41:17) **Definition:** RevOps problems are hard to solve but remarkably consistent across similar-stage companies — a Series B sales-led company has the same problems and the same fixes as its peers — which is why the function outsources well while sales and product must stay in-house. Because the right answer is complex to find but transfers across companies, an outside operator who has seen it many times is more valuable than an internal hire; the heart-of-the-company functions where accountability can't be outsourced are the exceptions. ## Quotes _Speakers inferred from an undiarized transcript — verify before attributing._ > "You can put the best process in place ever, but if there's no way to legitimately enforce that process, it's going to break." > > — Robert Moseley, The LeanScale Podcast Ep. 64 (02:05) > "They're salespeople. They're supposed to be selling, not doing data entry. They spend like 20% of their time doing this stuff, just to do it poorly." > > — Robert Moseley, The LeanScale Podcast Ep. 64 (03:20) > "The C in CRM stands for customer, but you can't even give a reliable list of who your customers actually are, what they own, how much they're using it, who's using it. Can't do that." > > — Robert Moseley, The LeanScale Podcast Ep. 64 (06:57) > "Tools like Salesforce aren't really built for salespeople. They're built for reporting and dashboarding and leadership, but they're not built for salespeople to actually sell." > > — Robert Moseley, The LeanScale Podcast Ep. 64 (09:55) > "They become consumers of data instead of producers of data." > > — Robert Moseley, The LeanScale Podcast Ep. 64 (10:26) > "I 1000% agree Salesforce is not built for salespeople. I think it's built for RevOps more than anyone." > > — Anthony Enrico, The LeanScale Podcast Ep. 64 (13:51) > "You can build 1,000% pipeline coverage, but if your conversion rate is a quarter of everybody else, that doesn't really matter. You're just burning through all your leads." > > — Robert Moseley, The LeanScale Podcast Ep. 64 (16:10) > "They spend so much time figuring out what's going to close that they have no time to actually impact what's going to close." > > — Robert Moseley, The LeanScale Podcast Ep. 64 (22:23) > "As much as salespeople like to think we can control the process, buyers control the process." > > — Robert Moseley, The LeanScale Podcast Ep. 64 (24:03) > "AI has killed that. It's completely decimated every single lead generation channel that we've used before." > > — Robert Moseley, The LeanScale Podcast Ep. 64 (35:58) > "You've got to go back to good old-fashioned marketing. If you're the one who can talk about the pain that your product solves the best, using the words of your actual ICP, you're going to resonate the most. That's how you win." > > — Robert Moseley, The LeanScale Podcast Ep. 64 (35:58) > "It's like a mix of Mad Men with data. Pour yourself a whiskey, get all the AI-gathered data, and then make something beautiful happen." > > — Anthony Enrico, The LeanScale Podcast Ep. 64 (36:43) > "Go to where the puck is going, not where it's at right now. If you talk to your customers, you're where it's at right now. If you listen to what the field is telling you, then you can start to build towards where the puck is going." > > — Robert Moseley, The LeanScale Podcast Ep. 64 (37:56) > "Some of the hardest jobs in the world are being an internal RevOps person, where it's like, hey, here's our Salesforce implementation, it's been here for 20 years, don't touch it — because if you do, something's going to break." > > — Robert Moseley, The LeanScale Podcast Ep. 64 (39:17) > "It's high complexity, but actually pretty low variability between companies. All the problems are the same, and most of the solutions are the same." > > — Robert Moseley, The LeanScale Podcast Ep. 64 (41:17) > "Sales and product, those really have to be internal. That's the heart of the company. You can't outsource the accountability." > > — Robert Moseley, The LeanScale Podcast Ep. 64 (42:33) ## Practical advice by role ### Founders - Design your go-to-market data model from day one — you'll be doing a lot of the early selling yourself, so build the system to capture context automatically rather than relying on discipline you won't have at scale. - Keep sales and product in-house early; they're the heart of the company and you can't outsource the accountability. RevOps, whose problems are consistent across similar-stage companies, is a much stronger outsourcing candidate. - Don't rebuild the old volume playbook — cold calling and mass sequences are dead. Invest early in messaging grounded in your ICP's actual words, because your product also has to be genuinely good for great selling to matter. ### RevOps Leaders - Stop enforcing data entry with mandatory fields and stage-gate warnings — it produces data reps invent to unblock themselves. Remove humans from capture so the process no longer depends on compliance. - Fix the data model first: auto-create a contact record for every person on an opportunity, classify their procurement role, populate opportunity contact roles, and set a trustworthy two-way sync before layering on scoring and agents. - Roll out crawl-walk-run — connect CRM, call recorder, Slack/Teams, and email; automate record creation and enrichment; then turn on deal-health analysis and agent-driven execution. - Automate CRM hygiene: enforce relationships and ownership, and reassign a departing rep's records automatically instead of letting them rot. ### Sales Leaders - Coach from objective data — health scores, win rates, and coverage benchmarked against peers — instead of interrogating reps deal-by-deal about status you could read from the system. - Review pipeline on two axes: separate 'will it close' from 'will it close on time,' and hunt for deals just below the this-quarter line early enough to accelerate them. - Diagnose SDR-turned-AE reps on conversion, not just pipeline generation; 1,000% coverage with a quarter of the win rate is burning leads, not building a business. - Free your best sellers to be 'field generals' — put their skill into impacting deals, not into hand-populating a CRM. ### Marketing Leaders - Get your messaging from the field: mine real sales conversations for the pain and language your ICP actually uses, rather than pushing aspirational company narratives buyers don't care about (the 'Worktopia' trap). - Ask RevOps for a live, filterable list of champions — high promoter-score contacts by ICP persona, with quotes — to source case studies and testimonials instead of guessing which customers to chase. - Treat sales conversations as your least-biased product and market signal; existing customers feel their problem is already solved and give the least critical feedback. ## AI takeaways **Thesis:** AI has finally gotten good enough to remove humans from CRM data entry — continuously listening to every call and email, extracting structured fields, creating and enriching records, and syncing them back — which converts the CRM from an untrusted reporting tool into a trustworthy, agent-ready data model. In the same breath, AI has decimated the legacy volume channels, so the winning GTM move shifts from more sequences to sharper messaging drawn from the actual words of your ICP. - **Continuous capture beats summaries** — Everyone has a call recorder that summarizes a call. The step change is listening to every touchpoint on every opportunity and extracting the specific fields leadership actually wants — use cases, buying process, budget cycle, people involved, competitors and the use cases they came up under. - **Remove humans from the process** — Process breaks on human compliance and enforcement produces junk data. The fix is to take people out of data capture entirely, then two-way sync the structured result back to Salesforce or HubSpot. - **Agents need context, not just reasoning** — The Genie agent can build a custom ROI calculator and draft the decision-maker email only because current/projected team size, discussed pricing, decision-makers, and how the buyer evaluates ROI are already captured and associated with the record. - **AI killed the volume playbook** — Cold calling is dead and you can't buy Salesloft and blast sequences; AI has flattened every legacy lead-gen channel. Messaging in your ICP's real language — which AI can now extract from conversations — is the durable edge. - **Scale by fanning out agents** — GTM Engine spins up 50–60 agents to process a single opportunity from different angles — a pattern for turning unstructured conversation into a structured, queryable data model at speed. **Agent & automation ideas** - A continuous CRM-population agent that listens to calls and emails, creates and enriches records, writes structured fields and opportunity contact roles, and two-way syncs to Salesforce/HubSpot in the background. - A deal-health agent that plots each opportunity on health and close-timing axes, tracks health/interest scores over time, and auto-adjusts the forecast close date the moment a slippage email arrives. - A follow-up watchdog on the calendar and inbox that flags every meeting with no follow-up and every unanswered opportunity email, then drafts the response for one-click send. - A champion-list agent that scores contacts (promoter score), filters by ICP persona, and hands marketing a live, quote-backed list of advocates for case studies and testimonials. - A deal-execution agent (ROI calculators, external benchmark research, deliverable drafting) grounded in the full captured context of the opportunity, routed back to the record owner. ## Operations takeaways ### Revenue operations - **Enforcement fails.