AI That Sells (and When It Doesn’t): VALR Advisors’ Mike Brunnick on GTM Fit, Coaching Agents, and RepFit

Summary

AI is everywhere—but how do you turn it into real revenue without drowning in generic outputs and “productivity theater”? Mike Brunnick, founder and CEO of VALR Advisors and interim CRO at HyperLayer, shares a holistic playbook for diagnosing revenue gaps across the entire go-to-market engine. Drawing on 25 years leading services, sales, and customer orgs—and seven years as a Marine infantry officer—Mike explains the VALR framework (Vision, Action, Leadership through change = Results) and why bravery means choosing one strategic initiative and saying no to the rest. He breaks down how to govern GenAI for true productivity, encode your company’s differentiation into closed-loop agents, and why AI sales coaching tools like GRW.AI can fill the widening gap left by stretched first-line managers. Mike also unveils RepFit, VALR's behavioral-science-plus-LLM assessment that quantifies sales-team quality with a predictive 0–100 score and practical coaching maps. Expect clear guidance on where you actually win, when to walk away, and why agentic buyers—not sellers—may be the next big shift.

Timestamps

[00:35] – From Marines to CRO: Mike’s path to VALR Advisors and interim CRO at HyperLayer

[02:05] – The GTM engine: holistic assessments across product, marketing, sales, CS, and services; the VALR framework and “brevity is bravery”

[06:10] – AI that drives outcomes: govern usage, avoid productivity sinks, and build closed-loop agents with your unique differentiators

[09:10] – Coaching at scale: GRW.AI (Grow) as an AI sales coach, rising spans of control, and lifting win rates

[12:30] – Measuring sales-team quality: introducing RepFit—behavioral science + LLMs to predict top-performer potential and guide org design

[17:35] – The biggest GTM challenge: knowing where you win and saying no to misfit deals

[19:40] – Agentic AI today and tomorrow: human sellers, automated CS, and why agentic buyers may arrive before agentic sellers

[22:05] – How to engage VALR: consulting, workshops, coaching, and assessments on VALRAdvisors.com

[23:15] – Parting advice: talk to employees about how they use AI—optimize for customer time and real productivity

Takeaways

- Govern AI usage and define “productive vs. busy” to prevent GenAI from becoming a time sink.

- Encode your company’s differentiation into closed-loop agents to avoid generic, undifferentiated outputs.

- Deploy AI sales coaching (e.g., GRW.AI) to augment overstretched first-line managers and improve deal velocity.

- Quantify sales talent with behavioral science plus LLM scoring (RepFit) to predict top performers and design stronger teams.

- Focus bravely: choose one strategic initiative, align KPIs, and walk away from poor-fit opportunities.

- Prepare for agentic buyers by tightening RFP responses, feature mapping, and seller readiness to “sell to an agent.”

AI to Pipeline: CMO Karen Sung on Channel-First GTM, LinkedIn Iteration, and an Automated SDR Engine

Summary

Drowning in AI-fueled noise but still expected to grow pipeline on a lean team? Karen Sung, Chief Marketing Officer at Cyber Defense Group, shares the practical playbook she’s using to turn AI into revenue. With 15+ years in B2B enterprise SaaS and deep cybersecurity experience, Karen details how she built a custom “COO” GPT that ingests SEO, web, and paid data to spotlight what truly moves pipeline—not vanity metrics. She unpacks her biweekly LinkedIn iteration cadence, why outcomes-based messaging beats fear-mongering in cyber, and how a channel-first GTM (complete with a relationship-driven podcast) lets every asset do double duty for partners and direct. Karen also flags common AI pitfalls—from copy-paste content to skipping SEO fundamentals—and previews an automated SDR engine, built in-house with Zapier and ChatGPT, that qualifies inbound, updates the CRM, and books meetings fast. She closes with the skills she’s hiring for now: marketers who can build AI-enabled systems, not just prompts.

Timestamps

[00:25] – Karen’s path to B2B cybersecurity and what draws her to marketing

[01:40] – AI as a force multiplier: faster iteration, signal detection, and personalization

[03:06] – Building a “COO” GPT to synthesize SEO, web, and paid data into pipeline impact

[04:28] – LinkedIn ads: a biweekly iteration rhythm and practical A/B testing with ChatGPT

[05:45] – Cutting through cyber noise with outcomes-based messaging (not doom-and-gloom)

[06:50] – Channel-first GTM: partner enablement + a relationship-driven podcast

[07:59] – AI pitfalls and SEO foundations: why human judgment still matters

[13:09] – Prototyping an automated SDR engine with Zapier + ChatGPT for speed-to-lead

Takeaways

- Build a channel-first GTM so every asset serves partners and direct—make content do double duty.

- Use AI to connect marketing metrics to pipeline; prioritize signals over vanity numbers.

- Iterate LinkedIn ads on a fixed cadence; keep a running “memory” and test small variations.

- Differentiate with outcomes-based messaging and competitive analysis, not fear tactics.

- Treat AI as a copilot, not a copier—human-edit for quality and reinforce SEO fundamentals.

- Automate speed-to-lead: qualify inbound, route to CRM, and trigger outreach with Zapier + ChatGPT.

Move Faster Together: AI-Ready GTM, Data Discipline, and Offline Wins with Clara De Lima

Summary

AI is everywhere in sales and marketing—but how do you move faster without losing brand, clarity, or alignment? Clara De Lima, Head of Enterprise Marketing at Function Health, shares a practical playbook for AI-enabled GTM. A zero-to-one B2B marketer, Clara explains how to build shared speed across teams, not just individual productivity. She covers embedding AI across your stack, creating an internal GPT to enforce brand/legal/product terminology, and tightening data infrastructure so marketing, sales, product, and post-sales can act on the same signals. You’ll hear why AI can overcomplicate messaging and make you sound like competitors—and how to counter with human taste, read-aloud edits, and rigorous testing. Clara also tackles the tougher top of funnel, advocating for high-touch offline plays (small events, dinners) that build real relationships and out-ROI many digital bets. She closes on what’s next: AI chat across tools, improved visibility, and why clean inputs still determine outcomes—plus her core leadership principle: bring your team along so everyone moves at speed.

