Human-First: The GTM Hiring Show

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

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/

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/

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/

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.