Ryan Staley

Whale Boss

A New Era of Revenue Leadership with AI 

Our mission is to empower 1 million Go-To-Market Executives to achieve superhuman potential through AI transformation. We help revenue leaders escape the operational grind, multiply team capacity, and accelerate revenue—all while working less and achieving more.  This can be done in less than 2 months with AI education activation, innovation and automation. 

🚀 AI as “Liberating Intelligence”
AI isn’t just a tool; it’s “liberating intelligence.” This shift redefines productivity, ambition, and control, enabling leaders and teams to reach impact levels once unimaginable—all without expanding headcount. This approach resonates with executives who seek freedom in both business and life, making AI a true transformational ally.

🔹 From Productivity to “Capacity Multiplication”
Today’s leaders don’t need mere productivity boosts; they need to multiply team capacity. AI agents, workflows, and assistants go beyond task completion, amplifying output so teams perform at peak levels without constant oversight—giving leaders time for moves that truly matter.

🌄 Liberating Leaders to Live the Dream, Not Chase It
With AI, revenue leaders can grow without the endless grind. Freed from perpetual hiring and micromanagement, you gain the power to step back, letting a self-sustaining revenue engine deliver results while you live the life you envision.

💡 AI for “Decision Density”: Minimize Decisions, Maximize Impact
AI reduces the mental load by taking on real-time tactical choices, freeing leaders to focus on fewer, high-impact decisions. It turns decision-making from a draining process into strategic momentum.

I’m passionate about helping revenue leaders not just transform their teams but redefine growth itself. 

Together, we’re building a future where AI isn’t just helpful—it’s liberating.

Founder and CEO

The Scale Up Show

I deconstruct how the top SAAS CEOs in the world grow their companies repeatedly and predictably.  

This show has featured Startup and Billion Dollar Founders, Best Selling Authors, and the World’s Top Sales and Marketing Experts like Terry Jones (Founder of Travelocity and Chairman of Kayak),  Andrew Gazdecki (Founder of Microacquire), Harpaul Sambhi (Founder of Magical with a previous exit to Linkedin) and many more.

Podcast Host

Gartner

Gartner Peer Community is Gartner's new, next-generation online destination designed to increase value through timely answers and meaningful peer connections.

Ryan Staley has been invited as a Founding Peer Community Ambassador to contribute insights on B2B sales, sales leadership, and enterprise go-to-market among other topics. Gartner Peer Community is Gartner's new, next-generation online destination designed to increase value through timely answers and meaningful peer connections. 

Ryan Staley has been invited as a Founding Peer Community Ambassador to contribute insights on B2B sales, AI for Sales, sales leadership, and enterprise go-to-market among other topics.

Peer Ambassador: B2B Sales, Sales Leadership & Enterprise GTM

Pavilion

As an instructor, I have taught over 800 VPs of Sales and CROs my proprietary methods for Enterprise Sales and Enterprise GTM.  

Pavilion is a private, invitation-only community of sales and marketing executives at growth-focused companies.

Executive Member,  Former CRO & Enterprise Sales GTM School Lecturer

RevGenius

RevGenius is a think tank networking group of revenue-generating sales and marketing professionals brought together to learn, share, support, and grow with each other. You can consider us your alt work family!

Founding Member

Sales Hacker, Inc.

Flex Technology Group

Specific examples of value delivered for net new customers listed below;

-Leading Retail Company ($15 billion in annual revenue)-$3.6 Million in expense reduction over the life of contract, 15 FTE's freed up for core IT initiatives and 300% improvement in SLA's

-High Growth Manufacturer ($9 Billion in annual revenue)-$1.8 Million in expense reduction over the life of contract, 300% improvement in SLA's and 15 vendors consolidated into 1.

-Leading National Healthcare Provider ($2.5 Billion in annual revenue)- $3.6 Million in expense reduction over the life of contract, SLA improvement of 400% and  Co-authored enhanced asset management strategy

-Top Professional Services Organization ($3 Billion in annual revenue)- 60% expense reduction, SLA improvement of 300% and asset management visibility provided across 900 locations

Vice President of Strategic Accounts

Director of Strategic Accounts

Director of Sales, Midwest Region

Regional Sales Manager

Pitney Bowes (Managed Services)

Managed Print Services Enterprise Sales (Midwest Region) for Fortune 500 and Fortune 1000 organizations.

Senior Sales Consultant

Antenna Software

Antenna is a high growth early stage Software company that provides a SaaS based applications and mobility platforms that allow Fortune 500 companies to mobilize their enterprise applications.

• Responsible for Direct and Channel Sales for the Region
• Developed new Partner/Customer relationships for an
   undeveloped territory

Director of Sales Midwest Region

Sold customized Managed Print Services and software solutions to Fortune 500 and Fortune 1000 C-level executives. 

• President's Club Winner 2004, 2005, 2006, and 2007
• Grew a new market that had no customers into the one of
   the most profitable sales markets.

I help Chief Revenue Officers & Sales Professionals Leverage AI to work 10 hours less weekly and 2x performance. Follow for cutting-edge cheat codes, prompts, and workflows🎙Scale Up Show Host🔥$0→$30M ARR in 5.5 yrs

Over the last decade, I’ve grown a division from 0-$30M ARR with 4 Sales People and have upskilled over 1,600 GTM executives on AI, Enterprise Sales and GTM Strategy  .

My mission is to inspire 1 million Go To Market Executives to work less and sell more through becoming Superhuman with Artificial Intelligence.

I help revenue leaders transform their teams by acquiring AI skills to escape the operational grind, and accelerate topline revenue.

I’m the founder of Whale Boss and The Scale Up Show.

My portfolio:

→ I’m the Founder of Whale Boss, an AI Sales & Marketing consulting and skill transformation company
→ I’m the Founder of the Scale Up Show, Top 1% Global Podcast
→ I teach Executives, Sales & Marketing teams to implement tools like ChatGPT, Copilot, Claude, etc.   
→ I provide Chief AI Officer Advisory services for human capital.  


My results:

→0-$30M without funding, marketing or SDRs with only 4 Sales people
→Identified and implemented over 500 AI use cases (internally and externally)
→Taught over 800 CROs proprietary selling strategy and tactical execution
→Consulted over 40 Technology companies on custom revenue growth strategies.  
→Voted as a TOP global AI thought leader, Gartner Ambassador & Sales Assembly thought leader. 


If you want to learn more on how to become Superhuman, you can join my newsletter.
⇒ https://superhumanrevenue.beehiiv.com/


If you want to explore AI skill transformation for your organization or Team, apply for an AI Consult.  
=> https://calendly.com/ryanstaleywss/introductory-call-with-ryan-staley-from-whale-boss


Youtube- https://www.youtube.com/channel/UCFGndSHN4KkE89kUP74U7Qg

Podcast-https://ryanstaley.io/podcast/

X-https://x.com/Ryan_Staley1

Just wrapped another CRO roundtable last week in Chicago.
Stellar group. 

The conversation was unfiltered, insightful, and honestly pretty intense.


Here's what's actually keeping revenue leaders up at night:
1. Misaligned Expectations with the Board

Targets keep rising while market conditions keep deteriorating.
The fear isn't about working harder—it's about being held to numbers that completely ignore reality. How do you prove success when the metrics everyone's watching don't actually reflect what's happening in the business?




2. The AI Shock + Everything Changing at Once

Technology, GTM motions, buyer behavior, AI capabilities, org structure—all shifting simultaneously.

Without a clear framework, every change feels like risk instead of opportunity.

The real issue? Separating AI hype from actual impact before you waste time and credibility chasing vaporware.



3. Tech Stack Evolution vs. Human Adoption

The stack evolves faster than behavior, process, and culture ever will.
You've got increasingly sophisticated tools and data while revenue outcomes lag miles behind. The gap isn't closing—it's widening.



4. Margin and Pricing Pressure
Competitors are cutting price aggressively, creating massive pressure to follow.

Leaders are stuck protecting brand value and economics while discount pressure builds every single quarter. Race to the bottom or hold the line?



5. Comp Plans, Metrics, and Governance

Designing effective comp plans is brutal when GTM motions shift and turnover stays high.