** Mandatory fields and stage-gate warnings produce untrusted data reps invent to unblock themselves; remove humans from capture instead of enforcing compliance. - **Data model before dashboards.** Create and enrich a contact record for every person on a deal, classify procurement roles, and populate opportunity contact roles — the foundation everything downstream depends on. - **Two-way sync, run in the background.** Connect CRM, call recorder, Slack/Teams, and email, map fields, and let record creation and field updates run automatically without rep involvement. - **Automate hygiene and ownership.** Auto-enforce relationships and ownership and reassign a departing rep's records automatically rather than letting orphaned records rot. - **The internal RevOps trap.** Inheriting a 20-year-old Salesforce you can't touch, with no time to research tools, is why the market is shifting toward external, cutting-edge operators. ### Pipeline & marketing ops - **Two-dimensional health.** Separate whether a deal will close from whether it will close on time; the map exposes safe bets, acceleration opportunities, and firm-decision-date deals you might lose. - **Pull-in strategy.** To hit the number, look first at deals just below the this-quarter line, early enough in the quarter to actually accelerate them. - **Interest ≠ health.** A contact can be highly interested but have no budget; scoring interest and health separately keeps forecasts honest. - **Live close-date adjustment.** An inbound 'we didn't get budget, let's touch base in February' email auto-moves the forecast close date and drops the health score in real time. - **Coverage isn't conversion.** 1,000% pipeline coverage with a quarter of the win rate is burning leads; diagnose reps on deal management, not just pipeline generation. ## Metrics mentioned | Value | Metric | Context | | --- | --- | --- | | ~20% | Rep time on CRM data entry | Salespeople spend roughly a fifth of their time on data entry and do it poorly; automating it hands 10–20% of selling time back. | | 9th (first founded) | Robert's startups | GTM Engine is Robert's ninth startup and the first he has founded, across ~17 years close to go-to-market. | | ~$3B to ServiceNow | Moveworks exit | Robert was among the first GTM hires at Moveworks, later acquired by ServiceNow for roughly $3 billion. | | -10 to +10 | Promoter score range | Per-contact score identifying champions vs. blockers, with reasons and specific quotes attached. | | 50–60 | Agents per opportunity | GTM Engine spins up 50–60 agents to process an opportunity's data from different angles. | | 30–60 min | Implementation time | Setup is mostly field mapping; the system then processes and syncs in the background. | | 1,000% coverage, ¼ win rate | Coverage vs. conversion | Illustration that an SDR-turned-AE can generate huge pipeline yet burn leads with a low conversion rate. | | 12–20 hrs/week | Admin time (Gartner, cited in demo) | A benchmark surfaced by the Genie agent's research step: reps spend 40–50% of a 40-hour week on manual/legacy admin work. | ## Entities mentioned - **GTM Engine** (company) — Robert Moseley's company; the AI platform he demos that continuously captures call, email, and touchpoint data, auto-populates the CRM, scores deal health, and runs a deal-execution agent. Try it at GTMEngine.ai. · https://leanscale-knowledge-hub.netlify.app/company/gtm-engine/ - **Jobs2Web** (company) — The startup where Robert began his career as a software engineer out of college; its acquisition gave him his first exit and hooked him on startups. · https://leanscale-knowledge-hub.netlify.app/company/jobs2web/ - **Moveworks** (company) — Where Robert was among the first go-to-market hires, selling to IT service-desk managers; his example of the marketing-to-sales disconnect ('Worktopia' vs. resolving tickets). Later acquired by ServiceNow for roughly $3B. · https://leanscale-knowledge-hub.netlify.app/company/moveworks/ - **Harvey** (company) — Named alongside Moveworks and Copy.ai as a company where Robert was in the first group of go-to-market hires. · https://leanscale-knowledge-hub.netlify.app/company/harvey/ - **Copy.ai** (company) — Cited as another early-stage company where Robert was among the first go-to-market hires setting up systems from scratch. · https://leanscale-knowledge-hub.netlify.app/company/copy-ai/ - **ServiceNow** (company) — Acquired Moveworks for roughly $3B — the reason Robert felt free to use Moveworks as his marketing-disconnect example. · https://leanscale-knowledge-hub.netlify.app/company/servicenow/ - **LeanScale** (company) — Anthony's firm; started in 2021 when there were no RevOps agencies, now facing competitors. Its R&D comes largely from customer engagements rather than standalone research. · https://leanscale-knowledge-hub.netlify.app/company/leanscale/ - **Nutanix** (company) — Cited as the kind of large 'behemoth' that can go to market purely through channel — a motion Robert argues does not work for early-stage companies. · https://leanscale-knowledge-hub.netlify.app/company/nutanix/ - **Robert Moseley** (person, guest) — CEO & founder of GTM Engine; ~17 years as a first go-to-market hire at Moveworks, Harvey, and Copy.ai after starting as a software engineer. · https://leanscale-knowledge-hub.netlify.app/guest/robert-moseley/ - **Anthony Enrico** (person, host) — Co-founder of LeanScale and host of The LeanScale Podcast. · https://leanscale-knowledge-hub.netlify.app/guest/anthony-enrico/ - **Salesforce** (tool, CRM) — The CRM Robert critiques throughout: a reporting/dashboarding system built for RevOps and leadership, not sellers; opportunity contact roles go unused and it can't reliably list customers. GTM Engine two-way syncs structured data back into it. - **HubSpot** (tool, CRM) — Named alongside Salesforce as the CRM GTM Engine two-way syncs extracted, structured data back into. - **Salesloft** (tool, Sales Engagement) — Cited as the emblem of the dead volume playbook — 'you can't just buy Salesloft and blast out sequences anymore.' - **Slack** (tool, Team Messaging) — Named with Teams as a communication system GTM Engine connects during the crawl-phase setup to capture touchpoints. - **Perplexity** (tool, AI Search / Research) — Referenced (transcribed 'Proplexity') as an external-research tool the Genie agent integrates with to pull industry benchmarks and tighten a business case. ## FAQ **Q: What is GTM Engine?** A: GTM Engine is an AI platform, founded by Robert Moseley, that continuously listens to every call, email, and touchpoint on a sales opportunity and automatically populates the CRM (Salesforce or HubSpot) with structured data — removing salespeople from data entry. It also creates and enriches contact records, scores deal health, tracks follow-ups, and runs a reasoning agent called 'Genie' to execute deal tasks. You can try it at GTMEngine.ai. **Q: Why do CRM data and RevOps processes break down at scale?** A: Because they depend on people. You can design a perfect process, but without a legitimate way to enforce it, it breaks. Salespeople spend roughly 20% of their time on CRM data entry, do it poorly, and produce data nobody trusts — and enforcement via mandatory fields just makes reps invent values to unblock themselves. The durable fix is to remove humans from data capture by having AI extract it automatically. **Q: Is Salesforce built for salespeople?** A: No. Robert Moseley argues CRMs like Salesforce are reporting and dashboarding tools built for RevOps and leadership, not for reps to sell. Reps typically go in only to update fields before the weekly forecast call and do their real work in other tools. GTM Engine flips reps into consumers of data — served pipeline health, follow-up gaps, and next actions — instead of producers of data. **Q: How does GTM Engine score deal health?** A: On a two-dimensional map: the horizontal axis is how healthy (likely to win) the opportunity is, and the vertical axis is how likely it is to close when the rep expects. That yields safe bets, acceleration opportunities (real deals slipping to next quarter), and deals with a firm in-quarter decision date where you may not be selected. Health and interest scores are plotted over time, and forecast close dates auto-adjust from inbound emails. **Q: What is a promoter score in GTM Engine?