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.

I have been geekin on Claude Skills files lately... want help? Comment below.

A "Skill" is a reusable set of instructions, packaged as a simple folder, that teaches Claude exactly how to handle specific tasks and workflows.

Not a prompt. Not a template. Not a GPT wrapper.

A systematic, portable, repeatable intelligence layer that works the same way every single time, for every person on your team.

Read that again.

Every. Person. On. Your. Team.

This is the difference between "we use AI" and "AI is embedded in how we operate."

Here's what becomes possible when GTM teams build Skills by function:
Marketing 
Build a Skill that embeds your brand guidelines, messaging framework, tone of voice, and content structure. Every piece of content — blog posts, email sequences, ad copy, one-pagers — comes out on-brand without a single revision note about "voice." Your style guide stops being a PDF nobody reads and becomes an operating system that executes automatically.

Sales Enablement 
Build Skills that generate battle cards pulling live competitive intel. Skills that create persona-specific talk tracks based on your sales methodology. Skills that produce discovery call prep docs in 60 seconds using real CRM data. Your enablement team stops being a bottleneck and starts being a force multiplier.

Revenue Operations 
Build Skills that run pipeline reviews with the same analytical rigor whether it's your VP or a new analyst asking. Forecasting workflows that apply your specific qualification criteria. Data validation processes that catch the same errors every time. Tribal knowledge becomes institutional capability.

Customer Success 
Build Skills that prep QBRs by pulling product usage data, support ticket trends, and renewal timelines into a structured executive summary. Health score analysis that follows your exact methodology. Expansion opportunity identification that runs the same playbook across every CSM on your team.

Business Development 
Build Skills that combine your ICP definition, your messaging framework, your personalization standards, and your sequence structure into one workflow. Every BDR writes outbound that sounds like it came from your top performer. New hires ramp in days instead of months.

I created a GTM-specific Skills playbook that maps directly to the roles above — with prioritized use cases, implementation sequences, and the frameworks I've used training 10,000+ GTM professionals on AI adoption.

It's the strategic layer on top of Anthropic's technical foundation.

Want it?
-Like this post
Comment "SKILL" and I'll send you BOTH:
Anthropic's Complete Guide to Building Skills for Claude (the free technical blueprint) and my own GTM version.

Or get the Anthropic guide here: https://lnkd.in/eKnFkteD
And some GTM Goodies version here: https://lnkd.in/eRtSsndC

Jonathan K.

Spent an hour watching 🚀 Victor Adefuye run 3 crazy workflows in Claude Cowork and its freakin rad. Want to see it? Comment below.

Victor has been in GTM enablement for over a decade. 5 years as an MD at Winning by Design. Teaching reps how to write emails, run discovery, execute MEDDPICC. The guy knows what "good" looks like better than almost anyone.

Then Cowork dropped. 

And he turned 10 years of enablement IP into AI agents.

Here's what he showed me. Live on the GTM AI Podcast.

He pointed Cowork at 360 conference leads and typed 3 sentences.

 Cowork spun up 15 parallel sub-agents. Each one independently researched a company, scored it against his ICP, and wrote personalized nurture emails that pulled from his 110-page content library. Matched by pain point.

He fed 7 sales calls into a folder. A custom MEDDPICC skill scored every single one in parallel. Individual scorecards with direct quotes and timestamps. Then a synthesis agent rolled up a team-wide gap analysis showing exactly where reps were weak: economic buyer, champion development, paper process.

And then came the one that broke my brain.

Victor is NOT a developer. Never has been. But in 2 weeks he built 7 Make.com automations by having a conversation with Cowork. It opened Make in his browser. Selected the modules. Configured connections. Wrote the prompts. One automation finds newly hired CROs every week and builds drip campaigns off the trigger. Built through chat.

He said something I can't stop thinking about:
"The same training I used to give to humans and hope they would remember, I now give to AI. It's more reliable."

That's the whole game right there. Your enablement playbook IS your AI moat. The people who spent years defining what good looks like are now the most dangerous builders in the room.

Stop prompting. Start packaging your expertise.

I broke down all 3 workflows step-by-step in my latest newsletter. