Summary

Selling a complex offering while integrating two companies and riding the AI wave? 

Greg Davis, Chief Revenue Officer at Mark Monitor Group, breaks down how to simplify complexity, scale trust, and drive growth. Greg shares how the Mark Monitor–Com Laude merger came together around a “customer-first, white-glove” ethos—structuring leadership early, building SME overlays, and aligning sales with operations for seamless handoffs. 

He details how AI now powers their go-to-market: a 28-language website chatbot, regionalized content, intent signals for targeted outreach, and strict data guardrails. 

Greg also unpacks two repeatable plays—leaning into GlobalBlock in 2024 to protect brands (and double expected growth) and mobilizing for ICANN’s new .brand application window with specialists, a focused landing page, and fast expert routing. He closes with timeless lessons: lead with problems (not products), co-author ROI with customers, and keep sales, marketing, and RevOps speaking the same language.

Timestamps

[00:45] – From finance and trading to CRO: coaching, simplifying complexity, and commercial growth

[02:56] – Translating technical value to outcomes: multi-stakeholder selling and co-authoring ROI

[04:47] – Inside the Mark Monitor–Com Laude merger: culture fit, GTM integration, and customer-first guardrails

[08:38] – Building SME overlays and “white-glove” ops alignment to serve global accounts at scale

[10:09] – AI in GTM: 28-language chatbot, regionalized site, intent signals, and strict data protection

[14:58] – Customer-first campaigns that win: GlobalBlock as a protective play (and growth driver)

[17:49] – The .brand window: why ICANN’s new round matters for trust, security, and control

[20:57] – What he’d learned sooner + parting advice: problem-first selling and GTM alignment

Takeaways

- Lead with customer problems—then co-author success metrics and ROI with buyers.

- Operationalize “customer-first” in mergers: set leadership early, align systems, and design white-glove handoffs.

- Deploy multilingual chat + regional content to cut speed-to-lead and meet customers where they are.

- Use intent signals to personalize outreach; retire generic sequences.

- Add SME overlays across sales for deep, complex conversations without slowing deals.

- Lean into market shifts that protect customers (e.g., GlobalBlock) even if they seem to cannibalize short-term revenue—and prep now for the ICANN .brand application window.

Summary

If pricing AI is breaking your forecast, this episode shows you how to regain control. 

Ping Identity’s Product Management Director, Stefan Kontschinsky—formerly of PwC and Okta—breaks down how to price AI in a way CFOs can trust and customers value. 

He explains the critical difference between what you sell (per user, per usage, per outcome) and how customers buy (contracting and consumption), then shares concrete levers to restore predictability: real-time consumption dashboards, clear limits, and smart overage policies. 

Stefan makes the case for outcome-based pricing as a differentiator, with examples from Intercom, Fin.ai, and even SpaceX’s “cargo delivered” model. He walks through a practical 2x2 of pricing tactics (auto-upgrades, burst protection, committed capacity, pooled credits) and demystifies credits with a resource-unit approach he deployed at Okta. 

You’ll also learn his monetization flywheel—quantify, create, track, and communicate value—and how AI can unify siloed sales/CS data to inform packaging and prove ROI. We wrap with what’s next: more optionality (bundles, seats, credits), how to hedge against rising model costs, and why reducing vendor lock-in is now a pricing strategy.

Timestamps

[00:21] – Guest intro and monetization journey: PwC to Okta to Ping Identity

[02:34] – Pricing AI 101: what you sell (user/usage/outcome) vs. how you buy (contract/consumption)

[05:07] – CFO predictability: consumption dashboards, limits, and proactive overage policies

[06:34] – Stand out with outcomes: examples from Intercom/Fin.ai and SpaceX’s “cargo delivered”

[08:14] – Tactics that work: auto-upgrades, burst protection, committed capacity, pooled credits, resource units

[11:06] – The monetization flywheel: quantify, create, track, and communicate value across Sales and CS

[14:01] – AI for pricing/packaging: un-siloing data to shape bundles and prove value

[15:54] – What’s next: optionality, rising token costs, and reducing lock-in risk

Takeaways

- Separate what you sell from how customers buy to avoid defaulting to pure usage billing.

- Build CFO-friendly predictability: show consumption in real time, set limits, and define overage policies.

- Pilot outcome-based pricing where you can; it differentiates and aligns price with delivered value.

- Offer committed capacity and pooled credits; use auto-upgrades and burst protection to balance risk.

- Standardize value quantification and close the loop with CS to track baselines, outcomes, and ROI.

- Use AI to unify pricing, sales, and CS data so packaging decisions reflect real customer behavior and value.

Summary

If AI pilots are popping up across your org but nothing sticks, you may be building on cracked foundations, fueling “agent sprawl,” and trying to automate the past.

Robert Macdonald, co-founder of Lumavac and former leader at Google, Pinterest, and Fox (plus a stint as Chief Product Officer in EdTech), shares how to make agentic AI real—and scalable. Drawing on 25 years in partnerships and product, Robert breaks down a practical path: start with one use case, fix technical debt, immerse cross-functional teams, and align on a shared playbook.

He unpacks Lumavac’s hybrid approach—Velocity—a combination of tool and hands-on guidance that centers on spec-driven development, context engineering, and secure system agents. Robert also reveals his partnership blueprint, inspired by Stuart Diamond’s “Getting More”: build on authentic value exchanges, spot early red flags, and co-develop with product and engineering to move faster together.

Expect concrete advice for CISOs, execs, and GTM leaders on establishing ownership, reducing chaos, and turning curiosity into consistent outcomes.