** A: A per-contact score from -10 to +10 that identifies champions and blockers at a glance, with the reasons and specific quotes behind each. Filtering contacts by ICP persona and a high promoter score produces a live, shareable list of advocates — so marketing gets a real case-study and testimonial list, complete with what each champion actually said, instead of chasing salespeople for names. **Q: Why does Robert Moseley say messaging is how you win in 2026?** A: Because AI has decimated the legacy lead-generation channels: you can't buy Salesloft and blast sequences, and cold calling no longer works. The remaining edge is old-fashioned marketing — describing the customer's pain in the actual words of your ICP. GTM Engine enables this by extracting that real language from sales conversations, so the answer isn't more SDRs managing templates but sharper, ICP-grounded messaging. **Q: Why is the market moving toward external, fractional RevOps?** A: Internal RevOps is one of the hardest jobs: you inherit a 20-year-old Salesforce instance you can't touch without breaking downstream processes, and you rarely have time to research new tools. External operators stay on the cutting edge across many companies. Because RevOps problems are high-complexity but low-variability — the same across similar-stage companies — an outside operator's recommendations transfer, which is why the function outsources well. **Q: What should a startup keep in-house versus outsource in go-to-market?** A: Robert argues RevOps is a strong outsourcing candidate because the problems and solutions are consistent across similar-stage companies, but sales and product must stay internal early — they are the heart of the company and you can't outsource the accountability. Channel-only motions can work for a large behemoth, but not for an early-stage company. ## Timeline - **00:00** — Meet Robert Moseley & the origin of GTM Engine - **00:40** — Startup junkie: 9 startups, from software engineer to founder - **02:05** — Why go-to-market systems always break: it's people - **02:52** — AI got good enough to capture every piece of data - **04:27** — The core problem: getting rich data into the CRM - **08:03** — Demo: crawl-walk-run and connecting the systems - **09:28** — Remove humans from data entry; reps as consumers of data - **10:52** — The calendar view & follow-up tracking - **13:51** — Salesforce is built for RevOps, not salespeople - **15:03** — Performance, coaching & the SDR-to-AE gap - **17:33** — The two-dimensional deal-health map - **19:27** — Target vs. safe forecast: pulling in at-risk deals - **22:23** — Sales leaders should be field generals, not forecasters - **23:27** — Deal gaps, suggested strategies & the Genie agent - **30:15** — The marketing disconnect: Moveworks & 'Worktopia' - **32:27** — Promoter score: building champion lists for marketing - **35:09** — Messaging wins in 2026: AI killed the old channels - **37:25** — Unified data model & listening to the field - **38:32** — Why the market is moving to external RevOps - **41:17** — High complexity, low variability: what to outsource - **42:33** — Closing & how to try GTM Engine ## Related episodes - **Ep. 85: Why AI + GTM Engineers Can't Replace RevOps** (Tessa Whittaker) — The companion argument: AI and GTM engineers automate the grunt work Robert targets, but the operating judgment stays human. · https://leanscale-knowledge-hub.netlify.app/podcast/tessa-whittaker-ai-gtm-engineers-revops/ - **Ep. 95: Why AI Means More RevOps Hires, Not Fewer** (Jimmy O'Halloran) — Both hold that AI makes operators superhuman rather than obsolete — tools are great, the humans and the data model matter more. · https://leanscale-knowledge-hub.netlify.app/podcast/jimmy-ohalloran-new-relic-revops-consumption-revenue/ - **Ep. 88: Why AI Won't Close Your Biggest Deals** (Michael Kiernan) — Pairs with the 'AI killed the channels, humans still sell' thread — AI arms reps but doesn't replace the seller. · https://leanscale-knowledge-hub.netlify.app/podcast/michael-kiernan-nextdoor-ai-wont-close-deals/ - **Ep. 6: Why Your Forecast Is Broken** (LeanScale) — Foundational forecasting episode; GTM Engine's two-dimensional deal-health map is a concrete answer to broken forecasting. · https://leanscale-knowledge-hub.netlify.app/podcast/why-your-forecast-is-broken/ - **Ep. 15: Where Should RevOps Report?** (LeanScale) — Org-design companion to Robert's claim that the CRM is built for RevOps/leadership and the internal-vs-external RevOps discussion. · https://leanscale-knowledge-hub.netlify.app/podcast/cameron-legge-where-revops-report/ ## Full transcript _Machine-transcribed and not diarized; speaker attribution is inferred._ _Transcript only, as a separate file: https://leanscale-knowledge-hub.netlify.app/podcast/robert-moseley-real-problem-with-sales/transcript.md_ ### 00:00 — Meet Robert Moseley & the origin of GTM Engine **[0:00]** Today we have Robert Mosley, CEO and founder of GTM Engine and really, really excited to walk through what they're bringing today. They're building the platform that is the perfect platform if you're in sales, SDR, anything go-to-market, and they are really pulling together some of the most important components of being the best version of yourself when you're selling to your prospects. So Robert, really, really excited to have you here. And also, every time we have a founder, I love just hearing what gave you the idea and inspiration to get GTM Engine off the ground? ### 00:40 — Startup junkie: 9 startups, from software engineer to founder **[0:40]** Yeah, yeah, Anthony. Appreciate it. Yeah, it's a good question. I mean, I'm kind of a startup junkie, right? So I've been, this is my ninth startup actually, like first one I founded, but I mean, back it up a little bit. I started my career as a software engineer out of college. I joined this little company called Jobs2Web. It was a startup, like literally in like a dentist office. We ended up getting acquired. And I made enough money to like buy a car, and like for somebody fresh out of college, I'm like, oh man, startups are super cool. **[1:08]** Like this all happened every time, right? Like why wouldn't everybody just work for startups? So then I just kind of got the startup bug after that, did it again and again and again. So I mean, it's been going on for like 17 years now. So how we got here is like, I'm typically like first on board on the go to market team, right? So, you know, move works part of like the first group of folks hired there. Same thing at Harvey, same thing at CopyAI. **[1:38]** And always end up like setting up the systems too, right? It's like, okay, usually we don't even have a CRM, right? Okay, so how do we put together a process from scratch, which is interesting because it's both an opportunity because you get to set it up the way that you want. But it's also, so like, I love that part. But like the thing that frustrated me over and over is like, as you grow, as you scale, as you get people actually like using this system, like it inevitably falls apart. ### 02:05 — Why go-to-market systems always break: it's people **[2:05]** It always does, right? I think like this is part of the problem you guys solve is like you go in and you clean things up for people. And so I spent a lot of time thinking about this because, you know, we put so much time and effort into something just for it to like not work. Well, what makes it not work, right? And basically came to the conclusion that it's people. You know, you can put the best process in place ever, but if there's no way to like legitimately enforce that process, it's going to break, right? **[2:35]** And I think like sales is a perfect example of this because like they have CRMs, which are essentially reporting tools. And they're really good at that. You know, there's an ecosystem around them and they're integrated into all these other systems. And so theoretically, if everything is correct in there, everything flows, everything's, you know, rosy, you can see exactly what's going on. ### 02:52 — AI got good enough to capture every piece of data **[2:52]** But as you know, like, that's never the case. So when I wanted to start this company, because I think we're in a spot now where like AI got powerful enough, where it can like just everybody has a call recorder, it can synthesize like calls, it can synthesize emails, you see this, everybody uses a call recorder today. So give me a summary, give me a follow up or my next step, stuff like that. But like, more powerful than that are like, well, what are the questions that your entire leadership team like wants to know about your sales process, right? **[3:20]** So those are the 100 custom fields that you have in the opportunity object that you expect the sales team to fill out, which they don't. And even when they do, it's like usually like