Human-First: The GTM Hiring Show

Captivate Talent
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AI is rewriting what "great" looks like across sales, marketing, customer success, and RevOps. Old playbooks have stopped working. And most founders are hiring in the dark. Human-First: The GTM Hiring Show cuts through the noise with high-signal conversations for B2B Tech founders, revenue leaders, and the VCs who back them - from the team at Captivate Talent. Unlike other shows about hiring, we pull back the curtain on what a search actually looks like: real funnel data, real candidate feedback, and the kind of market intelligence that helps you make decisions based on reality, not guesswork. Every episode is built around three questions every leader asks before making a GTM hire: Am I ready to hire? (And what to do when the honest answer is "not yet")How do I hire? (How to run a tight process and spot "great" when you've never seen it before)Did I hire the right person? (How to diagnose whether it's the role, the comp, the process, or the person) Each episode features founders, revenue leaders, VCs, and operators who've made the hard calls firsthand - sharing what worked, what didn't, and what they'd do differently. We also tackle the AI fluency question head-on: not the hype, not the fear, just what's actually changing in GTM hiring and how to evaluate it in candidates. Human-First is produced by Captivate Talent, a boutique recruiting firm specializing in go-to-market hires for seed through Series B B2B Tech companies. If you're building a GTM team and want to hire with more clarity and less chaos, this show is for you. Subscribe so you don't miss an episode - and join the founders, VCs, and revenue leaders already listening.

  1. 1d ago

    AI Doesn't Fix Bad Data, It Weaponizes It with Elio Narciso

    AI doesn't fix bad revenue data. It scales it, with total confidence, across your entire go-to-market engine. You're deploying AI agents for outbound, enrichment, and pre-call research, but nobody's asked whether the CRM data underneath is actually trustworthy. If your ICP has ten different answers depending on who you ask, AI won't catch that. It will just run with it, faster. Elio Narciso is Co-Founder and CEO of Scalestack, the GTM data infrastructure platform used by MongoDB, Redis, and Typeform to clean and prioritize revenue data before it hits an AI agent. He spent years leading GTM strategy at AWS working with hundreds of scaling startups, and now hosts the Revenue Engine Masters podcast, interviewing senior revenue and RevOps leaders about building modern go-to-market engines. Elio gives you a clear-eyed look at what's actually breaking in GTM hiring right now. You'll walk away with a sharper view of which tasks still need a human, what the bar for entry-level recruitment looks like today, and where to start fixing your data before you hire or automate anything else. This episode covers why AI "weaponizes" bad data at scale, who should own AI adoption inside a revenue org, and why the hiring bar for SDRs and RevOps analysts has quietly gone up. It's built for founders, CROs, and Heads of Talent making go-to-market hiring decisions, not for anyone looking for a generic AI hype reel. Key Takeaways - AI doesn't just repeat bad CRM data, it runs confidently on top of it, at a scale no human team could match, across hundreds of agents at once. - Most of the last 20 years of GTM spend went into the system of record. Elio argues the next wave of value sits in a completely different layer, and most companies haven't built it yet. - The bar for entry-level GTM hires has quietly risen. Data entry and basic research aren't jobs anymore. Curiosity, business context, and judgment under uncertainty are. - Elio watches for one thing before a candidate gets past the first interview: do they understand how the business actually makes money, without being told. Useful Links & Resources Elio Narciso on LinkedIn: https://www.linkedin.com/in/elionarciso/ Scalestack: the GTM data infrastructure platform Elio and his team built, discussed throughout the episode https://scalestack.ai/ Revenue Engine Masters: Elio's podcast interviewing senior revenue and RevOps leaders Captivate Talent: https://captivatetalent.com Connect With the Show Host Danielle Parker on LinkedIn: https://www.linkedin.com/in/daniellemessler/ Captivate Talent on LinkedIn: https://www.linkedin.com/company/captivate-talent If your team is deploying AI agents on top of a CRM nobody trusts, you're not alone. Tell us in the comments: what's the one piece of your revenue data you'd never let an AI touch unsupervised? We'd also love to hear how your org is deciding who owns AI adoption. RevOps? The founder? IT? Drop your take below. Visit captivatetalent.com to learn how we help B2B tech companies hire exceptional GTM talent. #HumanFirst #GTMHiring #RevOps #SaaSRecruitment #AIinHiring