Timestamps

[00:45] – Clara’s path to Function Health and leading zero-to-one B2B GTM

[01:49] – AI embedded in every GTM tool: new expectations and data deluge

[03:06] – Data in, data out: aligning marketing, sales, product, and post-sales

[04:49] – Brand guardrails: an internal GPT for tone, legal, and product terminology

[07:02] – Prompting in practice: AI as sounding board, audio prompts, and copy-editing

[09:28] – How AI reshapes GTM: team speed, testing discipline, and creative generation

[15:16] – Cutting through TOFU noise: offline relationship plays and ROI vs. digital

[18:12] – What’s next: AI chat across tools, visibility, clean data—and Clara’s parting advice

Takeaways

- Build a shared AI backbone: create an internal GPT with brand, legal, and product language.

- Tighten data infrastructure so GTM teams can test, learn, and act on the same insights.

- Use AI as a thought partner; prompt for critique, then apply human taste and read-aloud edits.

- Scale A/B tests on AI-generated creatives but enforce brand uniqueness to avoid competitor sameness.

- Balance the funnel with offline relationship plays (small events, dinners) that deliver outsized ROI.

- Prioritize team velocity over solo speed—share tools, context, and learning so everyone levels up.

Beyond the AI Hype: Mark Kilens on GTM Focus, AI Search, and Clean Data

Summary

Drowning in AI noise and tool fatigue? This episode reframes growth around people, data, and disciplined go-to-market execution. Mark Kilens—VP of Marketing at EasyLlama, former HubSpot and Drift leader, and founder of TAC/GTMLibrary—explains why starting with tools is the fastest way to waste money. He shares how to turn customer conversations into a “second brain,” the shift from chasing SEO rankings to influencing AI search engines like ChatGPT and Gemini, and what multi-product GTM looks like as EasyLlama scales past $20M ARR. Expect candid takes on the AI bubble and agentic hype, practical ways to mine call recordings for competitive and pricing insights, and a clear distinction between GTM strategy and growth playbooks. Mark also covers value articulation in the sales process, why clean data is the real bottleneck, and what early-stage teams should prioritize: a minimum viable audience before everything else.

Timestamps

[00:00] – The AI bubble, hype cycles, and why pain is coming

[01:25] – Mark’s path: HubSpot, Drift, TAC, and joining EasyLlama

[02:54] – Start with people, not tools: rethinking AI adoption

[04:40] – Turn call recordings into your “second brain” with LLM analysis

[06:58] – From ranking to influence: winning with AI search and prompt monitoring

[10:29] – Multi-product GTM and scaling past $20M ARR; marketing- vs. sales- vs. partner-led

[12:59] – Converting inbound: articulating value, clean sales process, Gong and SendSpark

[18:05] – Agentic AI and “cloud employees”: the data quality problem

[19:58] – Playbooks for startups: MVA, GTM fit, and staying customer-obsessed

Takeaways

- Start with people and problems before tools; let customer behavior guide your GTM.

- Record every customer/buyer call and mine transcripts with LLMs for competitive, pricing, and messaging insights.

- Shift from SEO rankings to influencing AI search: monitor real prompts, seed off-site signals (analysts, PR, reviews, LinkedIn/Reddit).

- Define GTM strategy (marketing-, sales-, or partner-led) separately from growth playbooks; adjust by product and ICP.

- Train AEs to articulate value and ROI through storytelling; use tools to augment follow-up, not replace judgment.

- Fix your data before chasing agentic AI—clean, organized data is the prerequisite for any meaningful automation.

Building Trust at Speed: OpenServe’s Ryan Dennis on Agentic AI, Generative SEO, and “Go‑to‑Me” GTM

Summary

In an era of AI hype and zero‑click search, how do you build trust, move fast, and ship real outcomes? Ryan Dennis, Head of Marketing at OpenServe—the infrastructure layer for building agentic apps—unpacks a data-first playbook. After legitimizing crypto projects at ICO Alert, turning around sentiment at Tron/BitTorrent, and elevating brands like Stellar and TON, Ryan now applies agentic AI to modern marketing. He explains what an AI agent really is (goal-seeking, policy-bound, action-taking), why machine-native reasoning beats prompt wrappers, and how OpenServe orchestrates multi-agent workflows across your stack. Ryan details how he compares LLMs head-to-head with OpenArena, builds for generative answer engines (not just blue links), and avoids “tool-first” waste with guardrails and auditability. He also reframes GTM as “go-to-me,” shares why prediction markets and data-to-content win attention, and walks through a voice-agent demo that outperformed OpenAI by handling every requirement of a real transaction. Expect concrete systems, not slogans—and a reminder to pace yourself while the world races.

Timestamps

[00:45] – Guest intro: Ryan’s role at OpenServe and the case for agentic apps

[01:25] – Origin story: Bitcoin, real ownership, and a shift from ops to crypto

[06:00] – Legitimizing blockchain: vetting ICOs, Tron sentiment flip, BitTorrent, Stellar, TON

[10:05] – What an AI agent is and why machine-native reasoning matters; OpenServe orchestration

[11:50] – Ryan’s stack: multi-agent workflows, OpenArena model competitions, faster/cleaner outputs

[15:50] – SEO in the GAE era: build for answers, structure/freshness, measuring zero-click impact

[19:20] – Avoiding AI/MarTech pitfalls: tool-first spending, hallucinations, audit trails, ownership

[22:20] – GTM now = “go-to-me”: deep segmentation, respect your audience, data-as-content, prediction markets

[28:58] – Where to connect + parting advice on pace, priorities, and building a life you love

Takeaways

- Define and deploy agentic workflows: connect agents to email, Slack, Notion and let them act with policies and guardrails.

- Build for generative search: craft canonical explainers/comparisons with structured data, freshness, and citations to earn inclusion.

- Treat marketing as a system: create proprietary datasets, embeddings, and model competitions to raise quality and velocity.

- Spend with discipline: assign owners and KPIs before buying tools; test cheaply; require source citations and reasoning auditability.

