GTM AI Podcast with Coach K and Jonathan Moss

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Tas Newsletter:

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The Experiment That Exposed Everything
When Tas Hirani, a veteran enablement leader with a Six Sigma background from GE, noticed her sales teams struggling despite having access to cutting-edge AI tools, she did something radical. She didn’t run another survey or schedule more training sessions. Instead, she went undercover as a sales rep while maintaining her enablement role.

What she discovered explains why companies are spending millions on AI tools that collect dust while reps continue drowning in admin work.

The Brutal Truth About Sales AI Adoption
“Everyone’s got LinkedIn, LinkedIn Navigator, ChatGPT, Perplexity… but when I actually sat in the seat and tried to use these tools the way reps do, it was Pandora’s box,” Hirani reveals. The problem isn’t the technology—it’s how we’re implementing it.

Here’s why 90% of sales AI tools fail:

The “Dead Weight” Problem: Traditional tech forced salespeople to adapt their workflow to the tool. As Hirani puts it, “Technology was like dead weight that people were hauling up the hill… trying to get to this sale, but I can’t get there because I have to go to 12 different places.”

The Generic Solution Trap: Companies throw in Microsoft Copilot or ChatGPT behind a firewall and declare themselves “AI-enabled.” Hirani calls this “a recipe for failure” because it ignores business-specific context.

The IT Power Play: When IT departments impose generic AI solutions because they have “those two magic letters,” adoption inevitably fails. The tools that work are chosen by the business teams who actually use them.

The Reality Check That Changed Everything
During her time in the sales trenches, Hirani discovered something shocking. When she shared AI tools that worked brilliantly for her, the reactions from her team were mixed:

“Some reps said, ‘I don’t have any confidence in AI. It doesn’t sound like me. My prospect is gonna know that it’s not me if I haven’t felt the pain and written that email myself.’”

This revelation led to a fundamental insight: Every rep is at a different point in their AI adoption journey, and one-size-fits-all solutions are doomed to fail.

Visual learners needed completely different tools than text-based processors

New reps loved real-time coaching popups; veterans found them distracting

Some thrived with vanilla ChatGPT; others needed specialized solutions

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https://www.linkedin.com/in/scott-martinis/

https://www.b2bcatalyst.com/

Breaking Down GTM Engineering with Scott Martinez: A Game-Changing Conversation

Holy smokes, folks. I just had one of those conversations that makes you want to completely rebuild your entire go-to-market motion. Scott Martinez from B2B Catalyst dropped some absolute truth bombs that I’m still processing.

Let me be straight with you – I’ve been in sales and enablement for years, and Scott’s approach to GTM engineering is unlike anything I’ve seen. This isn’t your typical “send more emails” or “hire more SDRs” playbook. This is surgical precision applied to revenue generation.

Scott shared a story that stopped me in my tracks. He generated 700 MQLs across three companies – 180 for one, 90 for another, and 399 for the third. Guess how much converted to revenue? Zero. Zilch. Nada.

Why? Because generating leads isn’t the same as generating revenue. And that’s where most of us get it wrong.

Here’s what blew my mind: While most RevOps teams are doing territory planning based on industry and company size, Scott’s data shows that proper account qualification criteria can result in 2-5x higher close rates.

Think about that. If you’re targeting accounts outside your true ICP, you’re operating at 50-80% reduced effectiveness. You could make 100 calls into qualified accounts and get 5x better results than the same effort into unqualified accounts.

• Interview your top 3 sales reps with a “Perfect Opportunity Worksheet”
• Ask them: “When you’re researching the best prospect ever, what do you expect to see?”
• Look for specific signals:

Scott’s approach is brilliant here. Instead of trying to automate everything at once, he asks: “What’s the one constraint that, if fixed, would unblock everything else?”

Real example: An SDR team spending 2 hours per day on account qualification. Instead of replacing them with AI, Scott’s team:

• Identified 13 discrete website signals
• Built a scoring rubric
• Automated the qualification process
• Ran 80% of their CRM through it
• Found all the whitespace in their market

Result? SDRs got 2 hours back per day, marketing got proper targeting, and AEs could finally hit self-sourcing targets.