    AI Doesn't Fix Bad Data, It Weaponizes It with Elio Narciso

  2. Aug 31

    Why AI Is Bringing Back the Full Cycle Seller with Mark Roberge

    Before the SaaS era, there was no SDR, no AE, no CSM. There was just a salesperson with a territory and a phone book who set their own meetings, closed their own deals, and managed their own customers. Then Aaron Ross wrote ‘Predictable Revenue', and specialization took hold, and that athlete disappeared. Mark Roberge thinks AI is about to bring them back. Mark took HubSpot from zero to IPO, teaches entrepreneurial sales at Harvard Business School, and is a co-founder at Stage 2 Capital. His argument is straightforward: specialization made sense when humans had to compensate for each other's skill gaps. AI removes that constraint entirely, turning a C+ skill into an A+ one for any rep willing to use it. When that happens, the cost of specialization: a fragmented buyer experience and the management nightmare of local maximization, stops being worth paying. This episode is for founders and revenue leaders hiring and building GTM teams right now, VCs evaluating leadership hires at Series A and B, and anyone trying to figure out what great looks like in sales in the age of AI. Mark covers the full-cycle seller thesis, the selling time KPI nobody is tracking, how to interview candidates when AI can do their homework for them, and why the future CRO spec looks a lot more like a rev ops leader than a sales leader. Key Takeaways >> We are still in the brochure phase of AI in sales. Most teams are automating what they used to do rather than rethinking from first principles. The real breakthroughs have not happened yet. >> AI is turning C+ skills into A+ ones. When any rep can be AI-enabled across every part of the sales cycle, the case for specialization collapses and the full cycle athlete comes back. >> Selling time is the hidden KPI. Best-in-class sales reps spend around 15 hours a week actually selling. AI can double that without changing anything else: same rep, same territory, same product and that doubles output. >> The interview process needs to change. Stop testing prep. Start testing execution. Give candidates the task, let them use AI to prepare, and then assess the face-to-face performance: the role play, the live coaching, the conversation under pressure. >> The future CRO spec is flipping. A+ human management plus C+ system design used to win. In the AI era, A+ system design and operational learning ability becomes the moat. Most current specs are not asking for this yet. >> Run the legacy team and the experimentation team in parallel. Keep your human-driven team running proven sequences while a heavily rev ops-oriented experimentation team tests new roles, new tools, and new processes, then let the latter cannibalize the former. Useful Links & Resources Mark Roberge on LinkedIn: https://www.linkedin.com/in/markrobergeStage 2 Capital: https://www.stage2.capital/The Science of Scaling by Mark Roberge: https://www.amazon.co.uk/Science-Scaling-Revenue-Mark-Roberge/dp/1394319428McLean Hospital: https://www.mcleanhospital.orgLetter AI: https://www.letter.ai Connect With the Show Captivate Talent on LinkedIn: https://www.linkedin.com/company/captivate-talent/Danielle Parker on LinkedIn: https://www.linkedin.com/in/daniellemessler/Captivate Talent website: https://www.captivatetalent.com/

    Why AI Is Bringing Back the Full Cycle Seller with Mark Roberge

  3. Aug 18

    How Giulia Gagliardi Built Nory's GTM Team From Two People to a Full Demand Engine