- Reframe GTM as “go-to-me”: segment deeply, market to yourself first, and never pander—your audience is sophisticated.

- Turn data into content and culture: use prediction markets, publish model head-to-heads, and showcase demos with real task completion.

AI Is Rewriting GTM: Centrical’s CMO on GEO, Pipeline Velocity, and Brand Positioning

Summary

B2B go-to-market is getting rebuilt in real time—and marketing’s role is surging. Michael Ciancio, Chief Marketing Officer at Centrical (an employee performance and engagement platform), shares benchmark-backed insights on how AI is reshaping the entire revenue engine. He explains why AI’s real value is velocity, not replacement; how “GEO/AEO” (optimizing for AI search) is the new SEO; and why differentiation now lives in positioning and human storytelling, not “AI-powered” claims. Michael unpacks a first-mover edge in LLM citation worthiness, the shift to marketing teams as orchestrators, and how to build pipeline resilience with channel diversification and partnerships. He also details practical levers to speed deal cycles in a cautious market, the rise of customer marketing as a growth engine, and the daily prompting habits and tech fluency modern marketers need. Expect concrete guidance on avoiding “dysfunctional perfection,” structuring content for LLMs, and investing in brand and category earlier to shorten sales cycles.

Timestamps

[00:45] – Guest intro and the rise of marketing driving 50–60% of new business pipeline

[01:57] – AI across sales/marketing/CS: velocity over replacement; positioning as true differentiator

[04:26] – From SEO to GEO/AEO: optimize for LLM citations, authority, and brand guardrails

[06:51] – Cutting through AI noise: human storytelling, founder POV, and pain-led ads

[08:59] – GTM shifts ahead: orchestrator teams, pipeline diversification, and partnerships

[12:35] – Common mistake: “dysfunctional perfection”—why testing and failing fast wins

[13:53] – Skills that matter: tech fluency, GTM “engineering,” and conversational prompting

[18:00] – Pipeline velocity: fixing SAL→SQL friction and activating customers as a growth engine

Takeaways

- Optimize for GEO/AEO—structure content for LLM citation and authority, not just rankings and clicks.

- Use AI to boost velocity and free humans for strategy; keep human-in-the-loop for quality and brand voice.

- Lead with human storytelling that quantifies “investable pain” and backs it with real customer proof.

- Re-architect marketing as orchestrators/strategists; build tech literacy and daily prompting habits.

- Diversify pipeline and elevate partnerships; cap any single source at ~30–40% to reduce risk.

- Speed conversion, not just TOFU: instrument SAL→SQL, remove friction, and mobilize customers as advocates.

Hiring AI Teammates: Signals’ CRO on Cloud Employees, Agentic Orgs, and 90-Day ROI

Summary

AI is everywhere—but where does it actually produce measurable ROI? Gabe Larsen, Chief Revenue Officer at Signals and a veteran GTM leader whose previous startup was acquired by Meta, breaks down how to move from hype to impact with autonomous AI agents. At Signals, Gabe deploys “cloud employees”—job-based, omnichannel AI agents—for customer service, recruiting, and sales development, and runs a near 1:1 ratio of humans to agents on his team. He shares a crawl-walk-run playbook for adoption, when to buy vs. build, and the 30/60/90-day milestones that signal you’re on the right track. Gabe also explains how to stand out in a sea of “AI agents” by shifting from renting tools to hiring teammates, the emergence of the agentic enterprise, and why leaders should track an ECE ratio (employees vs. cloud employees). Expect tactical guidance on onboarding and managing agents, prompt frameworks that actually improve outcomes, and a pragmatic view of what’s next through 2026.

Timestamps

[00:45] – Guest intro: Gabe’s path from SaaS exits to Signals and “cloud employees”

[03:14] – Where AI wins vs. fails: four proven use cases and the power of volume + repetition

[06:09] – Adoption that sticks: crawl-walk-run, buy vs. build, and 30/60/90-day checkpoints

[08:18] – Stand out in a crowded AI market: from renting tools to hiring teammates

[11:25] – The autonomous organization: agentic enterprise and the ECE (employee-to-cloud-employee) ratio

[14:47] – Managing agents like teams: onboarding, KPIs, coaching, and prompt tuning

[17:46] – Prompting that works: don’t treat it like Google; use structured frameworks and natural dialogue

[21:13] – What’s next by 2026: from co-pilots to complex, more autonomous job-running agents

Takeaways

- Start with high-volume, repetitive roles (CS Tier 1/2, SDR, recruiting) to prove clear cost or revenue impact.

- Use a crawl-walk-run plan with ownership in-house; expect visible traction by 30–60 days and a working use case by 90.

- Decide buy vs. build by ambition and resources; even with partners, keep strategy and iteration internal.

- Differentiate your GTM: shift messaging from “AI tools” to “AI teammates” and name/position accordingly.

- Design for an agentic enterprise: balance human headcount with cloud employees and track an ECE ratio.

- Operationalize management: onboard, measure, and coach agents continuously; improve results with structured prompting.

Humans Over Hype: Docebo’s CRO on AI, Signal Scoring, and GTM Handoffs

Summary

If everyone has access to the same AI, where does competitive advantage come from? From your people. Mark Kosoglow, Chief Revenue Officer at Docebo and former Outreach sales leader who helped scale from zero to $250M, breaks down how to operationalize AI without losing the human edge. He shares how his team uses internal agents for research, open scoring with UserGems, and RAG-driven weighting to deliver the most convertible accounts to BDRs. Mark cautions against AI hallucinations and the “CliffsNotes mindset,” arguing that real learning fuels creativity and performance. He reframes “noise” in outbound as a timing and relevancy problem—and why brand awareness still matters. Then he gets tactical: defining crisp GTM handoffs using Winning by Design’s bow-tie model, setting units of measure for each handoff, building a Salesforce object to enforce acceptance criteria, and using AI to generate handoff notes. He closes with an operator’s view of GTM as engineering—and why sellers should let passion (not polish) lead.