Timestamps

[00:45] – Robert’s path: Google, Pinterest, Fox, EdTech CPO, and why he founded Lumavac

[03:22] – The partnership playbook: value exchange, transparency, and “Getting More”

[07:41] – Early red flags: guarded partners, low investment, and resetting with better questions

[13:24] – Top AI mistakes: cracked foundations, agent sprawl, and automating the past

[16:12] – Immersion over memos: getting execs and non-technical teams hands-on

[18:55] – Where to start: single use cases, ownership, and calming CISO concerns

[20:54] – Inside Velocity: spec-driven development, context engineering, and system agents

[27:39] – Differentiation and GTM: hybrid tool + services, co-building, and “start with one”

Takeaways

- Build partnerships on explicit value exchange and transparent goals; use “Getting More” to co-create outcomes you couldn’t reach alone.

- Heed early red flags—rigidity, reluctance to share, and low time/resource commitment—and pause instead of pushing.

- Fix technical debt before scaling AI; don’t build on cracked foundations.

- Curb agent sprawl with standards, clear ownership, and a shared playbook.

- Reimagine workflows, don’t just automate the past—pursue productivity and efficiency together.

- Accelerate adoption through immersion: get cross-functional teams hands-on via spec-driven development, context engineering, and secure system agents; start with one use case, then scale.

Summary

Drowning in AI-generated sameness? Learn how to make your brand feel unmistakably human. Marketing leader Chris Banks—whose career spans hospitality, event tech, and B2B SaaS—shares practical ways to balance AI with authentic voices that convert. He explains why real people must front your content, how to source thought leaders across the org (not just execs), and the simple changes that make webinars, videos, and social posts feel personal. Chris details how to keep brand voice consistent when scaling with AI by giving it a strong “foundation” (tone, examples, and context), plus a roadmap to avoid research rabbit holes. He also lays out a playbook for AI adoption inside companies: appoint an internal advocate, show use cases with data, and apply the five whys to drive behavior change. Expect candid lessons on what AI still can’t do well (like believable ad voiceovers), smart workflow wins (briefs, edits, and organization), and how event-led motions—from live Q&A to playful mixology segments—shorten the buyer journey. Listeners will walk away with frameworks to humanize content, pick high-impact AI projects, and ship work that stands out.

Timestamps

[00:45] – Chris’s background and focus: humanizing B2B brands and driving lead gen

[01:22] – Countering AI noise: elevating internal thought leaders and putting faces to content

[04:56] – Brand voice at scale: give AI a foundation (tone, examples, context) and iterate

[06:44] – Avoiding the AI rabbit hole: roadmaps, credible sources, and “good enough” thresholds

[08:39] – High-impact AI adoption: nominate an internal advocate and apply the five whys

[11:17] – Workflow wins: faster briefs, AI-assisted video edits, and organizing for speed

[12:40] – What not to automate: sourcing pitfalls and why AI video ads still feel off

[14:52] – Standing out: diverse formats, live Q&A, and event tactics that accelerate pipeline

Takeaways

- Put real people front and center—identify thought leaders across roles and pair ideas with faces.

- Use AI as a support layer: feed it brand foundations (tone, examples, context) and then edit for nuance.

- Prevent time-sinks with a clear roadmap—define “good enough,” cite sources, and resist endless tweaks.

- Appoint an internal AI advocate to drive adoption; show use cases, data, and apply the five whys.

- Diversify formats (how-tos, live demos, explainers) and make webinars interactive with Q&A and gamified elements.

- Pilot AI where minutes compound (briefs, edits, organization) and skip what hurts authenticity (generic voices, lookalike scripts).

Summary

If digital marketing is starting to feel the same everywhere, how do you actually stand out?

Anders Ohl, Chief Marketing Officer at ClickPoint Software (a lead management and sales automation platform), shares how he’s reframed SEO and go-to-market around cross-platform authority, great writing, and product-led growth.

With a background in film and fine art, Anders brings a brand-first, psychology-informed lens to AI and marketing. He explains why the era of keyword-stuffed content is over, how EEAT and semantic depth now win, and when to use AI as an agent versus a chatbot to truly simplify user workflows.

Anders details how shifting to targeted SEO and PLG produced near-100% qualified inbound, how he built an AI authority moat that outranked industry giants for “position zero,” and why compliance-forward marketing creates higher-value leads as regulations evolve.

He also breaks down the looming “post-software” challenge (vibe-coded agents), the comeback of direct mail in a digital stack, and how customer success—not sales—became ClickPoint’s growth engine.

Timestamps

[00:45] – Anders’ path from film and fine art to CMO at ClickPoint

[01:14] – AI’s impact on marketing: from “SEO tactics” to cross-platform visibility and brand authority

[03:13] – The end of bad SEO writing: EEAT, semantic depth, and real thought leadership

[04:00] – Using AI as a cognitive sparring partner; pushing back to get better outcomes

[05:42] – Agents vs. chatbots: designing AI to simplify, not add friction

[07:32] – Strategy in action: moving to targeted SEO + product-led growth for qualified demand

[08:52] – Building an AI authority map: winning position zero and outranking bigger budgets

[11:20] – The “post-software” threat: vibe coding, AI agents, and productizing institutional knowledge

[13:43] – Beating sameness: brand fundamentals, hybrid analog/digital channels, and modern direct mail

[16:06] – Regulation shifts: why compliance-first marketing yields higher-converting leads

[16:50] – Replacing sales with customer success: free tier, support, and upgrade-by-outcome

[18:11] – Art, psychology, and business empathy as brand differentiators

[19:51] – Parting advice: stay alert—chaos = opportunity

Takeaways

- Write for authority, not algorithms—prioritize EEAT, semantic depth, and real thought leadership.

- Build cross-platform visibility: develop an “authority map” that lifts all related topics and pages.

- Treat AI as a collaborator: challenge outputs, bounce tools against each other, and demand specificity.