not trusted, right? Because that's not their job, like their sales people, they're supposed to be selling, not like doing like data entry, they spend like 20% of their time doing this stuff, just to do it poorly. **[3:41]** But anyways, like AI got to the point where it could do that. So instead it was just a summary, like what if we listened in and like continuously like grabbed every piece of data, every piece of communication that happened on every single opportunity, and not just like give you a summary on here's how the call went. **[3:56]** But like, hey, these are the fields like this, for us, like CRM they use the call recorder they use, this is their use cases, this is their buying process, this is their budgetary cycle, here are the people that are involved to hear the array of competitors that have been mentioned and the use cases under which they've been mentioned. Because when you can do that's really the only way to solve it, right? Like you get people out of the process. So I've been banging our heads against the law on this problem for about a year and a half now. ### 04:27 — The core problem: getting rich data into the CRM **[4:27]** I think we think we cracked it. I think we're there, which is pretty cool because I mean, like, it does really open the floodgates for some really cool stuff when you figure that out. I think you've done a phenomenal job and I'm really excited because I know you have a demo prepared for us today. But really one of the main pain points that you're solving for is getting that data populated and it really is the biggest breakdown in the process. **[4:51]** And because humans are humans, they forget what was said on a call, or they don't articulate it in the data the right way, or they simply just don't do it because you're mainly hiring people who should be in front of people all day. Like you said, they should be selling, not entering data. But it's all the rich data that the company needs to make decisions. So automating that flow of data into a structured manner into your CRM is just unbelievably valuable. So, and I know there's so much more to GTM Engine, but starting there and solving that problem first really unlocks so much more potential. **[5:32]** Yeah, I mean, like you have to solve that problem first. It's not just like getting the data in there, right? Like you also have to have the relationships correct. So meaning like, who's actually, who am I talking to on this opportunity? There's an object in Salesforce called opportunity contact role, which I'm sure you're aware of. And like no one uses it, right? So it's like, who are our champions? Like if you ask a really good salesperson, they'll rattle it off. It's like, yeah, Brenda's our champion. She's great. **[5:58]** We were texting last night. She told me that she's talking to Steve next week. They're going to make a decision on this, but they're also looking at this. Like, don't rattle it off, right? Like none of that stuff is in Salesforce though. And so how is leadership supposed to like help coach, help like block and tackle? Hey, maybe they can go talk to like the VP of sales while the sales rep was talking about that. Or maybe we need to bring in a solutions engineer to run a demo for this person who is kind of skeptical about our solution and is on more on the technical side. **[6:26]** You know, what should they show? What should they talk about? What were they skeptical about? Like all this stuff is up here, but it never get like that handoff gets fumbled all the time. Right? So like one, you have to get like the data in there, which is really important for like reporting and dashboarding purposes. But like you also have to create the records, right? Like is there a contact in Salesforce for every person that you've talked to on this opportunity? Like probably not. Like is there any data on that contact? Do you actually know who they are? Like even what role they are? Like maybe role and like basic stuff like that. **[6:57]** But by role, I mean like what role are they playing in this like procurement process, sales process? Because then you can start to turn your CRM into like, you know, it's even hard today to like build a report. It's like, give me a list of all my customers. You know, usually that can't even be done in Salesforce, which is wild because like C and CRM stands for customer. But you can't even give you like a reliable list of who your customers actually are, what they own, how much they're using it, like who's using it, can't do that. **[7:28]** And that's the whole promise. So you need like remove humans even from that stuff to like really get that right and you got to get that right first and then all the other cool stuff falls into place. Yeah. Well, and I've heard, I've heard some of these promises before, but your platform is the first time I've seen it done in a really elegant and intentional way. So yeah, I'm really excited for you to kind of show what does it look like when you're in GTM engine, what type of data, what's capable so people can kind of get their hands wrapped around what this can do for their GTM work too. ### 08:03 — Demo: crawl-walk-run and connecting the systems **[8:03]** Yeah, for sure. Yeah, let me just jump into that then as I'm pulling this up. So it's actually kind of like a crawl, walk, run the way that we typically recommend folks start. So it starts with, I'd say, the RevOps people, right? And so like the very first thing is just like connecting systems, right? So connecting your CRM, connecting your call recorder, connecting through Slack or Teams or like whatever going through and then connecting up your Gmail, whatever you're using there. Setting up your team structure, boring stuff, right? Setting up your field mappings, right? It's like it sets up an instant two-way sync. **[8:48]** So every time like we update a field that we're filling out, we sync that back to the CRM HubSpot Salesforce. This can run completely in the background, right? So like typically we recommend just like start this stuff. Like let's just start like auto-creating the records like so via integration with email calendar. We see a new email. We create that record. We run the associations. We run research and enrichment on that. And then any thing that's said in that email or anything that's said in the call that they participated in is synthesized and updated on that record. And then we'll create like the opportunity contact roles and stuff. ### 09:28 — Remove humans from data entry; reps as consumers of data **[9:28]** And then you kind of like the first process is just like remove humans from that role. Like AEs hate, you no longer have to fill out Salesforce. I know it sounds like a very bold claim to make, but it's 100% true. Like they're bad at it anyways. You don't even want them doing that, right? Like even if you could do it as good as they're doing it today, which is really poorly, you would do that every single time because that gives them like 10, 20% of their time back to like actually sell, which is super useful. But then like something interesting happens. **[9:55]** Like after that, like then you start to realize like tools like Salesforce aren't really built for salespeople. Like they're built for reporting and dashboarding and leadership, but they're not built for salespeople to help them like actually sell, right? They're built so they're like creditor faces. They go in there. They input data. They get yelled at every Wednesday before like the weekly forecast call. They'll make sure like the CRM is updated. And that's all the time they spend in that. They use other tools to take notes, you know, completely. So anyways, like then we built a UI. **[10:26]** It's like, okay, well, if they don't have to, if the interface for inputting data no longer makes sense because they're no longer inputting data, like what does an interface look like, right? Because then they become consumers of data instead of producers of data. Meaning like, hey, here's everything in your pipeline for this quarter that you have closing. Here's the healthy stuff that we think is going to close. And we think it's going to close when you think it's going to close. And here's some of the unhealthy stuff. So anything highlighted in yellow, like this isn't going well. ### 10:52 — The calendar view & follow-up tracking **[10:52]** Like either health score is low, meaning like probably they're not going to buy from you. Or maybe like health score is high, like some of these, but it's just like in a different quarter, right? So it's like they're going to buy, but it's just not like they're not going to get it done in time for you to like hit your number this quarter. So it's just kind of like, okay, what should I work on? Right? The other thing is the biggest thing that I've seen in like my 17 years in sales is that like stuff just falls through the cracks. You're managing 20 opportunities, inevitably stuff