Timestamps

[00:45] – Mark’s background: Outreach to Catalyst to CRO at Docebo

[01:51] – “Everyone has AI; humans are the differentiator” and where AI helps in GTM

[03:31] – The risk of AI hallucinations and resisting the “summary-only” habit

[05:33] – Internal AI agents vs. paid tools; practical research automations

[06:45] – Signal scoring with UserGems: multi-signal weights, RAG, and BDR daily focus

[08:19] – Outbound “noise” redefined: timing, relevancy, and why some noise builds brand

[10:15] – A creative tactic you can copy: building trusted reach on LinkedIn

[12:18] – Clean GTM handoffs: WBD bow-tie, units of measure, Salesforce objects, and rigor

[15:58] – GTM as engineering (concrete/rebar analogy) and emerging operator mindset

[17:18] – Where to follow Mark + his parting advice: passion over professionalism

Takeaways

- Treat AI as a force multiplier—advantage comes from teaching people to use it creatively.

- Guard against hallucinations: verify, read deeply, and build real expertise.

- Consolidate signals and score them; deliver prioritized, weighted lead lists to reps daily.

- Redefine noise: optimize for timing and relevancy while maintaining brand presence.

- Engineer GTM handoffs: define units of measure, acceptance criteria, and enforce in Salesforce.

- Coach sellers to let passion show—human credibility beats sterile professionalism.

Measure Twice, Scale Once: Fractional CMO Michael Baer on GTM Foundations and Post‑Sale Growth

Summary

If your GTM feels busy but not compounding, this episode shows how to fix it. Fractional CMO Michael Baer of TechCXO (22 years helping growth-stage companies) explains why early-stage teams must slow down to speed up—by sharpening positioning, defining ICPs, and mapping the customer journey before piling on tactics. With deep experience in B2B and healthcare, Michael breaks down why “strategy is a destination,” why there are no silver bullets in today’s noisy channels, and how content that gives value first builds the trust modern buyers demand. He also shares a concrete post-sale play: a simple onboarding and education sequence for a diabetes care service that tripled initial conversions and doubled session utilization—boosting LTV while fueling referrals. Expect candid guidance on sales–marketing alignment, practical nurture flows, and how to protect credibility in crowded markets.

Timestamps

[00:45] – Guest intro: Fractional CMO role and TechCXO’s 22-year model

[01:50] – From agency to B2B/healthcare: why Michael focuses on growth-stage companies

[03:12] – The “scrappy to scalable” inflection point: professionalizing brand and GTM

[05:14] – Most-overlooked GTM step: positioning, messaging, and clear ICP personas

[08:27] – Measure twice, cut once: why two weeks of strategy saves months of waste

[09:04] – No silver bullets: multi-channel plans, value-first content, and credibility

[13:46] – Aligning sales and marketing: mapping the journey from awareness to conversion

[19:29] – Post-sale wins: onboarding campaign that tripled conversion and doubled usage

[23:09] – Where to find Michael + parting advice: know your customer deeply

Takeaways

- Codify positioning, messaging, and ICPs before tactics to accelerate results and reduce waste.

- Map the full customer journey together with sales; align content, nurturing, and moments to ask.

- Lead with value: publish useful, problem-solving content before pushing for a meeting.

- Protect credibility—especially in regulated or expert-driven markets—by avoiding spammy outreach.

- Invest in onboarding and education to drive adoption, reduce churn, and lift LTV.

- Build a focused, multi-channel plan; resist chasing every tactic and commit to consistent execution.

From AI Hype to Revenue: Robin’s CMO on Data-First Personalization and Human-in-the-Loop Sales

Summary

AI has unlocked personalization at unprecedented scale—and flooded GTM with noise. Kari Hanson, Chief Marketing Officer at Robin (workplace operations platform), explains how to cut through. With a career spanning agency, high-growth startups, and IPO journeys, Kari shares where agentic AI belongs in sales (transactional) and where it fails without human trust (enterprise). She breaks down the real unlock for 1:1 marketing—clean, connected data—and how her team uses ChatGPT, Claude, and Gemini to accelerate creative work, not replace it. Expect specifics on turning call transcripts into mid-funnel personalized pages, using AI to scale content and ads, and freeing people for high-value touchpoints. Kari also demystifies GEO (AI search) strategy: why structured content and third-party coverage teach LLMs better than keyword stuffing, and why a pay-to-play model is likely coming. If you’re navigating AI’s double-edged sword, this episode gives you a blueprint to stand out with clarity, credibility, and results.

Timestamps

[00:00] – The AI noise problem: fatigue, trust, and why high-value POV wins

[01:56] – Kari’s career path to CMO: from agency to IPOs to Robin

[04:59] – Personalization at scale and the rise of agentic AI in GTM

[06:35] – Agentic sales: where it works (transactional) and where humans must lead (enterprise)

[08:46] – Tool stack (ChatGPT, Claude, Gemini) and why clean data is the bottleneck

[11:15] – Mid-funnel plays: transcripts to tailored pages; AI-assisted onboarding with human touch

[15:23] – Cutting through noise: “zag” visuals, humor, and freeing reps for real conversations

[20:42] – From SEO to GEO: structure, FAQs, third-party PR, and LLM pay-to-play predictions

Takeaways

- Clean your data first—use AI to dedupe, enrich, and connect CRM/MA so personalization doesn’t backfire.

- Deploy agentic AI for transactional flows; add human checkpoints for complex, trust-heavy enterprise deals.

- Turn long-form insights into multi-asset campaigns with AI (edits, snippets, resizing) while preserving a human POV.

- Personalize mid-funnel: mine call transcripts to build tailored pages addressing problems, value, and competitors.

- Optimize for GEO: structure content with bullets/FAQs and earn third-party coverage to “teach” LLMs.

- Prepare for LLM monetization: shore up data, content, and measurement now to switch on paid and scale later.