- Choose the right AI UX: use agents for one-click tasks and chat only where conversation adds value.

- Shift to product-led growth with customer success at the core; let outcomes drive upgrades.

- Differentiate your brand beyond best practices—blend digital with smart direct mail and stay compliant to earn higher-value leads.

Summary

What happens when you bring a modern go-to-market playbook into a mission-driven nonprofit?

Shelly-Ann Wilson Anderson, Senior Vice President of Marketing & Communications at Boys & Girls Clubs of Metro Atlanta—and former CMO of Jamaica’s Exim Bank and PR/CSR leader at AT&T—shares how she’s building a predictable donor engine in a resource-constrained environment.

She breaks down a segmentation-led strategy across parents, youth influencers, donors, and alumni; the shift from one-off gifts to recurring “impact subscriptions”; and how automation can keep prospects warm year-round.

Shelly-Ann explains why in-person events “collapse the funnel,” detailing the narrative design behind BGCMA’s Youth of the Year Gala and how anchoring the story to Shaquille O’Neal unlocked national media and real-time social momentum.

She also offers a clear measurement model that pairs impact metrics with revenue KPIs, creative ways to stretch budgets with pro bono and matched media, and practical ways AI can multiply small teams without losing an authentic human voice.

Timestamps

[00:45] – Guest intro: From Jamaica’s financial sector to AT&T to leading marketing at BGCMA

[03:18] – Building a predictable donor engine: moving from relationships to a systematized GTM

[04:30] – Segmentation, digital campaigns, and converting to recurring “impact subscriptions”

[06:25] – Doing more with less: leveraging pro bono and matched media (e.g., metro digital billboards)

[07:51] – Measuring what matters: impact + revenue; KPIs for RD and program ops

[10:43] – In-person events as conversion accelerators: designing narratives that “collapse the funnel”

[13:18] – Case study: Youth of the Year Gala—Shaq’s authentic tie-in, national media, and real-time social

[18:39] – Storytelling and AI: multiplying content output while keeping the voice human

Takeaways

- Systematize donor acquisition with audience segmentation, multi-campaign digital programs, and recurring “impact subscriptions.”

- Design in-person events to convert: align theme, experience, and emotional storytelling with clear, on-the-spot CTAs.

- Pair impact and revenue: track ADA, teen engagement, new/recurring donors, event conversion, and post-event retention.

- Stretch limited budgets by tapping nonprofit advantages—pro bono placements, matched media, and partner networks.

- Use AI as a content multiplier to draft, refine, repurpose, and automate journeys—without sacrificing authenticity.

- Turn events into awareness flywheels: anchor stories to credible figures, publish in real time, and tie coverage to program outcomes.

Summary

AI is rewriting SaaS economics—and your pricing can no longer assume near-zero marginal costs.

Roee Hartuv, Senior Pricing Advisor and Head of GTM Expertise at willingness2pay.com, has helped 150+ SaaS and AI companies redesign pricing and packaging to unlock fast revenue growth.

He explains why pricing and packaging are the #1 growth levers, how AI’s token/API costs push teams from seat-based subscriptions to consumption models, and what to measure so revenue stays predictable.

Roee breaks down how to sell usage to enterprise CFOs, why hybrid models (base fee + metered usage) and caps restore confidence, and how real-time usage visibility prevents bill shock. He also shows how to differentiate with jobs-to-be-done packaging (not just good-better-best), creating cleaner initial sales and natural expansion paths.

Expect practical guidance, from the Salesforce “seat vs. usage” lesson to a simple 90-day pricing test and a reframing of GTM as one unified system.

Timestamps

[00:45] – Guest intro and why pricing/packaging is the top growth lever

[02:58] – AI’s impact on SaaS: from seat-based to usage and new unit economics

[05:36] – Metrics that matter in usage pricing and what Salesforce’s pivots reveal

[07:15] – Building buyer trust: real-time usage visibility and safe pricing “tests”

[08:48] – Fast revenue wins and migrating existing customers to new models

[10:20] – The enterprise CFO challenge: predictability in consumption pricing

[11:50] – Solutions: hybrid base + usage, caps, and usage forecasting

[14:53] – Differentiate with jobs-to-be-done packaging and clear expansion paths

Takeaways

- Instrument product to track granular usage events—not just logins—so you can meter fairly and bill confidently.

- Give buyers real-time usage/credit dashboards to prevent surprises and build trust.

- Offer hybrid pricing: a predictable base/platform or seat fee plus metered usage; add caps for enterprise predictability.

- Package by jobs-to-be-done instead of feature buckets to simplify buying and create natural expansion ladders.

- Run a 90-day pricing test: implement a 3–7% annual increase to keep pace with rising costs and validate price elasticity.

- Operate GTM as one system across marketing, sales, and customer success to reduce friction along the customer journey.

Summary

If your launches still run on “plan, ship, celebrate,” you’re leaving revenue on the table.

Ray Bahari, Global Product Marketing Lead at IBM and author of Systems, Not Slides: An AI Blueprint for Product Marketing, lays out how to move from launch theater to launch telemetry—compressing the distance between insight and execution.

Drawing on a career that spans scaling an early e‑commerce company, medtech at Stryker, mobile research at Polefish, insurance growth at Plymouth Rock, brand-building at JPMorgan’s Onyx, and ABM at IBM, Ray shares a practical playbook for adaptive positioning, sales enablement, and brand‑safe content at scale.

He explains how to build “minimum viable loops” across PMM—so positioning, proofs, messaging, and objections continuously inform each other—and how to choose a focused AI stack without drowning in tools.

Expect specifics on variant testing without losing brand consistency, creating a reusable bank of proofs, instrumenting journeys for speed-to-signal, and establishing a test‑and‑learn culture that actually sticks.