falls through the cracks. So we built this calendar view. **[11:29]** This does a couple things. Let me share this. What's really neat about this is it just it only shows you like your external medians. This is just dummy data too, so I don't get too married to this, but shows you external medians, it's color coded as well. So like red means like you had the meeting, but you didn't follow up at all, right? So like clearly I've done a very poor job here, according to this dummy data. Green means you did follow up and they responded, and then it'll be color coded yellow if you followed up and they didn't respond. So like you immediately know like, crap, I forgot to follow up after this. **[12:03]** I followed up with these guys, but I'm probably need to nudge them because they never got back to me and like this one is good to go. And then also, who are you meeting with, right? What are your upcoming meetings, right? What's it going to be about? What's the status of the opportunity? Who are you meeting with? What's interesting to know about them? How does our product align for them? We're lucky because we're salespeople selling the salespeople. So like we're kind of like practitioners of our own tool. That's not always the case. Like when I was at MoveWorks, we were selling to IT service desk managers, right? **[12:35]** Like the salespeople were not IT service desk managers. They had never done that role. They didn't even know what ITSM stood for. So just being able to like guide them, be like, "Hey, here's who you're meeting with. Here's why they're going to care about our product. Here's what you should talk about," right? Personally, for each individual that's going to be involved in that is a pretty big deal just for preparing correctly for meetings. Any questions on that? No, it makes a ton of sense. And I really think that calendar view is probably my favorite because that's how I think about... **[13:10]** I do a lot of selling, especially the founder, you know, early stage. You're going to be doing a lot of selling. So this is part of my workflow. And then so seeing, I use my calendar for everything. So that's how I remember, "Hey, what meetings did I have?" And I love that it's like, "Hey, you didn't follow up with them." And it really is as simple as that. Are you giving them the information they need? Are they being responsive? And then, yes, getting all the information kind of in one place in your cockpit to manage everything that's on your plate. I 1000% agree Salesforce is not built for salespeople. I think it's built for RevOps more than anyone. ### 13:51 — Salesforce is built for RevOps, not salespeople **[13:51]** So getting the information that they write is great. They do a pretty poor job at building for RevOps. Yeah. Well, of all the personas, I think that's like RevOps leadership. People just want to see the dashboards. Exactly. I mean, that's the whole point of it, right? What's actually happening in our pipeline? What's going to close this quarter? What's healthy? What's not healthy? But people loathe going into an opportunity or going into a contact to update stuff, like nobody ever wants to. Yeah. That's right. So a lot of buttons. Oftentimes, you'll go in there because you want to move the stage here, just have a good call. They agreed to POC. **[14:29]** I moved this to the POC stage. You do that, and it pops up. Warning, you can't do this because you've got to fill up these five fields first. And then you look at those five fields and you're like, "I don't know." So then you put NA or you make something up or whatever, just to unlock yourself from being able to move it to that stage. And so just the whole process that you put around that to enforce data collection because you know they wouldn't do it if you didn't put that in enforcement then actually leads to data that you don't trust. But on the flip side of this, when we're analyzing all this, there's other things over here too. ### 15:03 — Performance, coaching & the SDR-to-AE gap **[15:03]** In your court, these are emails in your inbox related to an opportunity that requires response that you haven't responded to. Or meetings like this that you haven't followed up with. We draft this. You can edit this and you can send it directly from here. And then we're going to show you. How are you performing? Where are you at for court attainment? Where are you expected to land at if nothing changes? And then how do you compare against your colleagues? I'm pipeline coverage. I'm pretty healthy here. Wind rate? Yeah, I'm doing really bad. I'm like 33% below my colleagues. So this is something I got to work on if I'm going to be above average AE. **[15:38]** This is where my focus needs to be. Even this stuff is super hard to find. Even for managers, you're trying to coach your team. How do you know how to coach them if you don't know what they're good at, what they're bad at? And all your interactions with them are just asking them, "What's going on in this opportunity? What's going on in this opportunity? What's going on in this one? Tell me about this one." You never really get a chance to coach. So this flips that on its head. You start from objective data, objective truth and go from there. Well, and I think a lot of, at least the good ones, a lot of people in sales want to self-coach. **[16:10]** And they want to know where they stack up against. It's a competitive environment. So if my win rate's low, then I'm going to be like, "Hey, how do I get that up?" Yeah, precisely. Exactly. And you need to know, we're strong. I see this all the time with SDRs that get promoted into an AE role. They're super good at generating pipeline, of course. That's what they've done. Basically, their whole working life up to that point usually. But they're not good at managing a complex deal cycle because they haven't done it before. So this just points out, yeah, you can build 1,000% pipeline coverage. But if your conversion rate is a quarter of everybody else, **[16:51]** that doesn't really matter. You're just burning through all your leads. So the other thing is, on this step of, hey, start with just collecting the data, cleaning up your CRM. There's a bunch of CRM hygiene stuff here that I didn't talk about, too, which will auto-enforce relationships, ownership. So if somebody leaves the company and they own all these records, we can automatically move them over to other people. Can we pause for one second? Sorry. Yeah. I'm just going to, like, buy robot vacuums. Yeah, take your time. I heard something. I thought maybe the window was open. Yeah, it's super loud. You probably can't hear it because of the noise cancellation, ### 17:33 — The two-dimensional deal-health map **[17:33]** but for me, it's super loud. All right, I'm back. So anyways, then we can also analyze the opportunity stuff. It's like, how healthy is it, really? What's likely to close? And this is a spectrum. So think of this as a two-dimensional view. The further right you are on this, the healthier the opportunity is. The further up you are, the more likely it is to close when you think it's going to close. So up here would be your safe bet. We think it's going to close. We think it's going to close when you think it's going to close. Down here, acceleration opportunities, we think it's going to close. We don't think it's going to make it in time for this quarter, **[18:13]** like whatever time frame you have selected here. These are the ones, like, they're not real, right? The AE might include them in their QBR presentation, but they're dead or they're not going to happen. And then up here, it's like, this is a really interesting category because it's like, if it is going to close, it's going to close in this quarter, but we don't think they're going to select us, right? Either they're leaning towards competition based on our analysis or buying nothing or whatever. But they've explicitly said, we're going to make a decision by X date and that date would be in this quarter. **[18:46]** Yeah, and I think you have those labeled well appropriately. Those are the ones that maybe you have an opportunity to affect. And they may say, hey, we have a deadline to get this done on the state. I've definitely been in deals like that, where it's a bake-off against three vendors or whatever, and we're making a decision December 1st. Okay, so yeah, it might close then, but your likelihood of win, like you said, you may need to get in there and really, really position yourself well against whatever alternative they're thinking about. Yeah, they're not responding, whatever that might be, right? So basically, too, you're exactly right. ### 19:27 — Target vs. safe forecast: pulling in at-risk deals **[19:27]** You hit a point, which is the whole reason we built this, because it's like, okay, this is dummy data, so this isn't super aligned with what I would like. But anyways, you have your target, right? How am I going to