    Giulia Gagliardi took Nory from two marketers to a full demand generation engine, then rebuilt it again to fund a $37 million US expansion. If you're staring down international expansion with no hiring playbook, or you're not sure whether your BDR team belongs in marketing or sales, this one's for you. Giulia Gagliardi is VP of Marketing and Growth at Nory, an AI-native restaurant management platform that just raised $37 million to fund its US expansion. She joined as Nory's second marketing hire, took inbound from under 5% of revenue to 50% in 14 months, and now leads a growth function that includes partnerships and the BDR team. You'll get Giulia's playbook for structuring a marketing team as it scales from pre-seed to Series B, including when to hire generalists versus specialists. She breaks down why Nory's BDR team sits under marketing instead of sales, how that changes as a company matures, and the two interview questions she asks every candidate, from intern to C-suite. Giulia and Danielle cover organizational design, sales and marketing hiring sequencing, and what changes when a GTM team expands from Europe into the US. They also dig into hiring for AI fluency in roles that didn't exist a year ago. This is for founders and marketing leaders scaling past product-market fit, not early-stage teams making their first hire. Key Takeaways Giulia took inbound from under 5% to 50% of Nory's revenue in 14 months, and it started with a two-person team wearing every hat.Nory's BDR team reports into marketing, not sales, tied to a single pipeline number instead of separate KPIs, but that setup has an expiry date.Giulia asks every candidate the same two questions, regardless of seniority, and the answers reveal who's actually done the work versus who's just seen it done.Competing against entrenched incumbents in the US market isn't the real hiring challenge. Finding builders who see the market opportunity is. Chapter Markers 0:00 Testing the US Market Before Launching  0:33 Welcome to Human First: Meet Julia Gagliardi of Nory  1:50 Building the Marketing Team from Two People to a Growth Org  4:41 People Strategy vs. a Hiring Plan  5:03 Giulia's Org Design Playbook: Growth, Brand & Product Marketing  7:05 Why Product Marketing and Brand Report to One Leader  8:29 There's No One-Size-Fits-All Hiring Sequence  11:21 Why BDRs Still Sit Under Marketing at Nory 14:30 The $37M Raise and the Mandate to Expand into the US  15:00 The First Question to Answer Before Building a US GTM Team  17:31 Balancing HQ Culture with a New Local Business Unit  18:05 Hiring Builders to Compete with Deep-Pocketed Incumbents  20:39 Mixing Industry Veterans with Hungry Junior Talent  22:39 Hiring for Roles That Didn't Exist Six Months Ago  23:17 Finding Talent for Skills, Not Job Titles  25:37 Assessing AI Fluency in Marketing Candidates  28:02 AI-Native Culture vs. Bolting AI onto Legacy Products  29:37 The Two Questions Giulia Asks Every Candidate  32:28 The Hire Who Didn't Look Right on Paper (But Worked)  34:19 What Founders Get Wrong About Marketing Leadership  35:33 Teaching Founders to See Brand as a Weapon  36:12 Closing Thoughts & Where to Find Nory Useful Links & Resources Giulia Gagliardi on LinkedIn: https://www.linkedin.com/in/giulia-gagliardi-28856b86/Nory: https://www.nory.ai/ Connect With the Show Captivate Talent on LinkedIn: https://www.linkedin.com/company/captivate-talent/Danielle Parker on LinkedIn: https://www.linkedin.com/in/daniellemessler/Captivate Talent website: https://www.captivatetalent.com/

    How Giulia Gagliardi Built Nory's GTM Team From Two People to a Full Demand Engine