Timestamps

[00:45] – Guest intro and career arc: from early e‑commerce to SaaS, medtech, fintech, and IBM

[02:34] – Influencer dynamics in buying: lessons from Oticon and randomized rewards

[04:58] – Inside Stryker: digitizing the field with iPads and measurable customer interactions

[06:54] – Mobile-first research: Polefish and getting 2,000 survey responses overnight

[07:50] – Competing in insurance: partnerships, a homegrown DMP, and incrementality

[09:40] – IBM and JPMorgan: ABM impact, building Onyx, and returning to scale GTM with AI

[11:43] – From theater to telemetry: compression, adaptive positioning, and brand-safe variants

[17:16] – Tool choices, custom GPTs, and building Minimum Viable Loops and culture

Takeaways

- Build systems and loops—not one-off campaigns—to compress the time from insight to execution.

- Use AI to synthesize calls, CRM notes, and web behavior into objections, proofs, and enablement updates.

- Treat positioning as adaptive: test variants by segment and learn from “second-best” messages.

- Maintain brand consistency by training models on guidelines and anchoring content in a reusable bank of proofs.

- Curate a pragmatic AI stack: pick tools by task, leverage custom GPTs/LLMs, and document what works.

- Make test-and-learn cultural: start small, secure executive sponsorship, and run weekly operating rhythms.

Summary

AI is flooding go-to-market with content and tools—but where does it actually move revenue and build trust?

Mariam Slimane, Fractional Chief Marketing Officer at CX Fractional Advisors with 15 years in B2B SaaS (DevOps and cybersecurity), shares a pragmatic playbook for applying AI where it counts.

She explains how AI is transforming enablement, training, market research, and call analysis—surfacing next-best actions so sales teams respond faster and smarter. Mariam is bullish on data hygiene, faster experimentation, and in-house AI co-pilots tailored to your workflows, and she’s candid about the risks of hyper-personalization and “AI theater.”

She breaks down a favorite revenue lever—AI-assisted value-based pricing and bundling—and why getting pricing right early builds credibility and reduces end-of-cycle discounting. Expect clear guidance on avoiding channel sprawl, doubling down on ICP clarity, investing in marketing ops alongside sales ops, and the specific AI skills leaders should learn now to unlock custom solutions and better data infrastructure.

Timestamps

[00:20] – Mariam’s background: B2B SaaS, DevOps/cybersecurity, and a scale-up mindset

[01:03] – Where AI truly helps GTM: enablement, call analysis, research, and next-best-action

[02:42] – Data hygiene and rapid experimentation; building org-specific AI co-pilots

[04:50] – Cautions: hyper-personalization limits, “AI theater,” and shiny-tool skepticism

[05:55] – Cutting through AI noise: authenticity, in-person touchpoints, and value over volume

[09:05] – Revenue tactic: AI-driven value-based pricing and bundling to stop leaving money on the table

[11:30] – Focus beats channel sprawl; know your ICP (e.g., why paid ads miss in public sector)

[13:53] – Ops and org design: invest in MOPs/SOPs, protect data, design from workflows and forecasts

Takeaways

- Clean your data to make AI reliable—bad inputs now create bigger, costlier mistakes.

- Use AI to power enablement and call analysis, surfacing tailored assets and responses for sellers.

- Build or customize AI co-pilots in-house to fit your workflows, products, and buyers.

- Lead with authenticity: let automation remove busywork, not human connection.

- Implement value-based pricing and bundling with AI-assisted analysis; align pricing with messaging to build trust early.

- Focus your channels around a precise ICP and strengthen the marketing–sales ops partnership to scale responsibly.

Summary

In a world flooded with AI-generated content, how do B2B startups earn real trust and stand out?

Kristin Elke, Managing Director at Brightrose and a 20+ year B2B tech marketer, breaks down how to balance AI efficiency with credibility. .

She shares a practical framework for building market trust through ecosystem advocacy—customers, partners, and analysts—paired with quantified proof (think time-to-value and measurable outcomes).

Kristin explains why modular, focus-driven GTM teams outperform one-size-fits-all hires, how to prune tool sprawl, and where AI can truly help—especially in customer expansion.

Expect specifics: turning happy customers into multi-format stories, focusing on segmentation and differentiated messaging, and using automated CS dashboards in place of over-engineered QBRs. Kristin also previews the evolution of her book, Trust, and why the Edelman Trust Barometer signals urgency for B2B leaders.

Timestamps

[00:59] – Kristin’s background and Brightrose’s “transform to perform” focus

[02:00] – AI’s real impact: first-draft speed, personalization, and the trust deficit (plus the productivity learning curve)

[05:57] – Startup credibility playbook: ecosystem advocacy, customer stories, and quantified proof/time-to-value

[09:48] – Focused GTM: segmentation, differentiated messaging, modular resourcing, and pruning tool sprawl

[12:37] – Beyond acquisition: AI tools for expansion and PLG-style upsell in enterprise accounts

[14:46] – Customer success pitfalls: over-rotating to new logos; automate health dashboards vs. constant QBRs

[17:37] – The evolving “trust” thesis and what’s coming in Kristin’s next book

[19:50] – Where to connect with Kristin and her parting advice: stay curious with AI—be diligent and specific

Takeaways

- Lead with proof: capture customer stories that include quantified outcomes (e.g., pipeline uplift, time-to-value) and third-party validation.

- Build ecosystem trust: mobilize customers, partners, and analysts—don’t rely on a single brand voice.

- Use AI to accelerate first drafts and personalization, then add strategy and POV to maintain credibility.

- Focus your GTM: segment ruthlessly, sharpen differentiated messaging, cut unused tools, and assemble modular talent to fill gaps.

- Prioritize expansion and retention: design scalable CS with automated dashboards and feedback loops; balance new-logo pursuit with upsell.