get there? You have your safe forecast. Let's say the target line is right here. I got to pull in some at-risk stuff. At-risk being like, it's in one of these categories, right? So where would I look first? First thing, I would probably look here. I would say closer to this line, we think it's going to close, but we don't think it's going to close this quarter, like just barely, like down here, it's like further away, right? **[20:02]** So I'd look for things close to this line, because you're the ones where, if you find out early enough in the quarter, you know, like right now, our fiscal year ends January 31st, but I have two months to see if I can do something to accelerate this one, right? That's where I would look first. Now, some of these over here, like, would be good to look at too, right? Okay, well, why, like, are they not responsive? Are they like, you know, what is it, right? What's super cool about this is like, you can dig in, and you can start to see because we're tracking all this data. So let's take a look at just like this one, for instance. Like, I can see this one, **[20:37]** and I can click into any opportunity and get like a full sense of all the data, you know, where it's at, what stage it's in, the health score. I can click into this. We actually plot this over time. So you can see like how the health score changes. You can see how the interest score changes, which is a bit different, like somebody could be interested, but like not have budget, so they could, interest level could be high, but health score could be low. Forecast close date, how this has changed over time with like each touch point. So if like somebody sends in an email and says like, hey, super sorry, wasn't able to get budget for this quarter, **[21:08]** but let's touch base in February, your fiscal year. Forecast close date would automatically be adjusted. Health score would go down, and we would flag that, right? So you would know right away that like, hey, this thing, like instantly, basically, as soon as email comes in, like we're processing this stuff, and you can start to do things about this. It's like, okay, they didn't get budget, but maybe we can do something clever. Like, hey, if we can get that signed this quarter, like we won't, you know, send you the invoice until next quarter. You know, the things that you can do to get around that stuff. **[21:38]** But if you find out last day of the quarter, you can't do anything about it, right? Right. Yeah, I think it's, you know, obviously you can tell, um, how you've designed the platform. Um, you and your team have had a lot of experience in sales to understand like those nuances, what's important, what matters to somebody who's coaching someone on a deal and just getting the right information to the right person at the right time. So you can actually do something about hitting your number or not, where I think a lot of teams, like there are a lot of VPs of sales that are, you know, probably extremely talented, ### 22:23 — Sales leaders should be field generals, not forecasters **[22:23]** but they just have no way of knowing where to go in and coach their team and help where they just don't have the right data. Well, I mean, they, they spent so much time figuring out what's going to close that they have no time to actually like impact what's going to close. You know what I mean? And I think like, I've talked to a lot of sales diggers and they're like, they really pride themselves. Like we're super accurate on our forecasting, which is great, right? Like you want to be accurate. Like you can't plan otherwise. Where do we hire? You know, how much budget are we going to have? Where do we cut back? Whatever. **[22:58]** I don't think that's really the role. Ideally, I don't think any like sales parallel. If you actually asked them, it's like, is that really what you should be doing? Like, that's not it. Like they want to be like field generals, you know, like front, front of the line, like on their horse, like, you know, saying like, all right, troops over here. We're going to flank them. And you know, you know, whatever, maybe a bad metaphor. But I think you get my point. Like that's where their skill set is better used. The other part where this gets super cool, right? It's like we're extracting all this data. Again, like any field that you want, right? ### 23:27 — Deal gaps, suggested strategies & the Genie agent **[23:27]** So we understand not only what we know, but what we don't know. So we do good deal gaps and suggested strategies. These are like all the custom fields that we would want to fill out. This can be any data type. It could be like true, false, a number, date, whatever. But what's cool about this is like we also package together like, okay, what are your next steps, right? So it's pulling this out. And let's say something like this, like develop a custom ROI calculator using Cyberhaven sales metrics and growth targets. Review this with decision makers. So we need to do this. This is hard, right? You have to know like, what are their sales metrics? **[24:03]** What are their growth targets? What is our pricing going to be? Where is our pricing calculator? How did they say that they're going to look at ROI? Because like maybe our standard ROI calculator isn't actually aligned with how they're thinking about this. And as much as salespeople like to think that we can control the process, like, you know, buyers control the process. If they say that this is how they're going to look at it, and the CFO says like, this is how I'm going to account for this, then guess what? You're not going to close the deal unless you like give them what they want. So anyways, like to accomplish this task, **[24:35]** develop a custom ROI calculator using their sales metrics and growth target. That's a hard job, right? Like the AE probably doesn't have the skill set to do this. They're going to pull in lots of people. So we also have this genie feature, which is like an agent, which I can just say like ask genie, right? Help me complete this task. Just click that button, click send. So this agent is like integrated with a bunch of different tools. It's full reasoning. It can do a lot of things. It has access to the whole context, every touch point that we've ever had, all the contacts and who they are, all the data, right? So we know their current team size. **[25:13]** We know their projected team size. We know what our annual pricing is going to be because we've talked about this and just by talking about it, we've extracted that. We've collected that data and it exists on these records. So we can see like the inner thoughts if you want. So you can see, you know, this user is asking me to develop this, here are the key decision makers, here are their pain points. Here's the value proposition mentioned, pricing, growth context. Okay, so now it's starting to put this all together, right? And this is impossible unless you have the underlying data context, right? So because this is all here and we've extracted all this **[25:50]** and we've updated it with every single touch point, we have all the inputs that we need to do this. So I can put together really, really cool stuff here. And I could even say like once this is done, you know, it's like, okay, what do you want me to do now? You want me to schedule this call? Should we customize the assumptions? Let's say, yeah, you're like this one, research any, yeah. Could you research some benchmarks to make the case more tight? Yeah, and while you're pulling this up, I mean, to ask a sales rep to, one, take the time to do this, two, assume they would do it well. And then three, like bring in the relevant research and data. **[26:33]** I mean, it would be hours and hours of work. If they even thought about doing it. Yeah, exactly right. Like that's the thing. And then I mean, like usually like they're not, you know, I've been in sales for a long time. They don't, they ask somebody else to do it. Usually it would be, you know, somebody like me, which would be annoying. But like also like, you know, you jump in, you help. But the other problem is, is like I would get pulled in on these projects and I'd be like, okay, cool, we got to do that. Do we know these things, right? Do we know what their sales team size is? Do we know like how they're thinking about ROI? **[27:12]** Like to do this, I have to know those assumptions, right? So even if I have all the skills to do this manually, I don't have the data to do it correctly, right? And this is like saving that context, associating that context with these records automatically without having to do anything allows you to do this. This is kind of a cool one. So it actually executed the research step. This is integrated with things like Proplexity and X7 and whatnot to do actually external research. So it comes back, right? Industry benchmark. They spend this much time with manual legacy systems from the