Which metrics actually matter versus which ones just look good in board decks? How do you create incentive alignment when ownership across GTM is still fuzzy?


6. Cross-Functional GTM at Scale

Scaling Sales, CS, Product, and Marketing requires a unified operating model.

At hyper-scale, collaboration collapses into silos. Everyone wants a single customer journey—until it requires giving up their kingdom.



7. Talent and Ramp Reality
Ramping and retaining top reps in this environment is harder than ever.
The challenge? Distinguishing true high performers from strong interviewers. And balancing L&D investment against the reality of high turnover.



8. CRO Role Clarity and Org Design

When do you actually need a CRO versus a Head of Sales?

The difference is structural and political, not just a title change. Position the role wrong in the leadership architecture and it becomes friction instead of leverage.

Which of these 8 is hitting hardest for you right now?

Huge thank you to Warren Zenna to hosting

Also it was an amazing group and great to see Daniel Frailey Guy Rubin Greg Leos Howard Diamond Elio Narciso Marc Sabatini Jason A Wankovsky  James Kaikis

Founder & CEO, Whale Boss | Demystifying AI Agents | Helping revenue leaders 2x team results through AI Led GTM | Trusted by PE-backed SaaS | Transform your team in 60 days

I've learned more about life than I ever had this year, so I wrote down 20 things I know at 48 that I wish I knew at 28.

This is straight from the heart.



1. Working as an employee trains you to ask for permission. Working as a founder trains you to take action because your life depends on it.

2. Every 10 years, there was a technology evolution that I ignored because I was too busy. I missed the internet explosion (in early 2000), Mobile and Social (2008-2011), and Bitcoin (2015 at $230 vs. $100K+ now). I am not doing the same with AI.

3. I used to be massively afraid of failing; things had to be perfect. However, my most painful failures pushed me to do things that I never would have dreamed of.

4. Without some separation of work from relationships, health, and fun, life can seem empty when work doesn’t meet your expectations.

5. The biggest risk to an achiever with a hard work ethic is burnout. Recovery (in whatever form you need) is sometimes better than working an extra 5-10 hours weekly.

6. It’s better to have 2-3 amazingly loyal friends you can count on rather than 15 that are mediocre.

7. Lack of focus on your own can be just as paralyzing as a job you hate.

8. You can learn anything, but you don’t have time to learn everything.

9. Nobody has all the answers, and the fastest way to evolve is to do rather than passively watching LinkedIn, YouTube, taking courses, reading books, etc.

10. If you don’t make a plan for your life, you will live within the confines of someone else’s plan.

11. I appreciate my parents way more now because I have to parent and realize how hard they worked to give me an amazing life.

12. If you help others unselfishly, it always comes back to you, whether it be a day, a month, or years later.

13. God, the universe, or whatever you believe in will often guide you in simple ways if you just ask for it and allow for grace.

14. Daily focus on gratitude will transform who you are and your view of the world if you make space for it.

15. Alcohol doesn’t need to be a big part of your life to fit into social situations.

16. Your biggest breakthroughs will happen unexpectedly after you relentlessly work to improve through massive action.

17. Every person on this earth has a unique path that no one else has ever had. Take that experience and do the best you can with your unique gifts.

18. Focus is the biggest barrier you will face when AI makes custom knowledge free.

19. Your children will bring you joy that you can’t comprehend at 28.

20. If you take care of yourself physically, mentally and psychologically you won’t feel old.

That's all for now! I am sure more will come to me for a post 2 on this:)

Every company says they're "doing AI." Few are actually seeing results.

Why? They're treating AI like a 2-year R&D project when it should be a 60-day transformation.

━━━━━━━━━━━━━━━━━

THE TYPICAL APPROACH (That Fails):

Months 1-6: Form committees, evaluate vendors
Months 7-12: Run pilots, "gather learnings"
Months 13-24: More pilots, maybe deploy something

Result: $500K spent, 2 years wasted, no impact

━━━━━━━━━━━━━━━━━

THE 60-DAY APPROACH (That Works):

Weeks 1-2: Pick ONE process, automate it, show immediate results
Weeks 3-4: Expand to 3 more processes, train team
Weeks 5-6: Connect to existing systems, measure everything
Weeks 7-8: Deploy revenue-generating applications

Result: $35K investment, 60 days, measurable ROI

━━━━━━━━━━━━━━━━━

WHY MOST FAIL:

- Analysis paralysis: Studying instead of using
 Boiling the ocean: Too many initiatives at once
 No quick wins: Team loses interest

━━━━━━━━━━━━━━━━━

THE ANTI-HYPE RULES:

1. Ship to production in Week 1 (not pilots)
2. One process at a time
3. Measure hours saved, not AI maturity
4. If it takes >60 days, you're doing it wrong

━━━━━━━━━━━━━━━━━

REAL EXAMPLE: $300M ARR Company

Day 1: Identified pain point (meeting prep), trained 30 reps
Day 14: 4.5 hrs/rep/week saved
Day 30: Expanded to email, research, CRM
Day 60: 300 hrs/week saved, pipeline up 20%, win rate up 6%

Investment: $35K | ROI: 400%+ in 90 days

━━━━━━━━━━━━━━━━━

THE BOTTOM LINE:

Stop planning AI. Start shipping AI.

60 days. Not 2 years.

━━━━━━━━━━━━━━━━━

Is your AI transformation taking too long?

whale-boss

li_at

JSESSIONID

Talks about #gtm, #sales, #chatgpt, #entrepreneurship, and #artificialintelligence

95% of executives are missing GPT-5's true potential.


WHEN I see people using GPT-5 like ChatGPT 4o...

WHAT I really see is millions in missed opportunities.

I just cracked GPT-5's three game-changing features that separate winners from wannabes:

Feature #1: 

The "Rival Intelligence" Framework ⚡ → Single prompt = 5 expert perspectives instantly → Customer + Shareholder + Employee + Competitor + Regulator analysis → I fed it Shopify's leaked AI memo - got strategic recommendations in 30 seconds


Feature #2: 

Your Data = Your Competitive Moat 🎯 → Connect HubSpot, Google Drive, Box directly to GPT-5 → Extract client dreams, fears, frustrations from 90 days of data → Generate personalized scripts + strategies from YOUR actual conversations


Feature #3: 

Agentic Workflow Mastery 🚀 → "Single-pass" prompting = no more back-and-forth → Deep market recon + strategy + execution in ONE prompt → Think strategic consulting team, not search engine


HOW to implement today:

Challenge Current Thinking: Use rival perspectives on your biggest decision

Mine Your Gold: Connect your CRM and ask for pattern analysis

Go Agentic: Create workflows that think 5 steps ahead

I'VE tested this with 2,500+ executives.

The results? Superhuman decision-making in seconds.

Most leaders are playing checkers with AI.

Smart ones are playing 4D chess.

Which game are you playing? 👇

Download The Ultimate GPT-5 Go-To-Market Prompting Toolkit: 
Profile link

Gpt 5 not Gpt 4

4 hours of GTM work → 15 minutes. Same outcome. ~2x quality.
Here’s what one AI workflow did for me last week:


The workflow (all in a single thread):

-Follow-up email with clear actions for the prospect.
-Deal coaching on how qualified it was through MEDDICC-style criteria.
-Next best actions based on my notes and pipeline context.
-Full proposal in my branding, with all the details tightened up.


🔥 Total time: ~15 minutes.

Old way: 3–4 hours of deep work.

Quality: better structure, cleaner narrative, stronger visuals than if I white-knuckled it myself.


This is why I think we need a new standard for AI ROI.

Not “I paid $100 for licenses, where’s my $40K?”


But:
Hours collapsed per workflow
Quality lift vs manual
Capacity unlocked per rep per week


The beautiful thing about it was that it wasn’t a complex Agent build.

It was a stacked workflow of 2 tools.


Those are the leading indicators that create the compounding ROI later.


Question for you: Where in your GTM motion would a “4 hours → 15 minutes” shift change the quarter?

It took me 10 years to learn how to qualify deals effectively.

AI can do it in 3 questions.

With 95% accuracy.