Here’s the exact math Scott uses (and you should too):

To hit $10M ARR:

• Need: 180 new customers at $50K each
• At 25% close rate = 720 opportunities needed
• At 20% meeting-to-opp rate = 3,600 meetings needed
• At 20% conversation-to-meeting rate = 18,000 conversations needed
• At 20% contact-to-conversation rate = 90,000 dials/emails needed
• With 5 contacts per account = 18,000 accounts needed

But here’s the kicker – every 10% of unqualified accounts in this mix torpedoes your downstream metrics.

Scott’s take on AI is refreshingly practical: “AI on its own is useless. You have to target it, constrain it, focus it, and give it examples to mimic and scale.”

His process:

1. Understand the manual process that works
2. Document exactly how your best people do it
3. Use AI to scale that proven process
4. Never try to AI your way around a broken process

Scott doesn’t worship tools, but he’s specific about what works:

• Phone data: You need 20%+ connect rates. If you’re at 3%, your data sucks
• Email: Industry average is dying. Apollo worked a year ago, doesn’t now
• Clay: Great for enrichment, but it’s <50% of the actual work
• Dialer stack: Get your team having 3-5 conversations per hour

Forget activity metrics. Here’s what to track:

• Qualified account identification rate
• Contact-to-conversation rate (aim for 20% with good data)
• Conversation-to-meeting rate (10% minimum, fix messaging if lower)
• Meeting-to-opportunity rate
• Close rate by account qualification score

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https://www.linkedin.com/in/lauren-schiavone/

https://www.wonderconsultingllc.com/

Navigating AI Transformation: A Conversation with Lauren Morgenstein

Join host Jonathan Kvarfordt, AKA Coach K, in the latest episode of the G-T-M-A-I podcast, as he engages with Lauren Morganstein. Lauren shares her journey from a 16-year career at P&G to venturing into the dynamic field of AI. They discuss her decision to found Wonder Consulting and her passion for demystifying AI for non-technical leaders. The conversation delves into practical applications of AI in business, the importance of upskilling, and the transformational potential of AI within organizations. Lauren also outlines her five-step AI transformation framework and shares insights on the evolving landscape of AI native companies and the critical role of effective AI councils.

00:00 Introduction and Guest Welcome
00:47 Lauren’s Background and Career Journey
01:20 Diving into AI and Its Impact
02:48 Upskilling and Learning AI
05:08 AI in Consumer Insights and Innovation
12:42 AI Councils and Organizational Transformation
17:33 The Future of Prompting in AI
17:58 Adoption and Tool Recommendations
18:27 Maximizing Approved Tools
20:28 Balancing AI and Human Roles
22:59 Trends in AI for 2025
23:46 AI Native Companies
27:15 Culture and Change Management in AI
30:43 Personal AI Tools and Final Thoughts

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https://www.linkedin.com/in/kimberlyhacker/

AI in the Sales Journey: Lessons from Testing 22 Note-Taking Tools

I recently connected with Kim Hacker, Head of Business Operations at Arrows, who ran a fascinating experiment testing 22 AI note-taking tools simultaneously on sales calls. (read her in depth analysis here) Her insights opened my eyes to how AI is changing our sales processes in practical, meaningful ways.

Kim’s experiment came from a real need. Working at Arrows, a company building AI-powered digital sales rooms, she wanted to understand which note-taking tools would best support their AI features.

“I use ChatGPT, I use Claude, I use AI day to day. But I was feeling like I didn’t have the knowledge to back up a marketing campaign for our AI features,” Kim explained.

What surprised her was how different the results were across all 22 tools. The top performers weren’t just capturing information – they were making it immediately useful for sales reps.

Fathom took first place, with Granola second and Circle Back third. These winners stood out by producing skimmable notes that captured exactly what matters in sales – key stakeholders, timeline, and buyer interests – without unnecessary fluff.

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Sami Rejeb is no stranger to transformative technology. With over 20 years of experience in revenue management, he’s seen the sector evolve from manual operations to dynamic, AI-driven systems. His journey began as a CRM consultant at KPMG, followed by roles such as Customer Care Director for a mobile operator, Head of Value Selling for Oracle in the EMEA region, and managing RevOps for Salesforce in the Nordics. These experiences, filled with challenges and successes, motivated him to leverage AI to address the key issues revenue leaders face today.

From Global Corporations to Entrepreneurial Ventures

Sami’s global experience stretches across Oracle and Salesforce, and he has now taken a bold leap into entrepreneurship with ValueOrbit. This transition from large corporations to a startup naturally comes with its own set of differences. Sami recognizes that the agility of a startup offers unique advantages not typically found in larger, more established organizations.