AI‑Native GTM: Billy Collins on Authenticity, Agentic Systems, and Top‑of‑Funnel Growth

Summary

With AI flooding every inbox, how do go-to-market leaders cut through without losing the human touch? Billy Collins, Partner at Eidra, a consultancy collective blending creativity, engineering, and data—shares how he’s building Eidra's U.S. presence while navigating AI’s promise and pitfalls. A former product and innovation leader turned commercial operator, Billy explains why his fully agentic cold-outreach experiments fell short, why authenticity now outperforms automation, and how he uses AI for industry-level research and brand building rather than one-to-one selling. He breaks down an AI-native top-of-funnel model—always-on content, social listening, and human-in-the-loop quality control—and flags first-mover opportunities like third‑party apps inside ChatGPT as new distribution channels. Billy also weighs the limits of digital twins, offers a practical framework for finding “arbitrage” in new platforms, and shares how to stay experimental without shipping AI-generated slop.

Timestamps

[00:00] – Why GTM leaders need an experimenter’s mindset to spot AI-driven arbitrage

[01:21] – Billy’s path from product/innovation to commercial leader and Eidra's U.S. growth mandate

[03:21] – Agentic outreach experiments: end‑to‑end automation vs. authentic 1:1 messages

[06:54] – Targeting smarter: deep industry research with AI (Gemini), human-in-the-loop, and the “chemistry” filter

[10:13] – Keeping outreach creative and real: focus on shared pain points and voice

[11:12] – The toughest GTM challenge: top‑of‑funnel, trust, and brand recognition in a new market

[14:03] – How far to automate: digital twins, video tools, and why the team stays human-first

[17:13] – SEO to AEO: authoritative content, agentic engines, and first-mover gains via ChatGPT apps

[21:21] – Where to follow Billy: LinkedIn thought leadership and occasional video

[22:08] – Parting advice: stay curious, run tight experiments, and revisit tools as models mature

Takeaways

- Run tight experiments: document, measure, scale what works, and kill what doesn’t—fast.

- Use AI for leverage, not replacement: apply it to industry research, content ops, and social listening; keep humans on tone and quality.

- Lead with authenticity in outreach: write in your voice; resist fully automated, end‑to‑end agentic campaigns.

- Build an AI‑native top of funnel: always‑on content, platform listening, and brand consistency to earn trust quickly.

- Pursue new distribution arbitrage: experiment with third‑party apps inside ChatGPT and other emerging channels.

- Be realistic about AEO: authority still wins—invest in credible thought leadership and performant web experiences before chasing shortcuts.

AI-First GTM in Heavy Industry: Digital Twin Spokesperson, YouTube SEO, and Master Prompts

Summary

If your buyers aren’t on social, rarely check email, and have zero tolerance for fluff, how do you earn attention and trust? Sarah Calkin-Ward, Director of Marketing at Fero Labs, explains how she scales a one-person marketing team with AI to reach process engineers and metallurgists in safety-critical environments. She shares why Fero Labs built an AI “digital twin” spokesperson—Pete Persona—to deliver thought leadership at scale, and why YouTube beat other channels for discoverability in an AI-driven search world. Sarah breaks down a rigorous win–loss analysis cadence to surface limiting beliefs that stall deals, then shows how “snackable” breadcrumb content and pillar events move conservative buyers forward. She also details how master prompts keep brand, persona, and regional nuance consistent across content, and how to train LLMs (and correct them) so they become reliable teammates. Expect concrete tactics any B2B team can borrow—from platform choices and persona design to prompt engineering that multiplies output without diluting quality.

Timestamps

[00:45] – Meet Sarah Calkin-Ward and Fero Labs’ mission: profitable sustainability for industrial processes

[02:26] – From B‑roll to Pete Persona: creating a digital twin spokesperson and choosing YouTube

[04:35] – Beating AI search bias: why authority sites win and how to ride YouTube, Wikipedia, Reddit signals

[06:08] – Unblocking deals: identifying and addressing buyer limiting beliefs before campaigns

[07:45] – Data in a niche market: win–loss analysis, sales probing, and internal reviews over volume

[09:51] – Channel strategy for busy, offline buyers: industry events and visitor-first YouTube content

[12:53] – Building an AI-powered stack: prompt engineering and master prompts for brand consistency

[16:13] – Training LLMs as teammates: persona/regional chats, catching errors, and tone replication

[18:19] – GTM focus: visibility, timing, and “snackable” breadcrumbing to invite deeper engagement

[20:03] – Parting advice: get behind AI or get left behind

Takeaways

- Build an AI spokesperson to scale thought leadership when executive time is scarce.

- Prioritize platforms favored in AI-driven search; use YouTube for speed, authority, and face recognition.

- Surface and neutralize buyer limiting beliefs with disciplined win–loss and sales-driven discovery.

- Create “snackable” breadcrumb content that delivers value fast and invites deeper conversations.

- Operationalize master prompts (company, brand, persona, region, product) to keep every asset on-voice.

- Treat LLMs as trainable teammates: correct inaccuracies, store prompts centrally, and iterate for consistency.

From SEO to GEO: Winning AI Search with Data‑Backed Thought Leadership

Summary

Is traditional SEO still worth it in the era of AI search? Founder and CEO of Artios, Andreas Voniatis, argues it’s “on life support” and lays out a practical roadmap to pivot from keyword-driven SEO to GEO—Generative Engine Optimization. A qualified accountant turned data scientist, Andreas retrained in machine learning, built his own LLM, and now helps companies become visible where buyers actually discover brands: inside AI systems. He explains why LLMs don’t crawl like search engines, why information gain (teaching AI something new) is the new ranking advantage, and why consolidating content into one authoritative URL beats publishing dozens of thin posts. You’ll hear how to renegotiate SEO contracts, ditch keyword tools, mine real buyer data from LinkedIn/Reddit/X or surveys, and craft people‑centric, evidence‑based thought leadership that passes AI fact‑checking. Andreas also previews what’s next—multimodal models that can ingest far richer content—and how to future‑proof your strategy now.