- Explore AI where it moves expansion metrics: integrate signals to spot upsell paths and enable efficient PLG motions in large accounts.

Summary

Most partners don’t want to log into your portal—and that’s exactly where AI can change the game.

Vaughn Mordecai, Chief Revenue Officer at MindMatrix, explains how his team is turning partner enablement into an “AI operating system,” embedding intelligence so tasks like co-branding collateral or assembling sales rooms happen in the flow of work.

With 12+ years in channel, Vaughn gets specific on moving from bolted-on AI to agentive experiences, where an agent handles the busywork and surfaces what partners actually need—without killing human trust.

He shares the adoption traps that sink over half of AI programs (fear, irrelevance, “sideshow” pilots), and the blueprint to make AI stick: solve a real problem, keep humans at the center, and train teams to decide synchronously and fail fast. Expect practical examples, from MindMatrix’s “Skills in Seconds” microlearning to using NotebookLM and Gamma to speed proposals and decks, plus how sales-led data rooms personalize at scale.

Vaughn closes with a mandate for every GTM leader: make partnerships the fourth pillar alongside sales, marketing, and BD—because done right, they can drive 25–40% incremental revenue.

Timestamps

[00:45] – Vaughn’s path to CRO and MindMatrix’s partner management mission

[02:27] – AI as an operating system: embedding intelligence across GTM

[03:48] – From portals to agentive AI: enablement in the partner’s flow of work

[05:50] – Keep AI relevant: avoiding generic outreach and “AI vomit”

[08:24] – Cutting through the noise with human relationships and trust

[10:07] – Why AI fails inside orgs: fear, fit, and “OS vs. sideshow”

[12:47] – Synchronous decision-making: train to succeed or fail fast

[15:58] – Tools that work: Skills-in-Seconds, NotebookLM, Gamma, and sales data rooms

[22:00] – Parting advice: partnerships as the 4th GTM pillar driving 25–40% lift

Takeaways

- Embed AI as an operating system across partner workflows to eliminate portal friction.

- Deploy agentive AI to co-brand assets and surface next-best collateral on demand.

- Prioritize relevance: judge AI by how it helps partners and AMs, not by outputs alone.

- Keep humans in the loop—trust and relationships are what cut through market noise.

- Drive adoption by solving a real business problem, integrating AI into daily work, and training on data/process.

- Enable synchronous decisions: test quickly, learn, and scale or move on fast with microlearning and instant sales rooms.

Summary

AI is transforming go-to-market, but without the right guardrails and operators, it creates more noise than revenue.

Paul Quintal, Chief Revenue Officer at Iris, a global identity and cyber protection provider, shares how he builds a compliant, AI-enabled GTM engine while keeping the human edge.

With 20+ years leading revenue at companies from startup to IPO/acquisition, Paul explains where AI delivers real value today—signal gathering, knowledge-base assisted support, and hyper-personalized outreach—and where interoperability still lags. He walks through Iris’s vertical market strategy (TAM/SAM/SOM), the compliance hurdles of tools like ZoomInfo across US and GDPR, and how to map the true buying committee beyond the CISO to Product and Finance.

Paul also details his operating frameworks—DEA (Discipline, Execution, Accountability) and DOORS (Deliver on time, on budget, with referencable clients)—plus practical tactics for standing out in an AI-saturated inbox: permission-based, six-sentence emails, and yes, picking up the phone.

Expect candid, operator-level advice on turning AI signals into pipeline without abandoning the fundamentals.

Timestamps

[00:21] – Paul’s background: from dot-com sales to CRO/operator across IPOs and acquisitions

[02:00] – AI in GTM: real use cases, limits, and why guardrails/interoperability matter

[07:20] – When breaches hit: AI-assisted knowledge bases to stabilize call centers and NPS

[08:50] – Personalization at scale: custom outreach that clears spam and earns replies

[11:46] – Building a compliant GTM engine: ZoomInfo, GDPR hurdles, and vertical TAM/SAM/SOM

[15:23] – The DEA playbook: discipline, execution, accountability—and why the phone still wins

[16:42] – Find the right “pew”: mapping decision makers across CISO, Product, and Finance

[22:38] – DOORS framework: deliver on time, on budget, with referencable clients

Takeaways

- Establish AI guardrails and integrations across CRM and support tools to turn signals into workflows.

- Build vertical hunt lists with TAM/SAM/SOM; use AI for sequencing, but drive outcomes with live calling.

- Personalize outreach: ask permission, keep emails to six sentences, and avoid generic LinkedIn requests.

- Multi-thread deals: include CISO/CIO, Product (API integration), and Finance (revenue impact) in the buying group.

- Prepare for breach-driven volume spikes with AI-assisted knowledge bases to protect NPS and response times.

- Operationalize with frameworks: enforce DEA and DOORS to align activity, quality, and credibility at scale.

Summary

AI is rewriting go-to-market faster than most leaders can absorb—so where do you start?

Shawn D. Green, a veteran operator turned GTM advisor to 12+ companies across fintech, supply chain, and AI, lays out a practical path. With 25 years in tech (NetSuite—acquired by Oracle, BlackLine pre-IPO) and a recent Series A build-and-sale, Shawn blends deep operator chops with hands-on AI work, including an MIT program and building seven bots himself. He explains how to pilot AI with discipline—pick 1–3 high-impact use cases, assign an owner and timeline, and scale what works—while moving from dashboards to true AI orchestration.

Shawn breaks down the market (horizontal vs. vertical vs. enterprise AI), why intent-led, 24/7 engines are changing outbound, and how agentic AI pushes work to 99% so humans decide.

He also maps the org changes ahead, from repurposing headcount to RevOps evolving into GTM engineering, plus his daily upskilling routine, the “three rocks” focus, and how to use design partners to find real value. He closes with a simple mandate: don’t freeze—stay curious and build.