While major corporations like Oracle and Salesforce are marked by a high level of sophistication in sales strategies, the startup ecosystem allows for more flexibility and fortunately quick adaptation to new opportunities. At ValueOrbit, Sami aims to harness this flexibility to answer crucial sales-related questions: What deal should I prioritize? Should this be in our forecast? What should my next step be? These questions were central during his tenure at Oracle and Salesforce, and remain so as he pioneers ValueOrbit.

The Birth of ValueOrbit

The inception of ValueOrbit stemmed from a personal mission: to maximize the potential of CRM systems in driving sales success. While working at major organizations, Sami built layers of methodologies atop existing CRM tools, but it wasn’t until the advent of AI that he truly saw the potential to transform these processes. The use of AI in sales—something that previously seemed like a distant dream—became a reality, offering unprecedented possibilities for process enhancement.

Sami’s approach with ValueOrbit focuses on revenue intelligence—spanning deal generation, closing, forecasting, and even conversational intelligence. Unlike traditional competitors, ValueOrbit doesn’t simply aim to replicate existing solutions; it strives to redefine them by concentrating on process efficiency and automation.

The Future of CRM and AI Integration

Throughout our conversation, we explored the transformative potential of AI on CRM platforms. Sami believes that while CRM systems like Salesforce offer substantial value, they are ripe for evolution. He envisions a future where traditional CRM models, driven largely by manual input, are replaced or supplemented by automated systems that enhance user interactions.

In the modern sales ecosystem, the integration of AI is not just about speeding up existing processes. It offers a unique opportunity to rethink and redesign the entire sales methodology. The current tools provide enormous data capabilities, but aligning these with practical, day-to-day operations remains a challenge that Sami is eager to tackle.

Connect with Sami Rejeb

If you’re interested in learning more about ValueOrbit and the innovative work that Sami Rejeb is doing, I encourage you to connect with him directly. You can reach out via LinkedIn or email him at sami@valueorbit.com.

Thank you for joining me in this deep dive into the future of sales and AI with Sami Rejeb. Keep innovating and challenging the status quo—together, let’s build the future of sales.

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Join Coach K, also known as Jonathan Kvarfordt, in an engaging episode of the GTM AI Podcast featuring Dr. Desiree. They discuss Dr. Desiree’s impressive journey from Macho Akia to Germany and the US, her entrepreneurial ventures, and her deep expertise in financial economics and AI. Dr. Desiree shares insights into her current work at Loyee.ai, an innovative company revolutionizing sales and marketing through AI and machine learning. Learn how Loyee.ai helps businesses refine their Ideal Customer Profiles (ICPs), enhance lead quality, and optimize go-to-market strategies for maximum efficiency and impact. Tune in for valuable lessons on the intersection of AI, data, and sales strategy.

00:00 Introduction and Guest Welcome
01:44 Guest Background and Journey
03:27 Early Startups and PhD Experience
04:46 Founding the Current Company
05:39 Product and Market Fit
07:18 Competitive Landscape and Differentiation
10:33 Data Sources and Technology
14:11 Quality and Effectiveness of Leads
15:53 Machine Learning Models vs. Practical Solutions
16:31 Simplifying Data for Clients
16:56 Challenges with Machine Learning Models
17:26 Leveraging External Data Sources
17:56 Future of Machine Learning in Business
18:16 Diving into AI and LLMs
19:42 Quality and Cost Trade-offs in AI
20:33 Strategy and Data Refreshing
24:22 Signal Identification vs. Signal Creation
28:06 Business Impact Stories
32:54 Conclusion and Contact Information

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www.sparkhire.com

In the latest episode of the GTM AI Podcast, I got into an amazing discussion with Gary Schwake from Spark Hire, Inc. , a leading provider of hiring solutions for people-powered organizations. Their conversation explores the intersection of AI within the go-to-market (GTM) strategy and the hiring process, examining how AI can serve as a transformative tool rather than a mere technological novelty.

The Spark Hire Story

When asked about Spark Hire, Gary enthusiastically explains the unique proposition of the company. Spark Hire, initially a one-way video interview solution, has expanded to include an amalgamation of offerings by acquiring Co Meet and Cha, thereby providing holistic hiring solutions. These solutions are specifically tailored for people-powered organizations in sectors such as healthcare, education, and professional services. This approach allows Spark Hire to address the nuanced needs of these industries by leveraging advanced technologies.

