AI for Revenue Leaders: The AI Hat Podcast

Your prospects stopped Googling. They're asking ChatGPT which CRM to buy, asking Perplexity which marketing automation platform solves their problem, asking Gemini to compare your category - and AI is recommending your competitors, not you. This isn't a "future of search" problem. This is a pipeline problem. Right now.

Kevin White, Head of Marketing at Scrunch (and formerly leading growth at Twilio Segment, Retool, and Common Room), saw this crisis coming and built the measurement infrastructure to fix it. His clients are seeing 100% to 260% visibility gains in AI search in just 60 days - and tracking customers who convert specifically because they appeared in ChatGPT answers.

In this episode, Kevin deconstructs the "Citation Economy" - why chasing Reddit mentions is a trap, which citation sources actually drive AI recommendations in your vertical, and the exact workflow his team uses to go from "invisible in AI" to "recommended by LLMs" in under two months.

You'll learn the diagnostic every Revenue Leader should run Monday morning to find out if your brand exists when prospects ask AI for solutions. You'll discover why the "long, long tail" of AI search (20+ word prompts vs. 5-7 word Google queries) creates greenfield opportunities for brands willing to architect intent-driven content. And you'll hear how Scrunch's Agent Experience Platform delivers AI-optimized content to LLM crawlers without breaking the human website experience.

This is not theory. Kevin walks through Scrunch's own dogfooding - how they use their product to track competitive intelligence, diagnose content gaps, and systematically close citation opportunities. He shares the specific technical optimizations that actually move the needle (spoiler: it's not just adding FAQs). And he reveals how agentic workflows - using Claude Code to build marketing apps, automate spreadsheet enrichment, and analyze customer calls - are reclaiming hours his team used to lose to manual synthesis.

If your marketing team thinks they're winning because they rank on Page 1 of Google while your pipeline bleeds invisible in ChatGPT, this episode is your wake-up call. Kevin delivers the audit, the workflow, and the hard truth: you can't optimize what you don't measure.

Run the diagnostic. Close the gaps. Win the prompts that matter.

Show Notes & Transcript: https://theaihat.com/your-prospects-arent-googling-anymore-scrunchs-kevin-white-on-the-ai-visibility-crisis/

CHAPTERS

00:00 LLM SEO Lore

00:36 Podcast Intro Theme

01:45 AI Hat Welcome

02:10 Why Google Rankings Fail

02:53 Kevin White Joins

04:14 Wake Up Call Moment

07:15 Measuring AI Visibility

10:34 Citation Myths Debunked

13:57 Long Long Tail Search

16:45 Three Citation Buckets

19:07 Workflow To 260% Gains

24:15 Shadow AI Ad Break

25:25 Making Sites AI Readable

29:57 Dogfooding Scrunch Tactics

32:19 Top Priorities This Quarter

35:33 One Prompt Audit Trick

37:16 Where To Connect

38:40 Final Wrap Up

Kevin White on LinkedIn: https://www.linkedin.com/in/kevbosaurus/Scrunch: https://scrunch.com/
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If you're a VP of Sales watching your revenue team paste customer data into ChatGPT, you don't have an adoption problem - you have a governance crisis. Your best people are uploading signed NDAs to Claude and feeding pipeline data into Perplexity because 70% of their day is admin drag, and AI is the only thing fast enough to keep them above water.

Financial services tried to ban AI. It failed spectacularly. So they built governance frameworks that let teams move faster and sleep at night.

Dr. Angela Murphy - known as Payments Elsa - reveals the "Amnesty and Orchestration" playbook she architected for banks navigating the GENESIS Executive Order. She's a PhD strategist, 2024 PayTech Women Emerging Trendsetter, and advisor to financial institutions on AI governance and ethical AI mandates.

You'll learn the three-step governance audit every Revenue Leader should run this quarter - before Legal does. Angela shares real stories of teams using ChatGPT for payment disputes and compliance workflows, creating massive liability. She reveals the conversation framework to surface Shadow AI without triggering panic, the three policies you can implement in 30 days, and why explainability isn't compliance theater - it's revenue protection.

This isn't a "fire your team and replace them with bots" episode. Angela proves ethical AI can surface hidden revenue channels, identify products to sunset, and reveal sales cycle biases costing you deals.

The regulatory hammer is coming. Financial services just got hit first. Will you architect governance now, or audit the damage later?

Download the Executive Guide to Shadow AI at theaihat.com/shadow-ai.

Subscribe to AI for Revenue Leaders: The AI Hat Podcast and stop being a Pilot Purgatory statistic.