  4. Aug 4

    The Frankenstein System Killing Your AI ROI with Tom Andrews

    A lot of candidates have gotten very good at saying the right things about AI in interviews. The problem is that saying the right words and saying them in the right order are two very different skills, and most hiring teams cannot tell the difference until it is too late. Tom Andrews is VP of GTM and Revenue Operations at Hivebrite and principal at TA Advisory. He has taken a rev ops and enablement team from ten people to two without missing the output, and he has equally strong opinions about why most companies are trying to layer AI onto a data foundation that will never deliver real ROI. Tom has spent his career building the systems and teams that make organizations actually work, and on this episode he gets specific about what that looks like in an AI-driven world. This episode is for founders and revenue leaders hiring for rev ops and enablement roles, anyone trying to assess genuine AI fluency in a candidate, and leaders trying to figure out whether to fix or rebuild a broken tech stack. Tom covers how to design interview tasks that actually filter out AI-assisted bluffing, why data architecture has to come before any AI investment, and why he believes most in-house rev ops teams are heading toward a leaner, agency-supported model. Key Takeaways - Anyone can say the right words about AI. The skill to look for is whether they say them in the right order. - A well-formatted slide deck rarely comes from an LLM. - The real skill of a modern leader is asking great questions, not generating long documents. A poorly contextualized prompt produces a generic report. A precisely framed one, with real business context, produces something genuinely useful. - Most companies build a Frankenstein system: one tool bolted onto another, with no central data architecture. Fixing it is often more expensive than starting over. Choose your core platform, consolidate around it, and hire someone certified in that system who can create value from day one. - Token efficiency is becoming a real cost center. A well-structured org with clean markdown files and a clear context layer can get the same output from a fraction of the tokens that a messy, siloed system requires. - Bring in rev ops expertise earlier than feels necessary. The companies that build the scaffolding first avoid building a Leaning Tower of Pisa they will need to tear down and rebuild later. Chapter Markers (00:00) Cold open: why AI gets complex processes that humans struggle to describe (01:52) How Tom took his rev ops and enablement team from 10 to 2 (05:05) Why in-house rev ops is becoming harder to justify (09:21) The hidden cost problem: token usage and clean data (13:29) Tool fatigue and the challenge of leading through constant change (17:29) Spotting candidates who say the right words in the wrong order (18:37) Designing interview tasks AI cannot easily pass (22:09) Why hiring is one of the few things AI still cannot do for you (26:38) Fixing versus rebuilding a broken tech stack (31:22) Why Tom is going back to university to study machine learning (31:59) The Frankenstein's monster system and why it happens (35:19) Managing the cultural change to fix it for good (43:01) Wrap-up Useful Links & Resources Tom Andrews on LinkedIn: https://uk.linkedin.com/in/tommandrewsHiveBrite: https://www.hivebrite.io Connect With the Show Captivate Talent on LinkedIn: https://www.linkedin.com/company/captivate-talent/Danielle Parker on LinkedIn: https://www.linkedin.com/in/daniellemessler/

    The Frankenstein System Killing Your AI ROI with Tom Andrews

  5. Jul 21

    What Your First Marketing Hire Should Look Like at an Early-Stage Company with Kathleen Booth

    Most founders get their first marketing hire wrong. Not from lack of effort, but from hiring to a bar they haven't defined. You know you need a marketing leader, but you're not sure what seniority level fits your stage. You're drawn to the big-name resume, unsure how to test for AI fluency, and worried about spending six months discovering you hired for the wrong role. Kathleen Booth is VP of Marketing at Sequel.io and former SVP at Pavilion. She's built marketing teams across multiple B2B tech companies, recently navigated a deliberate job search in the AI era, and is now building an AI-first marketing function from scratch at an early-stage SaaS company. This conversation gives you a practical framework for matching your marketing hire to your actual stage of growth. You'll walk away knowing how to test for the qualities that matter most, what to prepare before you even write the job description, and why the big-logo CMO might be the most expensive mistake you make. Kathleen and host Danielle Parker dig into the real tension between AI capability and core marketing fundamentals, why founders confuse product-market fit with marketing talent, and what "figure it out factor" actually looks like in a candidate. This one's for early-stage B2B tech founders and revenue leaders making their first or second GTM hiring decision. Key Takeaways >> Founders often mistake the person for the product with many celebrated CMOs will tell you their success came from a product that sold itself, not a secret playbook. >> Testing for AI fluency means asking candidates to share their full prompt conversation, not just the polished output but how they challenge the AI reveals more than what it produced. >> Before engaging a recruiter or writing a job description, founders should build a "data room" for the hire: clear targets, the budget logic behind them, and which marketing discipline (demand gen, product marketing, or brand) they actually need first. >> The CEO-marketing leader relationship lives or dies on trust and you can hire the best marketer in the world, but if you can't communicate honestly with each other, it won't matter. Chapter Markers 00:00 - Why building AI-first marketing means doing the work yourself 01:28 - Kathleen's deliberate move from Pavilion to Sequel.io 04:17 - Clean slate vs. change management in marketing builds 05:41 - Finding the rare combo of product-market fit and no existing team 07:46 - The AI resume: showing your work before they ask 11:24 - VP vs. CMO: matching seniority to company stage 15:53 - The "high figure it out factor" and how to test for it 19:02 - Practical exercises in hiring: controversial but essential 22:52 - AI fluency vs. core marketing fundamentals 27:08 - The bookend strategy: owning inputs and outputs with AI 31:34 - Why founders reach for the wrong marketing leader 37:05 - What founders need to prepare before starting the search 39:06 - How to find a marketer who fits your stage and culture 41:40 - The one thing founders should do 3–6 months before hiring Useful Links & Resources Kathleen Booth on LinkedIn: https://www.linkedin.com/in/kathleenslatterybooth/Captivate Talent: https://www.captivatetalent.com Connect With the Show Captivate Talent on LinkedIn: https://www.linkedin.com/company/captivate-talent/Danielle Parker on LinkedIn: https://www.linkedin.com/in/daniellemessler/ What's the biggest mistake you've seen (or made) when hiring a first marketing leader? Drop it in the comments, we'd love to hear what you learned the hard way. If this episode made you rethink your next hire, subscribe and share it with a founder who needs to hear it before they post that job description. Visit captivatetalent.com to learn how we help B2B tech companies hire exceptional GTM talent.