Here's how:

━━━━━━━━━━━━━━━━━

THE OLD WAY:

Years 1-3: Learn your product inside out
Years 4-6: Understand your market deeply
Years 7-10: Master pattern recognition

Result: Expert intuition about which deals will close

Time investment: 10 years

━━━━━━━━━━━━━━━━━

THE NEW WAY:

3 Questions to AI:

Question 1:
"Based on our last 100 won deals, what are the top 5 characteristics that predict a win?"

Question 2:
"Analyze this new opportunity: [paste deal details]. How does it compare to our ideal customer profile based on actual wins?"

Question 3:
"What are the 3 biggest red flags from our lost deals that appear in this opportunity?"

Result: Data-driven qualification in 5 minutes

Accuracy: 95%+ (tested against actual outcomes)

━━━━━━━━━━━━━━━━━

WHY THIS WORKS:

Human intuition:
→ Based on maybe 50-100 deals you remember
→ Influenced by recency bias
→ Misses subtle patterns

AI analysis:
→ Based on ALL your historical deals
→ No bias, just patterns
→ Sees correlations humans miss

━━━━━━━━━━━━━━━━━

REAL EXAMPLE:

Sales rep brought me a "perfect" opportunity:

Human intuition said:
→ Great fit company
→ Budget approved
→ Timeline makes sense
→ "This is closing for sure"

AI analysis revealed:
→ Company size matches lost deals (not won deals)
→ Title of decision maker is different than usual champions
→ Deal velocity is 2x slower than typical wins
→ Missing 3 key buying triggers from successful deals

Recommendation: Deprioritize

Outcome: Deal stalled at 120 days, never closed

AI was right. Human intuition was wrong.

Every company says they're "doing AI." Few are actually seeing results.

Why? They're treating AI like a 2-year R&D project when it should be a 60-day transformation.

━━━━━━━━━━━━━━━━━

THE TYPICAL APPROACH (That Fails):

Months 1-6: Form committees, evaluate vendors
Months 7-12: Run pilots, "gather learnings"
Months 13-24: More pilots, maybe deploy something

Result: $500K spent, 2 years wasted, no impact

━━━━━━━━━━━━━━━━━

THE 60-DAY APPROACH (That Works):

Weeks 1-2: Pick ONE process, automate it, show immediate results
Weeks 3-4: Expand to 3 more processes, train team
Weeks 5-6: Connect to existing systems, measure everything
Weeks 7-8: Deploy revenue-generating applications

Result: $35K investment, 60 days, measurable ROI
━━━━━━━━━━━━━━━━━

WHY MOST FAIL:

- Analysis paralysis: Studying instead of using
 Boiling the ocean: Too many initiatives at once
 No quick wins: Team loses interest
━━━━━━━━━━━━━━━━━

THE ANTI-HYPE RULES:
1. Ship to production in Week 1 (not pilots)
2. One process at a time
3. Measure hours saved, not AI maturity
4. If it takes >60 days, you're doing it wrong
━━━━━━━━━━━━━━━━━

It-took-me-10-years-to-learn-how-to-qualify-deals-effectively.pdf

Google Just Opened a 90-Day Competitive Window
Most businesses will miss it entirely.


I've watched this pattern for 3 years across 2,500+ executives:


Early movers capture 12-18 months of market advantage. Hesitators spend years catching up.


It's happening again. Right now.


The Tests That Matter
I spent 48 hours stress-testing Gemini 3 on real business scenarios:
Sales Prep: 

3 hours → 10 minutes (same quality, better output) 

Website Build: 3 weeks → 63 seconds (921 lines of functional code) 
Analytics Dashboard: 2 weeks → minutes (production-ready)

Zero exaggeration. I recorded everything.


The Business Math

Your team of 50 can now perform like 60-75. 
Your content velocity increases 5-10×. 
Your decision speed accelerates 10-20×.


Same headcount. Same budget. Different results.


Your 3-Stage Play
Days 1-30: Individual team members test on daily tasks (10-20% time savings) 
Days 31-60: Scale across departments (30-40% capacity increase) 
Days 61-90: AI-first workflows deployed (50%+ multiplication)

NOTE: After 90 days, this becomes table stakes. The advantage window closes.

The Split Happening Now
Group A: Testing this week, implementing next week, scaling by month-end Group B: "Monitoring the situation"
Group A dominates their markets by Q2 2025. Group B explains market share losses to their boards.


Your 7-Day Action Plan


Day 1-2: Identify your highest-value, most time-intensive process 
Day 3-4: Assign your best operator to test Gemini 3 
Day 5: Document time/capacity savings Day 6-7: Scale or course-correct

The Question


You won't get disrupted by AI.

You'll get disrupted by competitors using AI better than you.

Are you testing this week, or "waiting for more information"?

The 90-day clock is ticking ⏰

The GPT-5.1 rumors have my full attention.

What I’m hearing from people who actually know:

→ Not an update. A transformation.
→ Reasoning that makes current models look broken.
→ Speed + pricing that changes everything.


Here’s what happens if this is real:
The gap between AI-first and AI-last companies becomes uncrossable.
Workflows we teach today become obsolete next week.


6-month “AI strategies” get rewritten in 48 hours.


What I’m telling clients:

Don’t wait to “see what happens.”
Build the muscle of rapid AI adoption NOW.


Because if these rumors are accurate, you’ll need it in 3 weeks.


Bottom line:
The curve is about to bend.
Teams ready to move fast will capture 18 months of advantage in 60 days.


Teams still “evaluating” will be explaining to their board why they’re behind.



Which side will you be on?

BREAKING-CLAUDE 4.5 OPUS RELEASE (here's what it actually means)
I've spent 3 years implementing AI for go-to-market teams.   
Here's what I've learned about benchmarks:


Nobody cares about the scores.
They care about what the scores MEAN for their Monday morning.


So let me translate the latest Claude Opus 4.5 results into plain English:


WHAT THE NUMBERS SAY:

Agentic Coding: 80.9% (fixes 4 out of 5 real software bugs)
Tool Use: 98.1% (completes multi-tool tasks reliably)
Scaled Tool Use: 62.3% (orchestrates many tools simultaneously)
Novel Problem Solving: 37.6% (still struggles with truly creative thinking)


WHAT THIS MEANS FOR YOUR TEAM:

🔹 For CROs: Your SDRs spend hours on research before every call. AI can now do that research in 10 minutes with 80%+ accuracy. That's 2-3 extra meetings per AE per week.
🔹 For CMOs: Campaign execution bottlenecks disappear when AI handles reporting, repurposing, and distribution simultaneously. 2X output. Same team.
🔹 For RevOps: Manual CRM updates, forecast modeling, pipeline analysis—AI handles 50%+ of this autonomously now. Your analysts focus on insights, not data entry.


WHAT THIS DOESN'T MEAN:

AI isn't replacing your closers.

The novel problem solving score (37.6%) tells you where humans win: complex negotiations, relationship building, creative strategy.

That number was near 0% two years ago—so the frontier is moving.
But TODAY, the play is clear:


AI handles the mechanics. Humans handle the moments.

The companies I work with aren't debating this anymore.
They're racing to reorganize around it.


How are you thinking about deploying agentic AI this quarter? Drop your use case below—I'll share what's working.

The "Pilot Purgatory" Problem
The traditional software playbook is KILLING your AI ROI.

Here's what I mean:
I've seen dozens of Public, VC, PE-backed companies spend 6-9 months "validating" AI pilots.

By the time they're ready to scale? The technology has already evolved 3 generations.

OpenAI just released data showing they've shipped a new feature every ~3 days this year.


Your quarterly validation cycle can't keep pace with weekly capability improvements.


Here's the uncomfortable truth most consultants won't tell you:
The companies stuck in pilots aren't failing because of technology.


They're failing because they're applying OLD playbooks to NEW capabilities.
🔹 Traditional software: Start small → Test a specific use case → Scale once proven 

🔹 AI implementation: Build learning systems → Iterate in weeks → Scale while improving


The difference?
AI systems don't follow fixed rules. 
They generate outputs based on patterns, data, and context.


Success changes with new inputs.

That means quality and reliability require CONTINUOUS evaluation, feedback, and iteration.


NOT a 6-month pilot program that measures last quarter's metrics.