Scartner 2025 reports 40 to 50% for 40 hour work week. **[27:50]** They spend, you know, 12 to 20 hours on admin tasks, okay? So it's like getting these industry benchmarks directly from and then it's recalculating this with industry benchmarks. Now, imagine how long this would take. We just did this in there. So imagine how long this would take to put something together, this good that's aligned with their data, what they've said, everything. Mm-hmm. Yeah, and then the follow-up of this too, like, "Hey, how do you want me to prep this? Let's get this in an email to the decision maker, whoever needs to see this." Like, amazing, like taking it not just from the steps but the actual asset and deliverable **[28:32]** and then the execution on communication. It's huge. Yeah, exactly. I mean, it's like everything to like-- and this is like path to close here. These suggested next steps. They come from two things, right? There's like your own sales process, which like you configure in like settings and whatever, like when on implementation you do that. So like we know, "Hey, here's your stages. Here's our entry/ex or criteria between these stages. Here's typically how we would like a sales process to go." But then it's also contextual, of course, based on like what they've told us during the sales process that we actually need to do next, right? **[29:04]** And like probably weighted more towards that stuff. Because then you can come and you can see exactly, "Okay, how do I move this deal forward? Like, what are my like most critical blockers right now that I have to do?" Like even knowing that's super hard, you'll see emails, "Hey, you have time like to touch base next week." Like that's super lame, right? Your competition is giving them more than that. If that's all you say, like you're probably not even going to get that next meeting. If you give them exactly what you want because you've listened, maybe you even have to listen, but much more likely to close business, **[29:37]** give every deal like its best chance. And then anybody else can come in here, have visibility on the exact same context that you have. So if you do need to pull in an SE or you want to ask like your management for some coaching advice, they have the context coming in. So the big thing about this stuff too is like this isn't just sales. You know, like the more and more you think about it, like the one place where you consistently talk to your ICPs is like actually in sales. And their customer success, sure, a little bit. But like honestly, like they just set up like a quarterly check-in, maybe a little bit higher touch at the beginning. ### 30:15 — The marketing disconnect: Moveworks & 'Worktopia' **[30:15]** But like in sales, you're having multiple conversations every single day with your ICP and your sales team is there, right? And like you've probably seen this where like sales is like, you know, what the hell is marketing talking about? Like why are they talking about that? Like we know because we have the most practice in this, like what they want. Like I'll give an example. I'm going to pick on a former company of mine, Moveworx, which is totally okay because ServiceNow just bought them for like $3 billion. So I don't think they're going to care that I pick on them. But like we have this problem. Like we're talking to IT service desk managers **[30:50]** and they cared only about can you resolve tickets for me? I'm underwater. I can't hire enough people. When I hire like a service desk agent, they last six months and they're gone. Like we're drowning in tickets and it takes us three days on average to resolve even a simple ticket. So cool. Well, we can do that, right? Hey, give us your ticket data. We'll analyze it. We'll show you exactly how many tickets that we can resolve. And they're like, hey, this is awesome. Let's go. On the marketing side though, they were talking about like, we had this thing called like Worktopia where it's like, **[31:24]** they were marketing us and like, yeah, it's like this magical place to work if you buy like Moveworx, like employee experience. Like I can tell you firsthand that the IT service desk manager like couldn't give two shits about employee experience. Like they were drowning in the work and they needed help, right? So sale, we never got any leads from marketing, by the way. But the SDRs, they were super clever. They would send like examples of this like analysis report that we did. They would send them out, say, hey, we can do this for you. You just sent us like, you know, export 12 months of your ticket data. **[31:56]** And we'll show you exactly how many tickets we can resolve for you. Would that be interesting? And they're like, everybody was like, yes, of course. So there's this like massive disconnect between like marketing, not knowing what your customers are saying. And this is like super important, like if you get the data model correct, right? And you populate, of course, like the opportunity object with like all the fields and other stuff that like you want to track and build reports on. Well, what about contacts? Like these usually they're never updated at all, right? So we'll create the contact role. We'll give explanations for everything, right? ### 32:27 — Promoter score: building champion lists for marketing **[32:27]** So you can always check our work here. What I really like is this promoter score. So this goes like negative 10 to positive 10. And it's like at a glance here, I can see like, you know, let's pretend there's another one here that's like negative four. I can see like, you know, who are, who my champions and promoters are and like who my blockers would be. And I can see why. Yeah, I can click on this and I can see exactly why. But what's really cool about this is then I can go into the records view and I can quickly say like, yeah, I want to build. I'm going to pull in some of our other fields. I'm going to pull in promoter score, promoter score reasoning, **[33:01]** and I'm going to pull in, let's say like the ICP persona, which is like another classification that we do. Okay, so now I have promoter score, promoter score reasoning, ICP persona. All right. Now I want to filter this and say I want a list of all of the contacts who I've ever talked to, their ICP persona, where their promoter score is greater than or equal to seven. All right. Okay, so now I have this list. I can save this. I can share this with say the marketing team. Hey, marketing team, you know, here's who we talked to that loves us. Here's their ICP persona. Oh, and here's why. All right, with specific quotes. **[33:54]** So instead of like the marketing team going like, hey, you know, who can we talk to about a case study or like a quote for the website or like whatever, it's like here. Like there's a real live list of all of our biggest champions and why they like us, specific things that they've said. And then if you find one really interesting, just like I showed you on the opportunity thing, you can click into that contact and you can ask additional questions about that. You know, like, hey, maybe I want to build a case study for sales leadership, or maybe I want to build a case study for operations. Maybe I want to build a case study for, you know, C level, right? **[34:27]** C-suite. Like I can filter this even more. Say like, you know, I want when the ICP persona contains, let's say, I don't know, maybe marketing. All right, we've got a couple. We haven't typically talked to marketing people. We should, but sales, you know, executives. I can slice and dice this data very, very quickly any way that I want. I know exactly what to talk about. I know where they work. I know their ICP persona. And then I know where to get the introduction from, who the owner of this record goes. Yeah, that's super powerful. I mean, normally, like you said, marketing teams running around interviewing salespeople, maybe. ### 35:09 — Messaging wins in 2026: AI killed the old channels **[35:09]** Usually they're just kind of doing it on the app. And then you also have the bias of the salespeople thinking, oh, why are people wanting to buy? So even if, even though they probably have the best, they're still biased. So to get, hey, here's literally what was said. And then you can trend it across multiple people and personas. Because I think messaging, it always has been. But even more so, getting your messaging absolutely dialed in is going to be the most important way to win in 2026. Like you have to. Yeah. Well, I mean, you have to, right? The channels are, you can't just buy sales loft and blast out sequences anymore. That doesn't work. **[35:58]** You can't call people. That doesn't work. No, AI has killed that. It's just completely decimated every single lead generation channel that we've used before. So what do you got to do? I mean, you got to go back to good old-fashioned marketing. If you're the one that can talk about the