    What Your First Marketing Hire Should Look Like at an Early-Stage Company with Kathleen Booth

  6. Jul 7

    AI Can Rank Your Candidates, But It Shouldn't Pick Who You Hire with Cassie Chao Leemans

    Most people using AI in recruiting are asking the wrong question. They want to know how much they can automate. Cassie Chao Leemans wants to know where the human has to stay in the loop, and she has built her entire workflow around that distinction. Cassie is VP of Talent at Craft Ventures, a team of one, and a recruiter with 15 years of experience scaling teams at Uber, Palantir, and Threads. Over the past year she has built her own AI-native CRM from scratch using Claude Code, with no engineering background, that automates the back-end work of recruiting while keeping every hiring decision firmly in human hands. Her take on where AI belongs in the recruiting process, and where it absolutely does not, is one of the clearest frameworks we have heard on the show. This episode is for early-stage founders thinking about their first recruiting hire, talent leaders looking to build smarter workflows without losing the human element, and anyone trying to figure out where to draw the line between AI and human judgment in hiring. Cassie covers build versus buy decisions, the danger of AI-written scorecards, why inbound has become a noise problem, and the one question every founder should ask before handing any part of their hiring process to a machine. Key Takeaways >> AI should amplify what you are already doing, not make decisions for you. The moment AI starts filtering candidates in or out without a human reviewing why, you are introducing false positives and false negatives that will cost you hires you cannot afford to lose. >> Start with your biggest pain point and automate that one thing first. You do not need to overhaul your entire workflow. Find the task you are repeating over and over and start there. >> AI-written scorecards are a red flag. If you are not the one evaluating whether a candidate collaborated well or showed initiative, you are outsourcing your own judgment to a model that was not trained on your values or your definition of great. >> The human moat in go-to-market hiring is interpersonal judgment. No AI can evaluate the nuances of how someone sells, collaborates, or shows up in a room. That assessment has to come from a human who has had the conversation. Chapter Markers (00:00) Cold open: Why humans have to stay in the loop (01:54) How Cassie built an AI-native CRM as a team of one (04:33) Build versus buy: why she stopped looking for an off-the-shelf solution (10:34) How early-stage founders should think about automating recruiting workflows (15:00) Does Cassie fear building too deep on one set of tools (22:10) Where Cassie draws the hard line between AI and human in hiring (26:45) Can you hire a great go-to-market person using AI alone (31:40) What founders get wrong about handing off recruiting (36:55) The one thing founders should and should not do with AI in recruiting (39:20) Selling versus buying market: how founders should think about talent right now (41:45) Wrap-up and where to find Cassie Useful Links & Resources Cassie Chao Leemans on LinkedIn: https://www.linkedin.com/in/cassieleemans Craft Ventures: https://www.craftventures.com Connect With the Show Captivate Talent on LinkedIn: https://www.linkedin.com/company/captivate-talent/ Danielle Parker on LinkedIn: https://www.linkedin.com/in/daniellemessler/