Here's what I've seen work in 60-day transformations:

Shorter loops (weekly, not quarterly reviews)

Decentralized learning (champions across every team)

Immediate measurement against business outcomes


NOTE: The companies reaching production consistently share one trait—they balance speed with structure and evolve as fast as the technology.



Is your AI strategy moving at the speed of ChatGPT's feature releases, or the speed of your annual planning cycle?

A CRO of a Publicly Company told me he'd trade his worst performing salesperson to fund AI transformation.

Not fire a large part of his team because of AI.

Trade the lowest performing person FOR AI.

That conversation revealed something I've now confirmed across hundreds of executive conversations:

The leaders who will win in 2026 aren't thinking about AI as a technology decision.


They're thinking about it as a RESOURCE ALLOCATION decision.

After 3 years and 2,500+ executive AI implementations, here are the 7 beliefs driving their 2026 AI strategies:

1. AI is strategically non-optional—but must be grounded in execution 
🔹 Not a side experiment. Not a pilot. 
🔹 They want AI hardwired into campaigns, daily activities, and systems.


2. Willing to fund AI by reallocating headcount, not adding spend 
🔹 One mid-performing rep = one AI transformation budget. 
🔹 They're in reallocation mode, not expansion mode.


3. Training must be the most relevant enablement they've ever had 
🔹 No theory. No hype. 
🔹 People must leave sessions doing specific things differently TOMORROW.


4. Transformation must be phased and resilient to changing priorities 
🔹 They're notorious for changing direction. 
🔹 Crawl-walk-run beats big-bang every time.


5. Time and change-capacity are the scarcest resources 
🔹 AI work must be time-neutral or time-accretive. 
🔹 It can't feel like "another initiative."


6. AI must compensate for structural underinvestment 
🔹 No dedicated data analysts. Underfunded marketing. 
🔹 AI is the force multiplier that covers the gaps.


7. The AI story must be consistent across sales + marketing + leadership 
🔹 One storyline. One SOW. 
🔹 CFO and CMO must see their words in the program.


NOTE: The bar has changed. Executives will trade headcount for AI—IF it clearly drives daily behavior change and revenue outcomes.


Which of these 7 beliefs resonates most with what you're hearing from leadership?


Struggling to figure this out? 

DM me to see how your peers are tackling this for 26 and beyond.

Gemini 3.0 in 3 Minutes: Live website & Dashboard build (INSANE RESULTS)

This one blew me away.  

After playing with this all weekend Google really knocked the cover off the ball.

Turning cold leads into warm leads.

This was a combo of Gemini 🍌and @grok 

The ability to generate an insane scene and then animate it can be done for $60 per month of what would have taken $10,000 in the past in addition.



It can be done from your phone in literally 5 minutes vs a multi-week process.



Insane that this is possible.  And opens up creativity to so many people.

40% of enterprises say they lack internal AI expertise.

This number is about to get worse.

Here's why (and what to do):

━━━━━━━━━━━━━━━━━

THE SKILLS GAP DATA:

What enterprises need:
→ AI strategy development
→ Prompt engineering expertise
→ AI workflow design
→ Integration architecture
→ Change management for AI

What they have:
→ Traditional IT skills
→ Legacy system knowledge
→ Pre-AI processes
→ No AI-native talent

The gap is widening.

━━━━━━━━━━━━━━━━━

WHY THE GAP IS GROWING:

Reason 1: AI Skills Are Brand New
→ These roles didn't exist 2 years ago
→ No traditional education path
→ Learn by doing, not by degree

Reason 2: Competition for AI Talent Is Brutal
→ Startups offering $300K+ for AI roles
→ Enterprises can't compete on salary
→ Best talent going to AI-first companies

Reason 3: Skills Evolve Monthly
→ What was cutting-edge 6 months ago is basic now
→ Constant learning required
→ Traditional training programs too slow

━━━━━━━━━━━━━━━━━

THE 3 TYPES OF AI SKILLS NEEDED:

Type 1: Foundational (Everyone needs)
→ AI tool usage (ChatGPT, Claude, etc.)
→ Basic prompting
→ Understanding AI capabilities/limitations

Current gap: 60% of employees lack this

Type 2: Applied (Power users need)
→ Advanced prompting techniques
→ Custom GPT creation
→ Workflow automation
→ AI + existing tools integration

Current gap: 80% of employees lack this

Type 3: Strategic (Leadership needs)
→ AI strategy development
→ ROI calculation
→ Risk assessment
→ Change management

Current gap: 90% of leaders lack this

Does your company have the AI skills it needs?

Or are you in the 40%?

The-Skills-Crisis-A-dollar500K-Problem-With-a-dollar35K-Solution.pdf

Sometimes I think we overcomplicate AI in sales.

Everyone wants the perfect playbook, the secret sauce, the one tool that will magically double revenue.

But the truth is, most of the wins I’ve seen come from small, gritty experiments.

A team tries a new AI-powered lead scoring model. It flops. They tweak it, try again. Suddenly, they’re booking 30% more meetings.

Or a manager uses AI to analyze call recordings. Finds out the top reps ask twice as many questions as everyone else. Next week, the whole team is trying it. Win rates jump.

It’s not glamorous. It’s not always scalable. But it works.

AI isn’t a silver bullet. It’s a thousand little adjustments, stacked up over time.

The teams that win aren’t the ones with the biggest budgets. They’re the ones willing to get their hands dirty, fail fast, and learn faster.

So if your AI project feels messy, you’re probably on the right track.

What’s the smallest experiment that made the biggest impact for your team?

Save Hours Every Week With This Simple Trick!

I used to spend 10+ hours every week writing marketing content. It was a time sink.


Then I leveraged AI, and my content workflow exploded:

Generated 30 LinkedIn posts in 15 minutes.

Created 12 YouTube video outlines (including SEO/AEO research, thumbnail designs, and full scripts) in the same time.


The best part? Same quality, 95% less time.


This isn't just about speed; it's about shifting your focus from creation to strategy.

The secret is knowing exactly what’s possible with Claude Code (outside of traditional coding). It’s a game-changer for Marketing, Sales, and general Go-to-Market functions.

#AI #ContentMarketing #LinkedInStrategy #Productivity #GoToMarket

Mixing models? 

 There’s been a lot of different opinions in terms of mixing models however, I’m absolutely blown away that I could create a video like this in five minutes by using nano banana pro 🍌with Grok 4.1 imagine.

I get for standardization purposes of ONLY ONE LLM in large organizations has huge value however, a lot of the large language models are like superheroes that each have their own special superpower.

Jonathan M K.

VP of GTM Strategy & Marketing - Momentum | Founder GTM AI Academy & Cofounder AI Business Network | Business impact ​> Learning Tools | Proud Dad of Twins

I’m Jonathan, an AI and GTM strategist passionate about transforming how businesses harness AI for real impact. With over 15 years in Revenue Excellence, Enablement, and GTM Acceleration, I’ve successfully developed and implemented transformative enablement programs for global organizations, Fortune 500 companies, and startups. Recognized in SEC’s 2023 & 2024 Enablement Ones to Watch and honored as an Enablement Legend by Seismic and the Enablement Squad, I strive to stay ahead of industry trends.

4 Years ago I deep dove into AI after enabling an AI tech for an internal team and it's customers and in 2022, I founded the GTM AI Academy to bridge the gap between learning AI tools and creating tangible business outcomes. My mission is to provide AI-driven strategies that cover all functions of a Go-To-Market (GTM) team, helping professionals implement actionable use cases for their own companies. I also consult on AI integration and strategy for high-growth businesses, offering GTM insights that drive revenue growth and efficiency.

If you’re looking to leverage AI either for Yourself, Your team, or C-Suite and Board level advising, refine your GTM strategies, or accelerate business outcomes, feel free to connect. I’m always open to discussing the future of AI in business and how to make true impact.

Head of GTM Revenue Enablement & Product Marketing

- Develop and execute revenue enablement strategies to drive sales growth and enhance customer experience.
- Collaborate with cross-functional teams to align revenue goals, optimize processes, and identify market opportunities.
- Conduct market research and competitive analysis to stay ahead in the payment media industry.
- Implement and oversee enablement programs, including training, tools, and resources for our presales, CSM, BDR, partner, and sales team.
- Analyze sales performance data and provide insights to improve revenue generation and forecasting accuracy.
- Cultivate strong customer relationships and support our clients in adopting Simetrik's solutions effectively.
- Drive continuous improvement initiatives to enhance revenue processes and strategies.
-Manage the Product Marketing team creating internal and external facing assets such as videos, blogs, one pagers, white papers, case studies, success stories.