CHAPTERS

00:00 Ethical AI = Revenue Growth: Find Gaps, Biases & New Channels

01:24 Show Intro & Theme Song: Welcome to The AI Hat Podcast

02:56 The Shadow AI Compliance Time Bomb (Real-World Examples)

03:43 Meet Dr. Angela Murphy (Payments Elsa) + Why Banks Try to Ban AI

07:41 Shadow AI in the Back Office: Spreadsheets, PII, and Manual Ops Risks

11:04 Why Revenue Leaders Should Watch FinTech: Payments Rails & Stablecoins

13:04 Genesis Executive Order Explained: “Suggestulation” and What’s Coming

16:24 From Fear to Frameworks: Finding Low-Hanging AI Wins with Guardrails

19:24 Resource Break: Executive Guide to Shadow AI

20:33 Orchestration 101: Tool Inventory, Training, and Policy from Existing Governance

23:33 Explainable AI: Decisions You Can Defend (Underwriting Example)

27:51 Ethics, Bias & Revenue Outcomes: Avoid Lawsuits and Unlock Better Decisions

31:19 Biggest Misconceptions: You Can’t Ban AI—and Education Isn’t Optional

37:30 Monday Morning Action Plan: Start the AI Policy, Audit Tools, Target Pain Points

40:46 Where to Find Angela + Final Wrap and Next Steps

Show Notes & Full Transcript: https://theaihat.com/why-your-sales-teams-shadow-ai-is-a-lawsuit-waiting-to-happen-a-fintech-cros-governance-playbook/
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Your sales team spends 70% of their time on admin work - not selling. Do the math: if your top AE makes $120K and only spends 30% of their time on revenue activities, you're paying $84,000 for data entry and CRM updates. That's the Admin Drag tax killing your pipeline velocity in 2026.

But here's the real problem: you bought AI six months ago (Gemini, ChatGPT, Einstein), rolled it out with a 45-minute webinar, and… crickets. Adoption is at 14%. Your VP is asking what you're paying for.

This isn't a technology problem. It's a leadership problem. You installed software when you needed to redesign the workflow.

In this episode, Mike Allton (Director of Partner-Led Growth at Agorapulse) deconstructs the 95% AI pilot failure rate and shows you exactly what Revenue Architecture looks like in practice. You'll discover:

• The real cost of manual meeting prep (90 minutes vs. 8 minutes with Gemini's Scheduler)• How to build a "Digital Crew" that automates research, briefing docs, and objection prep• Why Gemini's 2 million token context window changes everything for sales intelligence• The 3-day audit that reveals where your team is hemorrhaging time• How to move from "Human-in-the-Loop" to "Human-on-the-Loop" automation

Stop installing tools your team won't use. Start architecting workflows that reclaim 25-30 hours per rep, per week.

Resources: TheAIHat.com/speaking

CHAPTERS

00:00:00 - Introduction: The $600K Call She's Unprepared For

00:02:20 - The $84,000 Admin Drag Tax (Do the Math)

00:03:41 - Why 95% of AI Pilots Fail (It's Not the Technology)

00:04:05 - The Da Vinci Workshop: Building a Digital Crew

00:05:17 - Pilot Purgatory: The 6-Month Failure Timeline

00:07:06 - The Old Way: 90 Minutes of Manual Meeting Prep

00:08:48 - The Digital Crew Way: Gemini Scheduler in Action

00:11:48 - Why Gemini Is Structurally Different

00:12:16 - Native Workspace Integration (Friction Removal)

00:12:36 - The 2 Million Token Context Window

00:13:20 - Multimodal Native (Not Franken-Botted)

00:14:23 - Grounding with Google Search (The Accuracy Engine)

00:14:43 - The Scheduler: The Agentic Unlock

00:15:36 - Your Homework: The 3-Day Time Tracking Audit

00:17:01 - How to Architect Your Digital Crew (Workshop Info)

00:17:45 - Closing: Stop Installing Software, Start Architecting Systems

Show Notes & Transcript: https://theaihat.com/geminis-scheduler-just-fixed-sales-meeting-prep-and-killed-the-90-minute-research-grind/
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$1M in sales. $500K saved. Zero new headcount.

Your sales team isn't ignoring dead leads because they're lazy - they're drowning in admin drag. While you're debating whether to hire two more SDRs, your competitors have stopped asking "Who do I hire next?" and started asking "What system do I build next?"

Will Del Principe (Growth & Solutions Engineering Leader at Thoughtly) reveals how AI voice agents have crossed the quality threshold - handling objections, qualifying leads, and booking meetings at scale. Will personally drove close to $1M in sales in 6 months using voice agent workflows, and his clients are generating hundreds of thousands per month by reactivating dead pipeline.

You'll learn:

Why voice agents are fundamentally different from chatbots (and why that matters for stalled pipeline)

How to design re-engagement campaigns that resurrect cold leads without damaging your brand

Quality benchmarks for evaluating voice AI providers (70% of prospects don't detect it's AI)

The mindset shift from headcount scaling to system architecture

The low-risk pilot strategy for testing voice agents this quarter

Who this is for: VPs of Sales, CROs, and RevOps Directors in mid-market to enterprise B2B who need workflow architecture that produces measurable P&L impact - not pilot purgatory.