Timestamps

[00:45] – Guest intro and career highlights: NetSuite/Oracle, BlackLine, advisor across 12+ companies

[03:02] – AI’s impact on GTM and how to pilot: 1–3 use cases, ownership, timelines

[04:58] – From reporting to AI orchestration; why leaders must lean in now

[07:39] – Horizontal vs. vertical vs. enterprise AI: likely winners and exit paths

[10:08] – Outbound reimagined: 24/7 intent engines and hotter, more qualified leads

[12:04] – Agentic AI, explained: bots to 99%, humans make the call

[13:46] – Upskilling fast: daily practice and building bots as a non-technical leader

[19:05] – RevOps becomes GTM engineering: org design and headcount shifts

Takeaways

- Orchestrate AI with focus: select 1–3 high-value use cases, assign an owner, set a timeline, then scale.

- Narrow your bet: vertical or enterprise AI positions are more durable than “me-too” horizontals.

- Automate top-of-funnel: use AI to surface high-intent buyers so reps spend time partnering, not prospecting.

- Redesign roles: repurpose headcount for higher-value work; evolve RevOps into GTM engineering to coordinate tools, data, and agents.

- Build an AI habit: invest 30–45 minutes daily; prototype bots even if you’re non-technical.

- Use design partners to validate value, iterate quickly, and prove ROI before wide rollout.

David Boyle—co-founder and Managing Partner at RevCentric Partners, two-time CRO with exits, and a seven-time world #1 rep—shares what actually moves revenue.

A veteran of PTC, BMC, and Zscaler, David helped author and deploy MEDDIC at PTC and now advises early- to mid-stage companies on proven sales execution. He breaks down five practical AI use cases that boost seller productivity in research, outreach, and meeting prep—plus where AI agents are showing lift (first meetings) and where they aren’t yet (revenue).

David also outlines the three recurring growth blockers he sees across companies: hiring rigor, creation of qualified opportunities, and middle-to-lower funnel control. You’ll hear concrete hiring benchmarks (<20 business days, <5 interviewers, ~25% first-interview-to-offer) and a simple outbound math model that can 4x qualified pipeline.

He illustrates why complex enterprise cycles adopt AI more slowly with a 213-meeting Goldman Sachs deal—and closes with how RevCentric’s “teach in the trenches” approach drives real adoption.

Timestamps

[00:45] – Guest intro: career highlights, RevCentric, and MEDDIC origins at PTC

[02:30] – Cutting through AI hype: five sales use cases and real productivity gains

[04:46] – How AI reshapes GTM: orchestration, headcount questions, and changing seller skills

[07:22] – AI agents today: booking meetings vs. moving revenue

[08:25] – Adoption varies by complexity: enterprise vs. high-velocity sales; the 213-meeting deal

[12:47] – Three growth blockers: hiring, creating qualified opps, and lower-funnel control

[15:56] – Hiring discipline for SMBs: speed, simplicity, and clear accountability

[18:49] – Outbound funnel math: doubling inputs and conversions to 4x qualified pipeline

Takeaways

- Ground your AI strategy in defined sales use cases—research, outreach, and meeting prep that keep reps on-message.

- Train sellers to orchestrate systems while doubling down on fundamentals: champions, economic buyer access, and forecast control.

- Set hiring guardrails: under 20 business days, fewer than five interviewers, and ~25% first-interview-to-offer without lowering the bar.

- Assign clear hiring accountability to the hiring manager; avoid “committee” decisions that dilute ownership.

- Measure and coach outbound rigor: increase weekly seller-set meetings and raise first-meeting-to-qualified conversion.

- Do the funnel math—small gains compound; doubling meetings and conversion can 4x qualified opportunities.

Summary

Want AI to save time and money — not create more complexity?

Steve Gonzalez, Senior Technical Program Manager at Washington Digital Media and fractional CMO, lays out practical ways small businesses can adopt AI without getting trapped in endless SaaS subscriptions.

Steve blends hands-on tech (hosting on a NAS, Cloudflare protection, Google Calendar integrations) with creative marketing: AI-generated UGC, hybrid real/AI content, and case studies like Dudley Beauty School.

He also covers the near-future of VR/Web3, data/licensing issues around faces and content, and concrete prompting and MVP workflows that deliver scalable ROI.

Listen for step-by-step tactics to reduce costs, run better experiments, and get predictable results from AI.

Timestamps

[00:22] – Guest intro: Steve’s background and mission helping creatives adopt tech  

[01:47] – How AI is reshaping marketing and closing time/budget gaps  

[03:14] – Tool scouting: consistency, quality, and avoiding subscription bloat  

[05:24] – DIY digital architecture: booking, hosting on a NAS, Cloudflare example (whenisteveavailable.com)  

[06:56] – The next frontier: VR/Web3 commercial opportunities for small businesses  

[09:33] – Case study: Dudley Beauty School — mixing AIGC + real UGC across the funnel  

[12:36] – Rights, licensing, and the ethics of using faces and AI-generated likenesses  

[17:50] – Prompting & build strategy: ask first, breadcrumb features, and iterate with MVPs

Takeaways

- Audit and simplify your stack: replace recurring SaaS where a lightweight self-hosted or open-source setup suffices.  

- Start with a clear end goal: reverse-engineer outcomes before prompting an LLM.  

- Prompt strategically: answer who/what/when/where/why, speak your prompt, then refine incrementally.  

- Build incrementally: ship an MVP, then layer features and different AI tools to scale.  

- Isolate AI contexts: use separate AI accounts or instances per client to avoid data bleed and get tailored outputs.  

- Measure ROI early: run rapid experiments, track outcomes (time + money), and scale only the winners.