pain that your product solves, the most, the best, using the words of your actual ICP, you're going to resonate the most. That's how you win. That's how you start building more pipeline. Don't hire a team of SDRs just to manage templates or even AI writing sequences. That doesn't work. Go back to the basics. Yeah. I think it's like a mix of madmen with data. **[36:43]** So it's like pour yourself a whiskey and then get all the AI gathered data and then make something beautiful happen. Yeah. I mean, on the product side too, your product has to be good for you to win. Yeah. It's really good salespeople that try to sell bad products and they don't do well. It's not because they're bad at sales because product hasn't built, they don't listen well enough. And companies die all the time because of that. But you take this a step further. It's really this unified data model. What am I extracting? If people are no longer the bottleneck through the amount of data and the accuracy of data that I can collect from the conversations ### 37:25 — Unified data model & listening to the field **[37:25]** that I'm having with our customers and potential customers, really you can track anything that you want. So I want to know what are the use cases that are coming up? What are the points of friction? Where are the competitors that are coming up? And how does that map to the use cases that they care about? If I'm a product person, that's what I want to know. They'll talk to customers, they'll use some analytics tools to figure out where people are clicking in the UI or whatever, but that doesn't get them all the way there. You also have to listen to what people are saying in the market. I think product teams are too focused oftentimes **[37:56]** on what customer success is telling them and what their biggest existing customers are telling them. So it's like the Wayne Gretzky quote or whatever. Go to where the puck is going, not where it's at right now. If you talk to your customers, you're where it's at right now. If you listen to what the field is telling you, then you can start to build towards where the puck is going. Yeah, and also a bias that your current customers have is they're already locked in with whatever your solution is. So they might not be looking at other solutions right now. It's not on top of mind. In their mind, the problem's already solved. ### 38:32 — Why the market is moving to external RevOps **[38:32]** So it's like the easiest person who's not giving you the most critical feedback. And I found this too, for my own business, when we started LeanScale in 2021, there were no RevOps agencies. That wasn't really a thing. Now, they're all over the place. So we would go into places and we never even experienced going against a competitor before. It was like, we might hire in-house or we'll go through all. But now, we're in plenty of opportunities where it's like, yeah, we're evaluating four different people right now. And this one said they offered this service or they're supporting these tools. So that lens of what we're hearing from our prospects **[39:17]** is so much different than if we went to our customers and said, hey, what do you like about what we do? And where do you think we can do better? So you get much more critical and real feedback from the market. 100%. I think what you guys are doing is super clever too. And I understand why the market is kind of, I think, moving towards external RevOps versus internal. Because some of the hardest jobs in the world are being an internal RevOps person, where it's like, you're hired. It's like, hey, here's our Salesforce implementation. It's been here for 20 years. Don't touch it. Because if you do, something's going to break. **[39:52]** Because every time you try to remove a field, it's broken some process down the line. But I think external folks like you, you're very cutting edge. So you see the new tools that are out there. Hey, well, there is a new, better way of doing things, where I think an internal RevOps person just has to kind of work with the stuff that they have. Totally. And that is one component, staying in touch with the market. Because when you're internal, you rarely have a ton of time to do your own research and figure out what's going on and get you busy keeping Salesforce on. 100%. So even here at LeanScale, like, yes, we do proactively do some research. **[40:36]** A lot of our research comes from the podcast and doing content and bringing on new tools and technologies like yourself. But the main source of R&D actually comes from our engagements. We go into a customer. They already have a certain set of tools that they really like, or they're looking at something that they think is really interesting and they are asking for support it. That's how we learn, like, oh, that's something that we can use over here. So the pressure of our engagements themselves. Yeah, that's awesome. Yeah, I mean, I think, again, it makes total sense, right? That's what you should do. You should hire somebody who's seen way more things. ### 41:17 — High complexity, low variability: what to outsource **[41:17]** Like, hey, we have 20 customers or 50 customers, whatever. And we've used 100 different tools. Your situation, here's what I would use, here's how I would do it. Oh, and we're experts at starting it up. I think there's-- and that space is constantly evolving, too, especially go-to-market space. It's very-- there's new stuff coming out every day, right? Yeah, and I think for us, I think it's like high complexity, but actually pretty low variability between companies. Like, all the problems are the same, and most of the solutions are the same. So if I'm looking at a Series B sales-led growth company, **[41:51]** they're going to have the same problems and the same recommendations are going to help them. It's very complex, so it's hard to know exactly what the right answer is, but that right answer will fit across most companies. So that's why outsourcing something like RevOps, I think, makes sense. There's a lot of things-- I struggle to find a lot of really good outsource SDR companies just because I think the messaging is unique and-- Yeah. --outsource sales and then outsourcing product. Sales and product, those really, really have to be internal. Yeah. That's the heart of the company. You can't put the accountability-- Especially early on. ### 42:33 — Closing & how to try GTM Engine **[42:33]** Like, if you're Nutanix or some big behemoth and you want to go just through channel, then that might work. But yeah, not early on. Like, you can't. You have to have that in-house early on, for sure. Absolutely. Robert, this has been awesome. Thank you so much for walking us through the platform. I think-- I'll start with the beginning. You really solved one of the most-- this really painful problems that people experience getting data from calls and meetings and emails into the CRM in an organized fashion so that you can actually start to do something with it. You can start coaching your reps. For the rep themselves, they have a clear path to close **[43:14]** for each one of their opportunities. They know who to follow up with, when to follow up, and I think-- I can't say it enough. It's so intentionally designed, and you can tell that the people building the product have done sales before, which can be rare sometimes. So it's just very, very well-designed and well-built. So I'm really excited about what you all are doing, and I know there's probably so much on the roadmap that I'll be launching soon, too. Yeah. But just in closing, what's the best way for people listening to get their hands on GTM Engine, get in touch with you or somebody for your team? Yeah. I mean, most obviously, just go to the website, **[43:55]** GTMEngine.ai. There is a cool little demo that you can do. So if you have a call recorder already, drop a transcript from that into that, and we'll show you all the structured outputs and how it would look in Salesforce or HubSpot, as well, just so you can validate it on your own data. Like, "Oh, cool. I actually didn't know that." Yeah. It takes a couple minutes to run, because these AI processes are going to be basically spin up. You have 50 to 60 agents to process the data and look at it from different angles and whatever. But you can kind of see for yourself, if that looks cool, reach out to us. We can get you set up and get going. **[44:28]** Actually, implementation isn't super hard either. It's typically 30 minutes to an hour set up field mapping, then it just processes and-- You're good to go. Amazing. Amazing. Well, for anyone listening, head over to the website. I think you'll be really impressed with what they're doing. And Robert, thanks again so much, and can't wait to see what you guys do next. Awesome. Thanks, Anthony. --- _LeanScale Podcast Knowledge Hub. Free to quote and cite with attribution to The LeanScale Podcast (https://www.leanscale.team)._