Timestamps

[00:45] – Meet Andreas Voniatis: accountant → SEO → data scientist; building an LLM and the wake‑up call for SEO

[02:58] – SEO vs. AI search: ETL, models, and why LLMs are people‑driven (not keyword‑driven)

[04:46] – The new ranking edge: information gain and teaching LLMs something they don’t know

[06:06] – Content architecture shift: one robust URL > dozens of posts; energy cost and model constraints

[07:04] – What to stop and start: renegotiate SEO scope, ditch keyword tools, invest in data research

[08:41] – Finding real buyer questions: data mining LinkedIn/Reddit/X and running surveys

[10:33] – Building E‑E‑A‑T: founder/SME commentary, contrasting perspectives, and passing AI fact‑checks

[12:22] – What’s next: saturation, multimodal LLMs, richer content experiences, and interactive formats

[21:17] – SEO and social converge: buyer‑centric GEO content that works on site and in feed

[24:56] – Where to find Andreas + parting advice for preparing now

Takeaways

- Prioritize information gain: publish data‑backed, buyer‑centric thought leadership that teaches LLMs something new.

- Consolidate related topics into a single, authoritative URL to align with how models ingest content.

- Redefine SEO vendor scope to technical discoverability/indexation; retire keyword‑led content planning tools.

- Mine your market: scrape LinkedIn/Reddit/X or run surveys to uncover real prompts, language, and topics buyers care about.

- Boost E‑E‑A‑T: include founder/SME commentary and contrasting viewpoints; cite data and methods to pass AI fact‑checking.

- Future‑proof content: design richer, multimodal experiences (visuals, interactive elements) as models ingest more than text.

Cutting Through the Noise: AI, Specificity, and Enterprise GTM with Aware’s CRO Brian Krause

Summary

In a market flooded with messages and smarter buyers, how do you win complex enterprise deals without adding to the noise? Brian Krause, Chief Revenue Officer at Aware, angel investor, and strategic advisor, shares a playbook for precision go-to-market. Aware builds biometric software that helps organizations verify identities and combat deepfakes, synthetic fraud, and account takeovers—giving Brian a front-row view of both GTM transformation and AI-driven threats. He explains why the best sellers are “information brokers,” how AI should speed access to relevant insights (not replace relationships), and why Customer Success is the most overlooked frontier for AI-driven revenue. Brian also breaks down how to turn research-grade innovation into clear differentiation, why use-case specificity beats generic claims, and where chatbots help—and fail—in technical enterprise sales. He closes with practical security advice for leaders and consumers: be vigilant, and choose providers with biometrics and step-up authentication. 

Timestamps

- [00:01] – Guest intro and career path: from Wall Street to CRO at Aware

- [01:44] – Pace and specificity: why generalist selling no longer works

- [02:46] – AI’s real role in enterprise sales—and the overlooked Customer Success opportunity

- [05:54] – Practical tools: internal automation and arming reps with timely intel

- [08:56] – Fighting deepfakes: translating R&D into differentiated GTM

- [10:55] – Cutting through the noise: use-case messaging and consultant-level selling

- [14:59] – Bots vs. humans in complex sales: where chatbots help and where they don’t

- [17:07] – Protecting against AI-driven fraud: vigilance and biometrics-first security

Takeaways

- Anchor your GTM to precise use cases and the customer’s decision criteria—not broad product claims.

- Use AI to arm sellers with timely, relevant insights; accelerate research without replacing human relationships.

- Elevate Customer Success with AI that surfaces multi-threaded account signals to drive retention and expansion.

- Curate data inputs: tight ICPs, personas, and governed feeds beat “boil the ocean” approaches.

- Translate deep technical work into clear differentiation backed by proof points and demos.

- Strengthen security by choosing providers with biometrics and step-up authentication; continually train teams to spot phishing and deepfakes.

GTM in the GenAI Era: ActiveFence’s CRO on Safety, Research-Led Outreach, and Precision Prospecting

Summary

GenAI is surging—and so is the noise. How do you build pipeline, stay credible, and keep customers safe in the process? John O’Donnell, Chief Revenue Officer at ActiveFence, shares how his team protects 3B+ users while helping enterprises deploy AI responsibly. ActiveFence partners with the world’s largest social platforms and foundation model companies, powered by a world-class research team and a “database of evil” that tracks bad actors and emerging threats. John breaks down a research-led outreach motion that leads with value, not hype; how tools like Clay and Gong sharpen targeting and speed execution; and why “go-to-market” is becoming “go-to-network.” He also covers the biggest GTM challenge right now—filtering FOMO-fueled noise—and how to qualify by GenAI maturity stage. Expect practical tactics to mobilize subject-matter experts without pulling them off their day jobs, build credibility even when mentioning competitors, and scale globally as AI agents move into production.

Timestamps

- [00:45] – Guest intro: ActiveFence’s mission and protecting 3B+ users online

- [01:04] – John’s path from Rapid7 to CRO and why AI safety is a GTM advantage

- [03:17] – Precision prospecting with GenAI and Clay: from “wide nets” to right-time outreach

- [05:57] – Research-led messaging: dark web intel, value-first content, credibility at first touch

- [07:29] – Today’s GTM challenge: filtering FOMO noise and qualifying by GenAI maturity

- [09:15] – Operational leverage with Gong: CRM automation, email summaries, and faster follow-up

- [11:59] – Mobilizing SMEs: using AI to turn expert insight into scalable content (without the drag)

- [14:17] – Go-to-network: everyone sells; overcoming “salesy” fears and earning peer trust

- [18:16] – What’s next for ActiveFence: securing GenAI agents and global expansion

- [20:59] – Parting advice: continuous learning as a competitive edge

Takeaways

- Lead with intelligence: share research and threat insights upfront to earn trust before the first call.

- Use Clay to segment by industry, role, and GenAI stage; personalize messaging to policy and risk.

- Automate the admin: deploy Gong to capture notes, summarize emails, and accelerate next steps.

- Turn SMEs into multipliers: use AI to draft white papers and briefs from expert input in hours.

- Build a go-to-network culture: equip non-sales teams to answer peer questions credibly—even when mentioning competitors.

- Qualify for signal: map prospects by GenAI deployment maturity to focus timing, message, and resources.