    AI Can Rank Your Candidates, But It Shouldn't Pick Who You Hire with Cassie Chao Leemans

  7. Jun 23

    What is a GTM Engineer? Hiring, AI and the Future of RevOps with Lauren Hughes of Justworks

    Here's how to tell if you need a GTM Engineer and what to fix first. Lauren Hughes is VP of Revenue Effectiveness at JustWorks, where she rebuilt the entire function from the ground up by merging rev ops and enablement, introducing GTM engineering roles, and hiring AI enablement specialists. Roughly 75% of her team is either new or in a completely different seat than before. From this episode of Human-First, you'll walk away knowing exactly how to diagnose whether you need a GTM engineer or a Salesforce admin, how to assess builder mindset in interviews when no one has "10 years of GTM engineering experience" on their resume, and what foundation you need in place before any of it matters. This one's for founders, revenue leaders, and rev ops professionals trying to figure out what their team should actually look like right now and what it needs to become. We cover GTM hiring for technical roles, AI fluency as a hiring bar, case studies in the age of AI, and why your operating maturity determines your next hire. Key Takeaways >> If your pain is "Salesforce hygiene is bad and routing rules need fixing," you need an admin. GTM engineers are for inventing capabilities that don't exist yet, not maintaining what does. >> The strongest GTM engineer candidates deconstructed the problem from a data perspective and came with a complete system redesign, while average ones just talked about process improvements. >> Case studies are more critical than ever precisely because of AI with the real test is whether candidates can defend and explain what they built when questioned live. >> Before hiring a GTM engineer, get your data foundation right with clean infrastructure, clear ownership, and system discipline. AI and builders can't fix what's fundamentally broken underneath. Chapter Markers 00:00 - What a GTM engineer actually does 01:54 - Why JustWorks rebuilt revenue effectiveness from scratch 05:17 - How the rebuild played out: roles added, removed, and renamed 08:51 - GTM engineers vs. Salesforce admins: where the confusion comes from 14:58 - How to interview for builder mindset with no job title precedent 18:05 - Case studies in the age of AI: what's changed and what still works 21:02 - The AI fluency bar: what Lauren asks every candidate 24:08 - AI enablement roles and just-in-time personalized learning 29:52 - The flattening org: why middle management layers are thinning 33:44 - Career paths in rev ops: skills-based, not tenure-based 35:34 - What early-stage founders should do before hiring a GTM engineer 38:35 - Rev ops as the entry point for AI across the org 41:07 - Where Lauren learns: podcasts, communities, and vendor dinners 45:32 - The one thing to get right before writing a job description Useful Links & Resources Lauren Hughes on LinkedIn: https://www.linkedin.com/in/laurenehughes10023/ Captivate Talent: https://www.captivatetalent.com Connect With the Show Captivate Talent on LinkedIn: https://www.linkedin.com/company/captivate-talent/Danielle Parker on LinkedIn: https://www.linkedin.com/in/daniellemessler/ What's your take? Is the GTM engineer title getting ahead of what most teams actually need? Drop your thoughts in the comments, or tell us what your rev ops team looks like right now. We'd love to hear how you're navigating this shift. Visit captivatetalent.com to learn how we help B2B tech companies hire exceptional GTM talent. If this episode was useful, like, subscribe, and share it with someone building out their rev ops function.

    What is a GTM Engineer? Hiring, AI and the Future of RevOps with Lauren Hughes of Justworks