Download the free Executive Guide to Shadow AI at theaihat.com/shadow-ai

CHAPTERS:

00:00 - Introduction: The Efficiency Crisis Killing Your Pipeline02:08 - Your Sales Team Isn't Lazy - They're Drowning in Admin Drag03:27 - Meet Will Del Principe: $1M in Sales Using AI Voice Agents03:58 - The Coffee Invitation: When AI Crossed the Uncanny Valley06:00 - What Is a Voice Agent? (And Why It's Not Just Another Chatbot)08:05 - The Podcast Guest Analogy: How AI Removes Bottlenecks Without Replacing People11:14 - Mixing Outbound and Inbound: The New Campaign Architecture13:20 - Why You Can't Cold Call with AI (And How to Get Consent)15:07 - The Handoff: When Does AI Transfer to Human Sales Reps?16:22 - Reactivating Dead Leads: 650 Meetings Booked in 90 Days18:04 - Mid-Roll: The Executive Guide to Shadow AI20:23 - Quality Benchmarks: What Makes a Voice Agent Sound Human?24:22 - The Thoughtly Roadmap: Omnichannel AI Personas Are Coming26:49 - From "Who Do I Hire?" to "What System Do I Build?"28:10 - How to Pilot a Voice Agent: The Low-Risk, High-ROI Use Case29:47 - The CRM Graveyard Audit: What Would 10% Reactivation Mean for Your Revenue?31:02 - Where to Learn More About Thoughtly31:46 - Outro: Download the Shadow AI Guide

CONNECT:Will Del Principe - Thoughtly: thoughtly.co | thoughtly.co/demoMike Allton - The AI Hat: theaihat.com | theaihat.com/shadow-ai

AI for Revenue Leaders is your operational playbook for the Agentic Era. Host Mike Allton deconstructs how practitioners are deploying sanctioned AI to hit quota and reclaim sales capacity lost to admin drag. Stop guessing. Start architecting.
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If your revenue team takes three weeks to deliver a diagnostic report - and still misses the $21 million opportunity hiding in your pipeline - this episode reveals why manual analysis is costing you deals and how a RevOps CEO architected an AI agent that does it in 24 hours.

Tara Kinney, Founder and CEO of Atomic Revenue, has led 18 CRM implementations across 76 companies. She knows what "normal" looks like in revenue operations - and she blew it up. Her agency used to spend weeks qualifying prospects and delivering diagnostics. Now her Digital Crew does it in 24-48 hours, cuts the sales cycle by 90%, and finds revenue opportunities her manual analysis missed.

In this episode, Tara walks through the exact workflow: how the Revenue Reality Check agent integrates with HubSpot and Slack, how it auto-assigns prospects to the right strategist based on industry and pain point, and why the "Human-in-the-Loop" governance layer is non-negotiable. She also reveals the $21 million story - the moment her AI agent identified a conversion bottleneck she hadn't even tracked, proving that agents don't just save time, they see what humans miss.

This isn't theory. This is a deployable blueprint for Revenue Leaders who need to stop drowning in Admin Drag and start architecting workflows that deliver measurable P&L impact.

You'll Learn:

Why manual revenue diagnostics are too slow (and too shallow) for 2026 B2B velocity

The 24-hour diagnostic workflow that cut Atomic Revenue's sales cycle by 90%

How AI found a $21M opportunity by modeling scenarios humans can't calculate at scale

The HubSpot + Slack + Multi-LLM integration stack behind the Revenue Reality Check

Why "Human-in-the-Loop" governance prevents hallucination risks and protects your brand

The three revenue killers hiding in your pipeline: Sales handoffs, legal bottlenecks, and client onboarding gaps

Guest: Tara Kinney, Founder & CEO, Atomic RevenueCredentials: 76 companies supported, 18 CRM implementations, RevOps practitioner with real P&L accountability

If you're a VP of Sales, CRO, or RevOps Director tired of waiting weeks for answers while your competitors move faster, this episode gives you the audit framework and workflow architecture to deploy your first diagnostic agent.

Resources:

Try the Revenue Reality Check: atomicrevenue.com

Download the Executive Guide to Shadow AI: theaihat.com/shadow-ai

Chapters:

00:00 Introduction: The Impact of AI on Sales Processes

00:51 Meet Mike Allton and The AI Hat Podcast

02:00 Welcome to AI for Revenue Leaders

02:21 The Problem with Traditional Revenue Diagnostics

03:00 Introducing Tara Kinney and Atomic Revenue

03:55 The Evolution of Revenue Diagnostics with AI

06:07 How AI Transforms Revenue Reality Checks

08:45 The Role of AI in Enhancing Sales Strategies

17:00 The Human-AI Collaboration in Revenue Diagnostics

33:32 Key Metrics and Questions for Revenue Leaders

36:22 Conclusion and Next Steps
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