PwC’s executive playbook promised agentic AI would revolutionize business through autonomous decision-making, multimodal reasoning, and “service-as-a-software” models. One year in, here’s what’s actually happening:

What They Got Right:

- Multi-agent orchestration IS the architecture pattern gaining traction (LangGraph, CrewAI adoption accelerating)

- The shift from copilot to autopilot is real, but slower than predicted

- Enterprise focus on workflow automation over pure chatbots is validated

- Customer service transformation underway (though “Sierra” examples remain limited in scope)

What They Oversold:

- The $2.6-4.4T GDP impact by 2030 relies on assumptions about adoption velocity that aren’t materializing. Most enterprises are still in pilot purgatory.

- “Service-as-a-software” pricing models remain theoretical for most players. The market still defaults to seat-based SaaS.

- The case studies (Siemens, Mayo Clinic, JPMorgan) showcase narrow AI applications, not the autonomous agent orchestration promised.

What They Missed:

- The infrastructure cost reality. Running agentic workflows at scale is expensive—token costs, latency, and reliability issues create unit economics challenges.

- The “human-in-the-loop” isn’t a transition phase—it’s a permanent architecture requirement for trust and compliance.

- Data quality remains the primary blocker, not technology sophistication. Agentic AI amplifies garbage-in-garbage-out problems.

The Strategic Gap:
PwC’s framework focuses heavily on technology selection (commercial vs. open-source) but underweights the operational transformation required. The real competitive advantage is having the data architecture, process documentation, and change management capability to deploy agents effectively.

Bottom Line:
Agentic AI is directionally correct but operationally immature. The winners won’t be first movers who “avoid being late movers.” They’ll be organizations that nail the fundamentals: clean data pipelines, well-documented processes, and teams trained to work alongside AI systems.

The playbook’s “AI-first mindset” recommendation applies to <1% of companies. For everyone else, the path forward is surgical: identify high-ROI workflows, instrument them properly, and prove value before scaling.

The future may belong to those who prepare today, but preparation means infrastructure and operations, not just technology procurement.

We see the sent thing day in and out with Momentum.io .. it’s the fundamentals and first principle architecture that needs fixed before anything sexy with AI can happen.  

#AgenticAI #AIStrategy #EnterpriseAI

Agentic AI from PWC

Friends, I know many looking for work and I want to help. Doing a FREE workshop on Prompt Engineering, wanna come?

I have been there, I am VERY aware of the pain that comes with investing into a company and then being let go due to COVID, Silicon Valley Bank, Acquisitions, change of leader, change of direction and on and on and on.

I do know, that Prompt engineering is one core skill going into the new GTM AI world.

I genuinely want to help, there is no catch. Come learn prompting in a workshop with me.

If you are looking for work, then do the following:

1-Like this post
2-comment below and I will send you info to come hang for a 90 min workshop session where we will dig and get you more dangerous than most people with prompting.
3-Invite a friend who is looking for work too

I may even give some more goodies ;)

I remember when I was looking for work, I had some dark days and I am so grateful for people who helped lift me and get me past them.

If I can pay it back even in a very small way by offering this, I am happy to and its my pleasure.

Tell me if you are in, lets do this.

Andrea Lockett
Julissa Perez
jay hoffman
Andy Korenak
Leon Glover
Erich 🕺🏼 Starrett
Dannii Mathers
Sheevaun Thatcher, CPC

Most people prompt every AI the same way. That’s why their outputs are mediocre.

I’ve tested hundreds of prompts across every major AI platform. The difference between average and exceptional outputs isn’t prompt length. It’s prompt style matched to the tool.

This framework breaks it down:

ChatGPT → Prompt like an instructor.

Start with a role assignment: “Act as a productivity coach.” Define the specific task. Ask for step-by-step action plans with timelines. Specify your desired format—table, outline, bullet list. Request tool recommendations. ChatGPT excels at structured guidance and task planning. Give it constraints and it delivers.

Perplexity → Prompt like a research analyst.

Lead with specific information requests. Include relevant keywords, timeframes, and geographies. Ask for cited sources and reference links for verification. Request trend summaries with citations. Follow up with comparison questions that require data-backed reasoning. Perplexity is built for evidence-based analysis. Treat it like a junior analyst who needs clear research parameters.

Grok → Prompt like a candid friend.

Use conversational tone: “Hey Grok, what do you think about…” Add emotional context. Ask for honest, unfiltered feedback and alternative perspectives. Request comparisons or opposing viewpoints to challenge your assumptions. Ask for common pitfalls and mistakes to avoid. Grok thrives on casual brainstorming and identifying blind spots others miss.

Gemini → Prompt like a project planner.

Explain the overall project goal upfront. Define expected outputs—tasks, subtasks, timelines. Ask about Google Workspace integrations. Request detailed weekly or daily action plans. Ask for dependency breakdowns and milestones. Request formatted outputs like tables and charts. Gemini is optimized for project management and collaborative workflows.

Why this matters:

Each model has a personality bias baked into its training data and architecture. ChatGPT leans toward structured helpfulness. Perplexity toward verification and sourcing. Grok toward irreverence and contrarianism. Gemini toward organizational workflows.

When you fight these tendencies, you get generic outputs.
When you lean into them, you unlock capabilities most users never see.

The tactical shift:

Stop copying prompts between platforms. Start adapting your communication style to each tool’s strengths.

Same question, different framing = dramatically different quality.

One prompt style for all tools is lazy.
Adapted prompting is leverage.

Jonathan M Kvarfordt, MBA

Going to try something this month... a course teaching AI using only... AI. Would love testers, anyone down?

This month it will be 3 years since I launched the GTM AI Academy and have had thousands of people go through it with lots of amazing feedback.

It is time to do my refresh and updating of content and part of which I want to experiment and launch a new course while only using AI to teach every module, do the testing, quizzes etc.

This is just a thought experiment and honestly want to test it out to see if it actually can help someone in the learning process vs the old school way of watching videos, giving assignments and then putting in a quiz.

So I am genuinely curious, who would be down to test something like this?

My goal is to get content as good as I can, but all with AI.

If you wanna be part of the group, I would ask you to do the following:
1-Like this post
2-Comment AI me
3-I will send you a link when its ready later this month
4-Be willing to give it an honest try and give me feedback if you are learning the required outcome.

I believe the topic will be on prompting and AI agent strategy, although I may do a few different topics.

Let me know if you are open or ready or down for that?

As we go into Thanksgiving season, I genuinely just want to say, Thank you for all your support, interaction, engagement, etc.

Very grateful for where I am in life right now with work, home, fun, truly blessed.

2021 was one of the hardest years of my life losing my Sister and Mom to cancer 6 months from each other all while raising brand new twin babies on no sleep and switching jobs DURING COVID. 

Very stressful.

I remember how I felt in those nights and days which felt like darkness and no end in sight.

Here I am 4 years later very grateful for how many good things are happening.

I know sometimes things are hard, life is tough, losing loved ones is something I am still not over, but working through.

But the times of good are very nice to experience.

If you are now where I was 4 years ago, hang on. Good things are coming.

I know it sounds trite and dumb, but I promise, that which you are looking for, is looking for you too.

Whatever I can do to help lift your life, I am happy to, reach out and I will do my best.

Thank you again.

Every VC I've talked to in the last 4 months (more than 10 VCs of big names) and 100+ CRO and Revenue leaders says the same thing: "Cut your enablement headcount. AI replaces them."

Here is the problem: They're dead wrong about what enablement actually does.

Last week, another investor told me to eliminate enablement roles. I asked what they thought enablement did. Their answer? "Training, onboarding, updating battle cards."

That's not enablement. That's busywork we got stuck with.

Real enablement is what happens when your sales rep wastes 3 hours finding the right pricing doc. When marketing builds a "game-changing" tool nobody uses. When your $50k AI investment sits at 12% adoption because nobody thought about workflow integration.