Summary

Your buyers aren’t Googling first anymore—they’re asking ChatGPT, Claude, and Grok. In this episode, Robert Kazmi, Chief Revenue Officer at Koombea, an AI-driven mobile and web app development partner, explains how go-to-market teams must evolve from search engine optimization (SEO) to generative engine optimization (GEO). Robert shares how AI is reshaping buyer expectations, compressing research cycles, and rewarding brands that add real value to LLMs’ source ecosystems. He details how his team rebuilds content pillars for GEO, uses AI to triage support vs. human engagement, and even treats LLMs as indirect lead sources by understanding the data they consume. Robert also breaks down developer–AI interaction—why hallucinations demand human audit, how prompt engineering becomes a core skill, and what “line of sight” leadership looks like to align metrics with outcomes. Expect practical insights on standing out amid AI noise, compressing trust-building to seconds, and preparing teams for an AI-native GTM.

Timestamps

[00:45] – Guest intro and Koombea's AI-native app focus  

[01:20] – AI reshaping buyer expectations; productivity, self-serve, and marketing triage  

[03:45] – From SEO to GEO: getting found inside ChatGPT, Claude, and Grok  

[05:50] – Rebuilding content pillars for GEO and driving high-intent leads  

[08:40] – Turning LLMs into indirect lead sources: data-source strategy  

[12:20] – Developer–AI interaction: hallucinations, context, and prompt engineering  

[17:20] – Can AI agents sell? Filters, noise, and compressing trust to a five-second search  

[32:05] – Operating with “line of sight”: metrics that matter, alignment, and execution

Takeaways

- Reorient from SEO to GEO: design content for LLM retrieval—specific, expert, and genuinely useful.  

- Seed value where LLMs look: publish proof-rich assets in high-weight sources (e.g., forums, expert hubs, analyst content).  

- Use AI to triage and scale: route simple requests to self-serve and reserve humans for complexity; let AI accelerate content pillars and campaigns.  

- Build human guardrails: upskill teams in prompt engineering and implement audit workflows to catch hallucinations.  

- Break through AI noise: lead with creativity, clear outcomes, and social proof to earn trust fast.  

- Lead with “line of sight”: align teams on objectives and measurable business metrics—not vanity numbers

Summary

AI lets marketers move faster than ever—but brand trust is on the line. Chris Howard, Executive Director and Head of Marketing at Lace Partners, shares how his team acts as brand custodians in an AI-first world, rehumanizing content so it reflects who they are—not a generic consultancy. Chris explains why marketers must shift from “content creators” to “editors,” the core skills he now hires for (agility and precise prompting), and how he cut content turnaround from days to hours. He details a practical analytics win using Excel and Microsoft Copilot to decode LinkedIn performance at scale—then feed those insights back into language, format, and channel strategy. You’ll hear how Lace is redesigning content for AEO (answer engine optimization), elevating short-form video quality, and selecting tools without getting trapped by vendor churn. Chris also reframes AI as a “teammate” embedded across workflows—especially in HR—while addressing creative opportunities (Synthesia, Descript) and risks around identity and security. He closes with candid advice: experiment more, kill what’s not working, and double down on short-form video.

Timestamps

[00:45] – Guest intro: Lace Partners, remit, and marketing’s scope

[03:14] – Brand custodianship and “rehumanizing” AI-generated content

[06:43] – Skills for modern marketers: agility and precision prompting

[09:04] – Copilot + Excel: turning LinkedIn data into content and language patterns

[11:05] – Campaign design: AEO vs. SEO, FAQs, and upgrading short-form video

[13:53] – Biggest GTM challenge: keeping up with tool velocity and fit

[15:28] – Practical stack: Copilot, HubSpot AI agents, Riverside, Descript, Synthesia

[17:40] – AI as a teammate: embedding agents across HR and marketing

Takeaways

- Rehumanize AI output—treat brand as a craft and keep humans as final editors.

- Train for agility and master prompting to cut cycle times without losing quality.

- Let analytics lead creativity—use AI to mine platform data for language and format cues.

- Optimize for AEO—structure content for question-based discovery and FAQs.

- Professionalize short-form video—raise production value while staying fast.

- Treat AI as a teammate—embed agents in workflows and set guardrails for identity and security.

Summary

If your GTM playbook still looks like 2019—heavy on headcount, light on automation—it’s time for a reset. Fractional CRO and revenue advisor Corey Kleinbauer shares what’s changed in the past three years and how leaders can restart growth by combining ICP discipline, modern discovery, and AI-enabled workflows. With three decades leading revenue across e-learning, SaaS, and med tech, Corey explains why chasing blue-chip logos can quietly break SMB/mid-market motions, how to replicate a founder’s “zeal” with engineered sales processes, and where orgs are over-indexed (think SDR stacks) vs. where automation can create leverage without losing personalization. He outlines the three pipeline questions every leader should insist on, the skills to hire for in a human+AI workforce, and why empathy plus a clear point of view wins deals and protects timelines. Expect practical guidance on moving from founder-led to scalable sales, avoiding the upmarket trap, and using AI to boost productivity across the GTM lifecycle.

Timestamps

[00:45] – Corey’s background and why traditional GTM motions stopped working

[02:09] – The rugged sales environment: COVID disruption, inflation, and AI’s impact on go-to-market

[05:16] – Human + AI teams and the generational shift in expectations and work styles

[08:23] – ICP discipline: knowing when to walk away and why “everyone” isn’t your customer

[10:40] – The upmarket trap: how enterprise logos can derail SMB/mid-market engines

[12:11] – Fixing discovery: the 3 questions every pipeline review must answer

[14:45] – Unifying the sales process and using AI to automate workflows (without losing personalization)

[16:35] – Scaling beyond founder-led sales: replacing “founder zeal” with process, roles, and comp

[20:23] – Hiring for a dynamic, AI-ready GTM team

[22:43] – Empathy, point of view, and suggesting next steps

Takeaways

- Anchor pipeline reviews on three essentials: the problem you’re solving, the level you’re selling to, and the timeline.