Beyond Leads: Jeanne Hopkins on VoC, Tech Stacks, and Community-Led Growth

Summary

If your marketing still revolves around net-new leads, you’re leaving growth on the table. Jeanne Hopkins—former VP of Marketing at HubSpot (employee #100) and veteran B2B/B2C leader with roles at LEGO and Symmetricom—explains why the best marketers own the voice of the customer and build systems that align product, sales, and support. Jeanne shares how she launched an internal blog to unite global teams before “content ops” was a term, why monthly cross-functional VoC forums beat ad-hoc requests, and how to use AI and competitive intel for sharper battlecards without losing focus on product-market fit. She breaks down a practical tech stack audit in a CFO‑driven era, the community and partner strategies that scaled HubSpot (including webinars drawing 25,000 registrants and a partner channel now driving ~50% of revenue), and the change-management pitfalls when product UX shifts too far from what users know. Expect actionable guidance on customer marketing, channel mix, and budget discipline that travels from startup to enterprise.

Timestamps

- [00:45] – Jeanne’s career arc: journalism roots, accounting degree, LEGO to Symmetricom, and a lifelong learning mindset

- [03:26] – Building “Symblog”: internal comms as a growth lever and the case for marketing-led alignment

- [05:26] – From stitched tools to integrated stacks: HubSpot’s role and why marketing is communication (inside and out)

- [07:27] – Owning VoC: the monthly “three things customers want” meeting to align GTM and product roadmaps

- [11:16] – Practical AI and comp intel (Crayon): battlecards, differentiation vs. commodity, and PMF realities

- [14:14] – CFO-era decisions: how to rationalize your tech stack, avoid redundancy, and justify spend

- [16:19] – Content as a magnet: HubSpot’s inbound play, 25k-registrant Facebook series, and community that scales

- [19:13] – Channel strategy beyond direct: partners, e-commerce, and the exponential effect of shared content

- [26:19] – Personalization starts offline: listening to sales calls, calling customers, and the 80/20 retention gap

- [28:13] – Change management lessons: when a UX overhaul backfires and how to protect user continuity

- [30:36] – Parting advice: partner with your CFO, love your budget, and remember—the logo color doesn’t matter

Takeaways

- Own the voice of the customer by running a monthly cross-functional forum that prioritizes the top three customer needs across sales, support, marketing, and product.

- Audit and rationalize your tech stack: identify owners, budgets, renewal dates, overlap, and auto-renew traps; cut redundancy before the CFO does it for you.

- Use AI and competitive intel to accelerate research and battlecards, then anchor messaging in real differentiation and product-market fit—not “arts and crafts.”

- Build community-led content (series, how-tos, webinars) that teaches; package and repurpose to compound reach over time.

- Scale with partners and channels, not just direct: enable resellers and agencies to share your content and extend your brand.

- Prioritize customer marketing: listen to sales calls, proactively interview the “other 80%,” and turn retention and expansion into core growth engines.

- Manage change deliberately: protect user workflows, test with real customers, and avoid disruptive redesigns that break adoption.

Winning Pipeline in an AI Era: Scott Leese on Go-to-Network, Creative Prospecting, and Sales Fundamentals

Summary

Pipeline is harder to build, inboxes are noisy, and AI is changing how buyers buy—so what actually works now? Bestselling author and go-to-market advisor Scott Leese joins Growth Wizards to share a pragmatic playbook for winning deals in 2025. Scott advises seed to Series C companies on GTM, sales ops, and RevOps, runs a micro-VC, coaches 65+ CROs/VPs, and even operates an Austin landscaping business—giving him a rare vantage point across tech and traditional services. He breaks down where AI is useful today (coaching and mid-funnel automation), why agent-to-agent selling is closer than most think, and how to apply AI advantage in “slow adopter” industries. Then he dives into the creative, unscalable tactics that are working now—shifting from go-to-market to go-to-network, hosting micro-events, and standout direct mail—to open doors when cold calls and email underperform. Finally, Scott brings it back to basics: diagnosing real pain, creating urgency, quantifying impact, and closing with discipline. He also previews his 24-hour Midnight to Millions virtual sales event and closes with a challenge: track network growth as a core sales KPI.

Timestamps

- [00:45] – Who is Scott Leese: advisor, coach, founder, and multi-business operator

- [02:06] – Time management without AI assistants: partners, fast decisions, publish and ship

- [04:28] – Practical AI in GTM: coaching platforms, AI demo agents, and applying AI beyond tech

- [07:31] – Will AI own the funnel? Agent-to-agent sales timelines and what’s realistic

- [11:25] – Creativity that cuts through: go-to-network, micro-events, direct mail, and unscalable moves

- [16:21] – Back to basics: find pain, create urgency, differentiate clearly, quantify ROI, follow up

- [18:25] – Today’s GTM gap: pipeline first, then urgency, then fundamentals

- [19:34] – Midnight to Millions preview and why breadth of topics matters

- [22:05] – Parting advice: grow your network and measure it like a KPI

Takeaways

- Build a go-to-network engine: expand referrals and affiliate intros to open higher-converting opportunities.

- Do the unscalable to stand out: host invite-only dinners, micro-events, morning runs, or airport rides.

- Use creative direct mail to earn attention: thoughtful, on-brand gifts beat crowded inboxes.

- Apply AI where it’s ready now: coaching, discovery, and mid-funnel tasks—then expand beyond tech.

- Master the fundamentals: diagnose real pain, make it urgent, quantify impact, and close decisively.

- Track network growth as a core KPI and reward introductions with meaningful referral commissions.

From QVC to AI-Driven Enrollment: Mickey Baines on Data Hygiene, Frictionless Journeys, and 1:1 Outreach

Summary

What can a live TV toy demo teach leaders about building high-performing, AI-enabled teams? Mickey Baines, President and Chief Revenue Officer at Kennedy & Company Education Strategies, shares how seven-minute QVC segments shaped his approach to feedback, messaging, and execution in higher education. Mickey breaks down how to translate features into benefits, set up disciplined AI experimentation, and move from broad segments to truly one-to-one outreach. He explains why you must start with foundational prompting skills, align “AI toys” to strategy, and invest in clean, connected data to unlock predictive intent, next-best-action communications, and natural-language access to enterprise metrics. You’ll hear practical examples—from cutting call volume in half by solving password resets upfront to mining unstructured data to spot friction in real time—plus a maturity model for advancing toward personalized engagement at scale. He closes with how to keep your flywheel spinning: keep learning, assessing, and applying.