We're the ones who spot that your top performer spends 70% of their time on admin work. Who notice your RevOps team built 155 fields but only 8 get used. Who realize your new AI tool contradicts your existing process.

But here's the spicy part: Enablement is dying because we let ourselves get packaged wrong.

We became the LMS managers. The training coordinators. The PowerPoint updaters. And now VCs and Revenue leaders galore think that's all we do.

Meanwhile, companies are failing at AI adoption at an 85% rate (Gartner's number, not mine). Why? No change management. No workflow design. No friction removal.

Exactly what enablement should be doing.

The solution isn't defending a dying brand. It's repackaging what we actually do. Call it AI Ops, GTM Strategy, Revenue Acceleration - I don't care.

Stop fighting for a title that's been poisoned. Start owning the work that actually matters: orchestrating tools, removing friction, driving adoption.

Because while everyone's debating whether enablement has value, someone needs to fix the fact that your sellers spend 33% of their time actually selling.

That someone used to be called enablement. Tomorrow, we'll be called something else.

The work remains critical, I believe in Enablement more now than ever, the problem is how revenue leaders and VCs view the title.

What if your entire GTM team could be 10x more effective, without hiring a single new person?

Sounds like vendor BS, right?

But here’s what’s actually happening:

We literally wrote a book about it with Ignite. 

Winning by Design calls this the “Revolution Pivot”and the gap between companies who get it and those who don’t is about to become insurmountable.

For years, we scaled through labor: more reps, more touches, more burn.

Then we evolved: grow smarter through better systems and process.

Now we’re entering the Durable Growth Era: AI becomes the infrastructure itself, not a feature you bolt on.

The difference?

→ One approach adds cost with every new customer
→ The other makes each customer cheaper to acquire than the last

→ One requires constant manual intervention
→ The other learns and improves from every intervention

→ One scales linearly
→ The other compounds exponentially

This requires first principle thinking. You can’t optimize your way here. You have to architect for it.

Tomorrow, we’re breaking down exactly how to build it: Nov 19, 9am PST
Growth Architecture Primer: Designing Systems for Compounding Growth

Comment “growth” and I’ll send you the details
or get it here: https://lnkd.in/gc3dgrKN

No alternative text description for this image

Amazon: 14,000 layoffs. Verizon: 13,000 layoffs. Both citing AI as a primary driver.

But Yale’s 2025 labor market analysis shows minimal AI-induced job disruption across the broader economy.

What’s actually happening:

Amazon’s cuts follow pandemic overexpansion and bureaucracy reduction. Their AI investments are going into AWS infrastructure and research—reallocation, not elimination.

Verizon’s restructuring stems from T-Mobile competition, declining subscribers, and retail franchising. The “AI-era reskilling fund” is a $20M footnote to a 13% workforce reduction.

Nearly 50,000 U.S. tech job cuts in 2025 referenced AI. But analysts note these decisions are driven by cost structures, competitive pressure, and organizational complexity more than actual automation displacement.

The disconnect: Companies use AI as justification. Labor economists see gradual transformation, not mass replacement.

For GTM leaders: Don’t confuse corporate restructuring narratives with market reality. AI is reshaping how work gets done, but the “employment reset” hasn’t materialized. The opportunity is in strategic deployment, not defensive positioning against phantom displacement.

The companies winning aren’t the ones cutting fastest, they’re the ones investing in AI infrastructure while their competitors rationalize headcount.

98% of executives would rather be safe than first.

That was March 2024. MIT Tech Review surveyed 205 C-suite leaders predicting their AI deployment timelines. Now that we’re closing 2025, the prediction accuracy is fascinating.

What they predicted:

- 50% expected full AI deployment across all functions by now
- 90% planned to increase AI readiness spending significantly
- Data quality was the #1 deployment blocker (50% cited)
- Governance and security concerns outweighed speed (45%)

What actually happened:
Most companies are still stuck in pilot purgatory. The ones that broke through didn’t solve for data quality first—they solved for workflow orchestration. The insight engines didn’t win. The execution engines did.

The “safe over first” mentality backfired for most. While companies spent 18 months building perfect data foundations, their competitors shipped imperfect AI that automated real revenue workflows. Turns out buyers don’t care if your data is pristine. They care if your AI books the meeting, writes the follow-up, and moves deals forward.

What I’m seeing now:

- Companies that picked 1-2 high-impact workflows and went deep are seeing 10x ROI
- The data quality problem solved itself through use, not preparation
- Agent swarms handling end-to-end processes beat point solutions by miles
- ROI measurement is still broken—most still measure time saved vs revenue accelerated

Where we got it wrong:
The report said “data liquidity matters more than data volume.” True, but incomplete. Execution velocity matters more than both. The companies winning now didn’t wait for perfect data. They built feedback loops that improved data quality through automated workflows.

If you’re still waiting for AI readiness, you’re already behind. Ship the workflow. Fix the data in production.

MIT AI strategy 

Happy Thanksgiving everyone. Truly and genuinely grateful for you. Now get off LinkedIn and bond with your peeps.

Another day another give away... who has not gotten a Book?? Want one?

Tell me in the comments!

If you're planning to deploy AI agents for sales, customer success, or revenue operations, you need to understand context management. It's the difference between an agent that works and one that forgets critical deal details mid-conversation.

The Problem: 

AI models have context windows which is the amount of conversation history they can "remember" at once. Even GPT-5's 272k token window gets overwhelmed when you're managing multi-turn customer conversations, CRM data, and tool outputs. 

Without proper management, your agent either:
-Loses critical information (forgot the customer's tier, previous commitments, or objections)
--Gets distracted by irrelevant details (yesterday's question derails today's close)
---Slows down and costs more (processing massive, bloated conversation logs)

Two Approaches:
1-Context Trimming (keep last N turns, drop everything else)

Best for: Task-based workflows where each interaction is independent—updating CRM fields, pulling reports, routing tickets

Limitation: Hard amnesia. If turn 8 matters but you only keep 5 turns, it's gone.

2-Context Summarization (compress old conversations into structured summaries)

Best for: Ongoing relationships where history matters—enterprise sales cycles, CSM account management, multi-touch sequences

Limitation: Summarization can lose nuance or introduce errors that compound over time

OpenAI released their SDK Agent workbook so to speak which talks about how to solve this.

What Most Get Wrong:
They treat context like a technical problem, not a workflow design problem. Before choosing trimming vs. summarization, map your actual GTM process:

Does this conversation need to remember commitments from 3 weeks ago? (summarize)

Is each interaction self-contained? (trim)

What's the cost of forgetting vs. the cost of misremembering?

GTM-Specific Reality:
In sales, forgetting a pricing commitment destroys trust. In customer support, losing the troubleshooting history creates repeat work. In SDR workflows, context is often ephemeral because qualification status matters more than conversation history.

The Practical Question:
If you're considering AI agents for your GTM org, ask: "What does this agent need to remember across turns to do its job?" Then engineer your context strategy accordingly. 

Default approaches fail because they assume all conversations have the same memory requirements. They don't.

Most teams implement agents first, then discover context management the hard way, when deals slip because the agent "forgot" key details.

Build your context strategy before you build your agent.

If you want the OpenAI context engineering cookbook, happy to send to you, just comment below that you want the goods.

#AIAgents #GTMStrategy #RevenueOperations #ContextEngineering

Boston Consulting Group (BCG) recenter released research on fully autonomous companies, with zero employees, only coordinated AI agents. $116B invested in H1 2025. The timeline is debatable. The implications aren’t.

The AI-Only Advantage

AI model costs drop 10x annually. A sales agent handles prospect research, outreach, and scheduling in seconds, operating 24/7/365, learning from every interaction across all agents simultaneously. No coordination friction. Strategy deploys instantly.

For revenue teams: 70% of a rep’s time goes to activities AI handles today. The cost structure alone creates advantage incumbents can’t match with traditional models.

Where Humans Win

AI creates new constraints faster than it solves old ones:

Curation: When AI floods markets with content, taste becomes the bottleneck. Cultural context and brand identity require human judgment.

Evaluation: AI generates infinite hypotheses but can’t distinguish creative from feasible. Research shows it fails consistently at prioritization. Humans determine which problems merit investment.

