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.