Key Timestamps

- [00:45] – Mickey’s path: higher ed practitioner to consultant—and six years selling toys on QVC

- [02:24] – Live TV lessons: revenue-per-minute, ultra-specific feedback, and performing under pressure

- [07:13] – Turning features into benefits: messaging that resonates with buyers and students

- [08:40] – The AI “toys”: start with prompting, then run directed experiments aligned to strategy

- [11:57] – Predictive intent and 1:1 automation: voice agents, video, and next-best communication

- [13:50] – Natural-language analytics: querying connected data so leaders self-serve insights

- [15:49] – Removing friction: password resets, proactive comms, and using unstructured data to spot issues fast

- [19:43] – The hard truth: data hygiene, integration, and feature engineering to reach true personalization

- [24:57] – Where to find Mickey: Spark and Scale podcast and LinkedIn

- [26:18] – Parting advice: keep learning—feed the flywheel

Takeaways

- Start with prompting: build foundational skills and run directed, strategy-aligned AI experiments.

- Translate features into benefits to drive action; make the value obvious for your audience.

- Invest in data hygiene and integration; clean, connected data is prerequisite for predictive and 1:1 outreach.

- Use AI to surface friction from unstructured data and fix issues proactively (e.g., password reset bottlenecks).

- Prioritize by intent and prescribe next steps; move from broad segments to automated, one-to-one engagement.

- Enable natural-language access to metrics so executives and teams can self-serve insights in seconds.

Early-stage founders don’t need a $200K marketing hire to build pipeline—they need a system that scales. Justin Ashby, a Utah-based fractional VP of Marketing and former product marketing leader turned founder advisor, breaks down “go-to-market engineering”: stitching affordable tools and workflows to turn website intent into warm conversations.

With experience across 12 startups in the past eight months, BYU MBA venture training, and a service he calls “executive orchestration,” Justin shows how to activate founders on LinkedIn, personalize outreach for modern buying committees, and run rapid experiments that reveal winners fast. He shares the exact building blocks (think website tracking, enrichment, and automated—but human-sounding—multichannel follow-up), why AI should multiply—not replace—your efforts, and when a fractional leader outperforms a first full-time marketing hire.

Expect practical tactics like role-specific messaging to CFOs/RevOps/Marketing, gift-card and ABM plays (yes, even Lego kits), and scrappy in-person events that compress sales cycles.

Timestamps

- [00:45] – Justin’s path: product marketing, VC exposure, zero-to-one startups, and going fractional

- [04:49] – The first marketing hire dilemma: why fractional leadership de-risks early GTM

- [07:05] – Building a lean GTM engine: channels, partnerships, LinkedIn, and fast testing

- [08:45] – AI + GTM engineering: connecting tools and workflows without heavy “agents”

- [10:57] – The stack blueprint: website tracking → enrichment (e.g., Clay) → email (e.g., Instantly) → LinkedIn

- [11:39] – Executive orchestration: turning founders into consistent LinkedIn thought leaders

- [13:50] – Standing out: personalized, multi-threaded outreach for larger buying committees

- [17:20] – Experiment or fall behind: rapid iteration, ABM plays, and in-person events that accelerate deals

- [19:31] – How to connect with Justin and where he’s speaking next

Takeaways

- Engineer your GTM: connect website intent to enrichment, email, and LinkedIn outreach so follow-up happens automatically and fast.

- Activate executives as creators: use founder-led LinkedIn to drive awareness, intros, and trust at the top of the funnel.

- Personalize across the buying committee: craft role-specific messages for CFOs, RevOps, and functional leaders to multi-thread deals.

- Use AI as a multiplier: automate the grunt work but keep copy human, specific, and signal-rich.

- Run rapid experiments: test multiple motions monthly (email offers, ABM kits, partnerships), kill losers, and scale winners.

- Host scrappy in-person events: small meetups compress sales cycles and create high-quality conversations at low cost.

- Start fractional, then scale: bring in a senior operator to build the system and channels before hiring full-time.

Sponsor

This message is brought to you by Growth Wizards, the agency that uses podcasting as a major lead generation strategy. We handle the entire process from guest outreach, to scheduling demos. We target the right buyer that fits your exact ideal customer profile and the walk them through the entire podcast strategy so you get a consistent flow of high-ticket demos each month. Less spray-and-pray, more booked meetings with the right customer.

To learn more, message dave@growthwizards.io

What does it take to scale a global B2B startup into a unicorn? In this episode of Growth Wizards, host Tanner Green sits down with David Gales, Head of Marketing at Oyster, a global employment platform revolutionizing how companies hire and support talent across borders.

David shares how his early focus on customer experience and retention laid the foundation for Oyster’s scalable growth engine. From launching onboarding campaigns with authentic personal touches to building cross-functional frameworks with sales and operations, David breaks down the core systems that enabled Oyster’s global expansion.

He also dives into how the team uses tools like Clay for signal-based prospecting and AI-powered automation, how they run internal journey mapping workshops to close CX gaps, and why building the right infrastructure before scaling is essential.

Whether you're a startup marketer or a founder building toward scale, this conversation is full of actionable strategies you can apply today.

Timestamps

• [00:14] – David Gales introduces himself and Oyster’s global mission

• [01:32] – Early scaling challenges: Data scarcity and intuitive decision-making

• [03:15] – Why customer interviews and authentic outreach matter

• [05:59] – Building lifecycle frameworks and defining friction points

• [08:07] – Role of operations in aligning sales, CX, and marketing

• [10:33] – Tech stack essentials: How Oyster uses Clay for AI-led prospecting

• [13:29] – David’s advice for marketers building toward unicorn status

  
  

Takeaways

• Start with customer insight – Prioritize user interviews and real conversations before relying on data.

• Build before you scale – Invest early in operations, lifecycle mapping, and internal alignment.

• Use AI tools intentionally – Combine tools like Clay with automation for signal-driven prospecting.

• Design with delight in mind – Cross-functional CX workshops can uncover hidden gaps and drive better experiences.

• Test fast, learn faster – Rapid experimentation, followed by iteration, builds smarter marketing systems.