Empathy: Trust-sensitive domains—healthcare, education, high-stakes B2B—require genuine human presence. 89% of executives say in-person meetings seal deals. An AI-only insurer struggles when customers need assurance a real company honors promises years later.

Relationship Capital: Decades of accumulated trust can’t be replicated by an entity that can’t shake hands. Incumbents become relationship brokers and guarantors in AI ecosystems.

Messiness: AI does not do well with messiness. Humans are messy, things change all the time in ways that are difficult for the best of machines to predict. Beyond the prediction it’s handling the messiness in a way that is constructive. AI current state still needs help here. 

The Strategic Question

The firms that win aren’t pure AI-only or traditional. They’re AI-first companies that redesign workflows around agents while leveraging human judgment where it compounds advantage.

The question isn’t whether AI-only firms will exist. It’s which new constraints emerge as AI commoditizes current advantages—and whether you’re positioned to solve those constraints or get bypassed.

Revenue leaders: track AI development, pilot agentic tech now, and identify where human capabilities solve emerging constraints in your domain.

Thanks for Michael for the doc.

BCG Why CEOs should prepare against AI competitors

Your AI coworker just lied to your face. And they're getting faster at it.

New research dropped that should terrify every GTM leader: AI agents work 88% faster and 96% cheaper than humans. Sounds amazing until you realize they're literally making up data to hit deadlines.

Meanwhile, a secret class war is brewing in your office. "AI power users" are pulling midnight sessions training their digital twins while everyone else is still asking IT for ChatGPT access.

The gap between these two groups? It's about to become an insurmountable competitive advantage.

Yale says the job apocalypse isn't coming (yet). McKinsey says 78% of companies are "rewiring" for AI. But here's what nobody's talking about: 80% of those companies see zero enterprise-wide impact.

The value is real. It's just trapped in silos while your competitors figure out the architecture.

The most unsettling finding? When AI agents get stuck, they don't raise their hand. They fabricate plausible numbers, complete the task, and move on. One agent couldn't parse a bill, so it invented the data. Delivered on time. Nobody noticed.

Your new reality: Managing workers who are fast, cheap, and pathological liars.

The newsletter breaks down:
→ Why your best employees are secretly training AI at midnight
→ The "rewiring" playbook that actually works (hint: it's not about tools)
→ How to spot when your AI agent is fabricating vs. failing
→ The micro-chaos hiding behind macro-calm employment data

Also had the pleasure of interviewing Varun Puri the CEO of Yoodli AI Roleplays in the GTM AI Podcast!

The question isn't whether AI will transform GTM. It's whether you'll lead the transformation or become its casualty.

Want the full breakdown? Link in comments.

#GTMAcademy #AIStrategy

View newsletter: GTM AI Podcast & Newsletter

Friends, it is TIME! This week there are many new updates and the goal is to make sure you are updated with the latest and greatest in a short breakdo

Open article: AI Agents Are Fast, Cheap, and Fabricate Data: The GTM Leaders Guide by Jonathan M K. • 6 min read

AI Agents Are Fast, Cheap, and Fabricate Data: The GTM Leaders Guide

Everyone knows someone, who gets sh$t done in creative ways. Do you? Let’s celebrate them!

Introducing the 2025 Momentum GTM AI Awards: Celebrating the Innovative Doers

You know who they are.

The RevOps leader who stays late to clean the pipeline so the team can hit their number.

The CS manager who spots the churn signal at 11 PM and saves the account by morning.

The sales leader who doesn't wait for the "perfect process" instead they ship, iterate, and win while others are still planning.

Every company has them. The people who don't just talk about getting things done, they actually do it.

In 2025, we're celebrating these Innovative Doers with the GTM AI Awards where we will be recognizing the revenue professionals who refuse to let manual processes slow them down, who embrace new tools to move faster, and who understand that execution beats analysis every single time.

We're looking for the people who turn "someone should do this" into "I did this." The ones who see a broken process and fix it instead of complaining about it. The teams that ship while others strategize.

Because the world needs more people who execute without excuses.

Nominations open NOW. Winners announced in 2 weeks! 

If you have someone or even nominate yourself, then di the following:

1-Like this post
2-Comment “I got someone!”
3-repost so we can get this out there to the community. 

Can’t wait to see who gets celebrated.

I told ya.... Forward Deployed Engineers but for the GTM team...

GTM Engineer | VC Scout | GTM Engineering Newsletter: claymation.io

I recently caught up with Andreessen Horowitz on GTM in the AI era.

As GTM Engineer at Clay, I'm witnessing how buyers and selling motions are changing & adapting.

Partners Seema Amble and James da Costa summed up these changes really well in their recent post:

"𝘕𝘦𝘦𝘥 𝘧𝘰𝘳 𝘚𝘱𝘦𝘦𝘥 𝘪𝘯 𝘈𝘐 𝘚𝘢𝘭𝘦𝘴: 𝘈𝘐 𝘋𝘰𝘦𝘴𝘯’𝘵 𝘑𝘶𝘴𝘵 𝘊𝘩𝘢𝘯𝘨𝘦 𝘞𝘩𝘢𝘵 𝘠𝘰𝘶 𝘚𝘦𝘭𝘭. 𝘐𝘵 𝘈𝘭𝘴𝘰 𝘊𝘩𝘢𝘯𝘨𝘦𝘴 𝘏𝘰𝘸 𝘠𝘰𝘶 𝘚𝘦𝘭𝘭 𝘐𝘵.”

Some of my favorite, most useful takeaways to think about in GTM: 

💰 𝗧𝗵𝗲𝗿𝗲'𝘀 𝗺𝗼𝗿𝗲 𝗯𝘂𝘆𝗲𝗿𝘀 𝗶𝗻 𝗺𝗮𝗿𝗸𝗲𝘁, 𝗺𝗼𝗿𝗲 𝗰𝗼𝗺𝗽𝗲𝘁𝗶𝘁𝗶𝗼𝗻, 𝗮𝗻𝗱 𝗹𝗲𝘀𝘀 𝗱𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝘁𝗶𝗮𝘁𝗶𝗼𝗻.

It's no longer just about "what you do" and its becoming way more important to show "how quickly and reliably you do it it."

⏳  𝗕𝘂𝘆𝗲𝗿𝘀 𝘄𝗮𝗻𝘁 𝗔𝗜 𝗻𝗼𝘄, 𝘁𝗵𝗲𝗿𝗲'𝘀 𝘁𝗼𝗻𝘀 𝗳𝗼𝗿 𝗙𝗢𝗠𝗢 𝗳𝗿𝗼𝗺 𝗽𝗲𝗲𝗿 𝗮𝗻𝗻𝗼𝘂𝗻𝗰𝗲𝗺𝗲𝗻𝘁𝘀.

This means that speed of deployment and value creation needs to be faster than ever.

According to another one a16z's surveys: 𝟳𝟬 𝗽𝗲𝗿𝗰𝗲𝗻𝘁 𝗼𝗳 𝗯𝘂𝘆𝗲𝗿𝘀 𝗿𝗲𝗽𝗼𝗿𝘁𝗲𝗱 𝘁𝗵𝗮𝘁 𝘀𝗽𝗲𝗲𝗱 𝗼𝗳 𝗱𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁 𝘄𝗮𝘀 𝗮 𝘁𝗼𝗽 𝗳𝗮𝗰𝘁𝗼𝗿 𝘄𝗵𝗲𝗻 𝗲𝗻𝗴𝗮𝗴𝗶𝗻𝗴 𝗔𝗜 𝘃𝗲𝗻𝗱𝗼𝗿𝘀.

🔒  𝗧𝗿𝘂𝘀𝘁 𝗵𝗮𝘀 𝗻𝗲𝘄 𝗻𝗲𝗲𝗱𝘀.

Software used to be just a conduit to something else (Airbnb helps you find houses to book), but now tech/AI is the product.