- Define and defend your ICP; avoid enterprise “logo lust” if it breaks a volume-and-velocity motion.

- Engineer a repeatable sales process that replicates founder zeal; align roles and compensation to your model.

- Audit SDR-heavy motions; use AI to automate workflows and increase personalization without adding headcount.

- Hire for dynamism, mission alignment, and tool adoption across generations—not just past logos.

- Lead with empathy and a clear point of view: suggest next steps and give customers a graceful out to keep momentum real

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.”

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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

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.

With a career spanning mortgage sales, operations leadership, and consulting for founder-led startups to enterprise teams, Jason shares a unique perspective on what truly drives scalable, human-centric sales growth. From overcoming founder blind spots to building systems that support not replace your people, Jason delivers practical advice with sharp clarity.

You’ll learn how to identify cracks in your pipeline, why most CRMs go unused, and how to avoid the biggest AI traps in sales today. If you’ve ever wondered how to balance technology with trust in your sales motion, this episode is for you.

Key Timestamps

• [00:31] – Jason’s unconventional journey from operations to sales leadership

• [02:38] – The scalability gap between sales-led vs. product-led founders

• [04:47] – Why repeatability and systems matter more than charisma

• [05:56] – Core tech tools that support (not replace) your salespeople

• [07:48] – The real problem with follow-up and pipeline neglect

• [08:41] – How Jason diagnoses sales orgs using a 24-category gap assessment

• [10:24] – His approach to tool selection and AI integration

• [14:15] – The AI trap: why scaling poorly is worse than not scaling at all

Takeaways

• Admit what’s not working — Change starts when leaders recognize their own blind spots.

• Build around your people — Technology should empower your team to have better conversations, not replace them.

• Automate wisely — Use automation for alerts, follow-up, and lead hygiene—but not for blasting spammy outreach.

• Stick with your tech stack — Optimize the tools you have before chasing shiny new platforms.

• Use AI for scale, not shortcuts — Leverage AI to analyze pipelines, write drafts, and source leads—but always finish with a human touch.

• Assess before you act — A gap analysis across core revenue functions helps prioritize changes that move the needle.

In today’s episode of The Growth Wizards Podcast, we dive deep into modern sales strategy with Shawn Karol Sandy, Chief Revenue Officer at Protect Services and founder of The Selling Agency. With decades of experience across print, TV, and consulting, Shawn brings a refreshing perspective on what really moves the needle for sales and revenue teams—especially in mature, non-SaaS markets.

Shawn unpacks why most go-to-market strategies fail, how outdated paradigms are holding companies back, and what it takes to align your sales journey with your customer’s reality. From building voice-of-customer feedback loops to creating proprietary, scalable customer journeys through AI, this episode is packed with actionable insights for anyone leading revenue or sales functions.

If you’re trying to unify marketing, sales, and customer success while standing out in crowded markets, this episode is your playbook.

Key Timestamps

• [00:23] – Shawn’s career journey and love for sales

• [01:40] – Why so many businesses still operate on outdated sales paradigms

• [03:56] – The #1 challenge revenue teams and buyers both face today

• [05:13] – Using voice of the customer to inform sales strategies

• [09:22] – Why the real customer journey is anything but linear

• [11:30] – Building out detailed buyer personas and “day in the life” views

• [14:33] – How to make your customer journey feel proprietary and unique

• [15:49] – The rise of AI agents in go-to-market strategy

• [20:09] – Why being more human is the best competitive advantage

Takeaways

• Adopt a true growth mindset to stay relevant in fast-changing sales environments

• Build your sales strategy around empathy, not just urgency

• Create detailed buyer personas to tailor messaging and outreach

• Leverage voice-of-customer insights to uncover real pain points

• Use AI and automation to make your customer journey consistent and scalable

• Differentiate through human connection—make your process feel unique and personal

David shares how he builds foundational processes before launching flashy campaigns, why clear MQL and SQL definitions are critical, and how he’s experimenting with AI to scale his team’s impact. He also reveals how to avoid the common pitfall of sales and marketing misalignment—and why the customer’s experience must remain central in every phase of the funnel.

Whether you're a startup founder, GTM leader, or revenue operator, this episode is packed with actionable ideas to streamline your growth machine.

Timestamps

• [00:29] – David’s career journey: from agencies to startups to Boostability

• [02:51] – The challenge of joining an established company with legacy systems

• [03:57] – How David builds GTM strategies by starting with internal alignment

• [06:45] – The “RevRex” report: reverse-engineering revenue targets from sales data

• [08:36] – Defining MQLs and SQLs to create clarity and accountability

• [10:47] – Bridging sales and marketing: lessons from toxic silos

• [14:38] – Favorite tools and tech stacks for revenue operations

• [17:28] – Where AI fits in: hype vs. reality in marketing automation

• [20:31] – Building a GPT from case studies for smarter sales enablement

• [21:42] – Why authenticity is your best marketing strategy in the AI era

Takeaways

• Start with foundational clarity – Align internal definitions, processes, and handoffs before launching campaigns.
• Reverse-engineer your revenue goals – Use data-driven models to map out lead, demo, and conversion requirements.
• Bridge the marketing-sales divide – Joint accountability and shared metrics are key to GTM success.
• Focus on the customer experience – Ensure every touchpoint feels consistent and valuable.
• Experiment with AI cautiously – Understand its limits and test for real-world effectiveness before scaling.
• Leverage your own data – Turning case studies into structured knowledge can transform sales conversations.