AI in Go-To-Market and Hiring

A key part of the discussion revolves around AI’s role in both the GTM strategy and the hiring process. Gary emphasizes the imperative of utilizing AI to enhance human capabilities rather than replace them. He highlights the challenges of relying on probabilistic AI outcomes in hiring, pointing out the need for deterministic results to avoid unintended biases.

In contrast, Jonathan reflects on the opportunities AI presents for refining interview processes and enhancing decision-making in hiring, balancing the need for data-driven insights with the irreplaceable value of human judgment.

Strategizing with AI in a Changing Landscape

Both speakers delve into how AI influences strategic decisions in contemporary business environments. Gary discusses how AI facilitates defining ideal customer profiles (ICPs) with unprecedented precision, extracting insights from unstructured data to improve targeting and engagement strategies. Jonathan, on the other hand, likens the application of AI to a strategic advantage, especially for experienced leaders who understand the framework of effective GTM strategies.

The Collaborative Future

The exchange of ideas also touches on the evolving relationship between roles within organizations, such as the CIO and CRO, as AI becomes more embedded in operations and strategy. Gary notes the necessity of a cross-functional approach in AI implementation, often orchestrated by CEOs to ensure seamless integration across departments and improve overall customer experience.

Concluding Thoughts

In concluding the episode, Gary gave insights on what businesses should prioritize and avoid as they approach the upcoming year. He stresses the importance of aligning AI initiatives with core business objectives, advising companies to begin simply and scale thoughtfully. By focusing on fundamental questions and challenges, organizations can effectively employ AI to bolster their operations and drive meaningful outcomes.

Stay tuned for more insightful conversations on the GTM AI Podcast, where technology meets strategy in the fast-evolving business landscape. For direct engagement, connect with Gary Schwake through his LinkedIn profile or explore Spark Hire solutions at sparkhire.com.

Welcome to the GTM AI Podcast intro and overview!

Sponsored by the GTM AI Academy www.gtmaiacademy.com

Where we focus on Business Impact> Learning tools.

My goal is to help GTM teams, leaders, and individuals take their performance to the next level by becoming AI powered.

Listen for tips, strategies, insights, news, trends, and more.

Peter and I delve into the impact of AI on revenue enablement and go-to-market organizations. He discusses the need for skills adaptation, the importance of enabling sellers to do their jobs more effectively, and the potential changes in the role of enablement. 

We also talked about the proper deployment and governance of AI tools, the significance of data-driven decision-making, and the enduring need for human skills in building and maintaining relationships. Overall, our conversation highlights the opportunities and challenges presented by AI in the enablement space.

5 Main key points:
– Skills adaptation and changes in enablement and sales
– Enabling sellers to perform better with the help of AI
– Proper deployment and governance of AI tools
– Importance of data-driven decision-making in enablement
– The enduring need for human skills and relationships in the AI era

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Forrester:
https://www.forrester.com/bold

The GTM AI podcast discusses the exciting new frontier of AI-powered go-to-market solutions. Ashley and Santi, founders of Momentum, share insights about harnessing AI and automation to transform sales processes. They cover Momentum’s capabilities like summarizing multilingual calls and automating Salesforce data entry, along with the upcoming AI-driven coaching module. The discussion highlights the immense potential of AI in GTM for enabling tasks like extracting insights from thousands of calls and delivering more personalized customer interactions.

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https://www.momentum.io

Key Takeaways

1. Embrace AI for Efficiency: AI offers productivity wins by streamlining repetitive tasks so teams can focus on higher-value activities. It’s a tool to be embraced, not something that replaces human ingenuity.

2. Automation for Scalability: AI-driven automation is essential for scaling sales operations and enablement. It helps teams manage overwhelming volumes of data, coach consistently, and deliver value at scale.

3. Personalized Customer Insights: The future of sales lies in AI’s ability to analyze large data sets and uncover deep customer insights. This leads to highly personalized interactions and better outcomes.

4. Training AI for Accuracy: Understanding AI’s capabilities and limitations is crucial. Feed it context-specific information through training and coaching frameworks to ensure accurate and actionable output.

5. Adapt or Lag Behind: AI is revolutionizing the GTM landscape. Companies must adopt new AI tools and strategies, or risk being disrupted by those that do.