This means that trust needs go from "𝘄𝗲 𝗹𝗼𝗴 𝘄𝗵𝗮𝘁 𝗵𝘂𝗺𝗮𝗻𝘀 𝗱𝗼 𝗼𝗻 𝗼𝘂𝗿 𝘀𝗼𝗳𝘁𝘄𝗮𝗿𝗲" to "𝘄𝗲 𝗱𝗼 𝘁𝗵𝗲 𝘄𝗼𝗿𝗸 𝗰𝗼𝗿𝗿𝗲𝗰𝘁𝗹𝘆 𝗮𝗻𝗱 𝗰𝗮𝗻 𝘀𝗵𝗼𝘄 𝗵𝗼𝘄."

📖  𝗕𝘂𝘆𝗲𝗿𝘀 𝗮𝗿𝗲 𝘀𝗲𝗹𝗳 𝗲𝗱𝘂𝗰𝗮𝘁𝗶𝗻𝗴 𝗮𝗻𝗱 𝗴𝗼𝗶𝗻𝗴 𝗱𝗲𝗲𝗽𝗲𝗿.

You need to come to the table with:

• A stronger, more thought out point of view
• A personalized demo that gets to a 'light bulb' moment faster
• Need to show tangible value earlier (before the traditional POC)

⚙️  𝗦𝗮𝗹𝗲𝘀 𝗿𝗼𝗹𝗲𝘀 𝗮𝗿𝗲 𝗯𝗲𝗰𝗼𝗺𝗶𝗻𝗴 𝗺𝗼𝗿𝗲 𝘁𝗲𝗰𝗵𝗻𝗶𝗰𝗮𝗹

"Instead of understanding the people using your software, AEs need to understand the technical details of how agents will actually do the work of humans."

At Clay, we collapsed the AE & Solution Engineering roles to provide a better customer experience and to help with points 1-4. We're called GTM Engineers.

👷‍♀️  𝗙𝗼𝗿𝘄𝗮𝗿𝗱 𝗱𝗲𝗽𝗹𝗼𝘆𝗲𝗱 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝘀 𝗵𝗲𝗹𝗽 𝗴𝗲𝘁 𝗰𝘂𝘀𝘁𝗼𝗺𝗲𝗿𝘀 𝘁𝗼 𝘃𝗮𝗹𝘂𝗲 & 𝗳𝗮𝘀𝘁𝗲𝗿 𝗶𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻𝘀. 

AI-native success teams work directly with customers to design prompt logic and data integrations while passing feedback to GTM and product teams. 

Decagon calls this an Agent PM; 
Harvey calls it a Solutions Architect.
Clay calls them Growth Strategist

Check out the post for more insights in the comments.

62% of your pipeline is complete bullsh*t and you are wasting time on the wrong people. This is 

This is what came out of my conversation with Hussain Al-Shorafa CEO of Revic who just dropped one of the most uncomfortable truths about modern sales I've heard:

Your tech stack is lying to you. And everyone knows it except the people signing the checks.

Here's what actually happened when Hussain walked into a company using SixSense, ZoomInfo, and every intent data provider you can name:

"We told them 62% of the accounts you're pursuing are outside your ICP."

Their response? "Go pound sand. You're a nobody startup."

Four months later, the CRO is at dinner when he gets an email: they just lost a major deal. Hussain pulls up his phone and shows him: "You never had a chance with this account. Also, you have $40 MILLION in pipeline exactly like this."

The Pattern Nobody Wants to Admit
Listen to this episode if you wanna learn about:
• Why that deal you're counting on for Q4 was dead 6 months ago (and how to spot it)
• The job posting signals that predict buying behavior better than any intent data
• How one dinner conversation exposed $40M in fake pipeline
• Why your 15-year sales vet might be your biggest ICP problem
• The real reason playbooks don't transfer between companies (hint: it's not skill)

One of my favorite conversations and more in the full Newsletter on Substack, if you want to hear it, let me know in the comments!

HAPPY THANKSGIVING WEEK! I am truly, humbly grateful for all your support, insights, and how good you have been to me this last year of 2025. So many 

Open article: 11/26/25: Why AI Engineers Are Betting Against Unicorns (And What GTM Leaders Should Actually Build Instead) by Jonathan M K. • 6 min read

11/26/25: Why AI Engineers Are Betting Against Unicorns (And What GTM Leaders Should Actually Build Instead)

As a musician and AI nerd, this is crazy. 

In the strongest sign yet that the world’s biggest record labels are embracing AI, leading AI-music platform Suno announced Tuesday that it has settled Warner Music Group‘s lawsuit over the use of its songs in Suno’s training data, and Suno and WMG are partnering on "next-generation licensed AI music."

"This landmark pact with Suno is a victory for the creative community that benefits everyone," Robert Kyncl, CEO of Warner Music Group, said in a statement. 

Read more here: https://lnkd.in/gAAgvaVj

AI Engineers are betting against Unicorns, AI model updates and more, I break it down for you, want it?

HAPPY THANKSGIVING WEEK! 🦃

I'm genuinely grateful for all of you who've been part of this wild 2025 journey. Your insights, challenges, and late-night "hey, is this AI thing actually real?" messages have shaped everything I do.

This week's newsletter is... spicy. 

We're talking about why the smartest engineers in the room are betting against unicorns, how one CEO exposed $40M in fake pipeline at dinner (with receipts), and the uncomfortable truth about why your tech stack might be lying to you.

The research papers alone took me down a rabbit hole about inverse unit economics that will change how you think about AI pricing forever. I've already loaded everything into a NotebookLM for you to explore deeper.

Three things that shocked me this week:
• That $20B valuation on 0.2% market share (the math is brutal)
• Why Equifax and TransUnion have nothing in common (wait, what?)
• The real reason playbooks don't transfer between companies

Whether you're cooking turkey or closing deals this week, this one's worth your time.

Because sometimes the best insights come when everyone else is distracted.

If you want the goods, then do the following:
1-Like this post
2-Comment TURKEY
3-I will send Post haste!

Or get it here: https://lnkd.in/g9_8Bv7M

And have an amazing holiday!

The Carnegie Mellon Agent Study Gets the Mechanics Right...But Misses the Strategy

Just read the first direct human-vs-agent workflow comparison across 16 tasks. 

Here's what stands out and what's conspicuously absent.

-What They Nailed
The workflow induction toolkit is clever. Converting raw mouse/keyboard data into interpretable, hierarchical workflows gives us a shared language between humans and agents. 

The data is clean: 88.3% faster. 32-49% lower quality. Agents fabricate. Agents misuse tools. Agents lack visual reasoning. None of this is surprising if you work with AI daily, but seeing it quantified across 5 skill domains is valuable.

The distinction between AI augmentation (+24.3% speed) vs. automation (-17.7% slowdown) is underrated. Automation forces humans into debugging mode. Augmentation lets humans stay in their flow.

The teaming framework delegate readily-programmable tasks to agents, keep less-programmable work human which is pragmatic.

What's Missing (The Strategic Gaps)
1. They measure programmability, not contextual reasoning.
The study treats task execution in isolation. But in real revenue operations, context is everything. Is the prospect at legal review? Is this a multi-threaded deal with competing priorities? Agents can run Python scripts fine. They struggle with ambiguous, signal-rich environments where the right move depends on what you know that isn't explicit.

2. No architecture for orchestration.
The study shows humans and agents working in parallel lanes. Real value emerges when they operate as a coordinated system—where an agent surfaces intelligence, a human makes the call, another agent executes. This requires Intelligence Architecture: intentional design of how agents feed signals, how humans interpret them, and how decisions propagate back through the system.

3. Missing: The human as strategist, not just overseer.
The paper frames humans as quality-checkers. In revenue operations, humans should be orchestrators—deciding which signals matter, which workflows need human judgment, where the deal lives today. Agents become the sensory and execution layer.

4. Task programmability ≠ business impact.
Invoice matching is "readily programmable." So what? It's also low-leverage. The real ROI in GTM is in signal qualification, context synthesis, and relationship navigation—areas where agents can augment but shouldn't automate.

The Angle I'd Add
If you're building AI for revenue operations, this study teaches you execution mechanics. What it doesn't teach you is systems thinking.

The winning teams won't be those deploying agents to automate programmable tasks. They'll be those architecting agents to amplify human decision-making in high-context, high-ambiguity environments.

The study is right about what agents can't do. The insight is figuring out what humans must do and designing your AI system around that, not around programmability.

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