  8. Jun 10

    The Framework Behind a Great First GTM Hire with Rav Dhaliwal

    A lot of founders get their first go-to-market hire wrong. Not because they pick the wrong resume, but because they skip the thinking that comes before the job description. You've raised the round, your investors are pushing you to scale, and you're copy-pasting job descriptions from companies ten times your size. But you don't actually know what "great" looks like for this role at your stage, and the cost of getting it wrong won't show up for 12 to 18 months. Rav Dhaliwal is a partner at Crane Venture Partners who spent the first half of his career as an operator building GTM teams at Slack, Zendesk, and Yammer. He's made every early-stage hiring mistake possible and now helps founders avoid the same traps. You'll walk away with a clear framework for deciding whether you're actually ready to hire, the math to pressure-test your plan, and a structured approach to interviewing that separates real performers from polished storytellers. This is practical, data-backed hiring advice you can use immediately. This episode is for B2B tech founders making their first (or next) GTM hire, VCs advising portfolio companies on GTM buildout, and revenue leaders navigating the shift from founder-led sales. Rav covers readiness signals, scorecard design, behavioral interviewing, and the critical transition from founder-led to founder-managed sales. Key Takeaways >> Before hiring an AE, work backward from OTE to pipeline: if you can't generate 3 - 4x qualified pipeline against their quota target, the timing isn't right, no matter what your investors say. >> The four types of salespeople willing to join before you've figured out your market are almost never the ones you want. Be aware of poor performers, lucky riders, stealth consultants, and semi-retired operators. >> A job description lists activities; a hiring scorecard defines mission, outcomes, and behavioral competencies and it's the difference between collecting useful interview data and getting sold by a good storyteller. >> Test intrinsic motivation before you test skills: why someone wants to leave, why they want to join you specifically, and whether their career goals align with what you're actually offering. Chapter Markers 00:00 - Why early-stage GTM mis-hires create management debt 02:07 - What a hiring mistake actually costs in practice 04:30 - How to know if it's the right time to hire 06:54 - The OTE-to-pipeline math every founder should run 09:32 - Pushing back when investors pressure you to scale 10:14 - Four types of salespeople who'll join too early 12:49 - Sussing out stage fit in interviews 16:04 - Behaviors vs. skills: where the conviction comes from 18:49 - Behavioral interviewing in practice with real examples 22:01 - The hiring scorecard: mission, outcomes, competencies 28:53 - Early warning signs of a GTM mis-hire 30:37 - Founder-led sales to founder-managed sales 34:38 - Why the Frankenstein job description is dangerous 36:31 - Testing intrinsic motivation in screening calls 41:18 - Key takeaways and wrap-up Useful Links & Resources Rav Dhaliwal on LinkedIn: https://www.linkedin.com/in/ravinderdhaliwal/Who by Geoff Smart and Randy Street - the hiring framework referenced in this episode: https://whothebook.com/Crane Venture Partners: https://crane.vc Connect With the Show Captivate Talent on LinkedIn: https://www.linkedin.com/company/captivate-talent/Danielle Parker on LinkedIn: https://www.linkedin.com/in/daniellemessler/Captivate Talent website: https://www.captivatetalent.com/

    The Framework Behind a Great First GTM Hire with Rav Dhaliwal
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AI is rewriting what "great" looks like across sales, marketing, customer success, and RevOps. Old playbooks have stopped working. And most founders are hiring in the dark. Human-First: The GTM Hiring Show cuts through the noise with high-signal conversations for B2B Tech founders, revenue leaders, and the VCs who back them - from the team at Captivate Talent. Unlike other shows about hiring, we pull back the curtain on what a search actually looks like: real funnel data, real candidate feedback, and the kind of market intelligence that helps you make decisions based on reality, not guesswork. Every episode is built around three questions every leader asks before making a GTM hire: Am I ready to hire? (And what to do when the honest answer is "not yet")How do I hire? (How to run a tight process and spot "great" when you've never seen it before)Did I hire the right person? (How to diagnose whether it's the role, the comp, the process, or the person) Each episode features founders, revenue leaders, VCs, and operators who've made the hard calls firsthand - sharing what worked, what didn't, and what they'd do differently. We also tackle the AI fluency question head-on: not the hype, not the fear, just what's actually changing in GTM hiring and how to evaluate it in candidates. Human-First is produced by Captivate Talent, a boutique recruiting firm specializing in go-to-market hires for seed through Series B B2B Tech companies. If you're building a GTM team and want to hire with more clarity and less chaos, this show is for you. Subscribe so you don't miss an episode - and join the founders, VCs, and revenue leaders already listening.

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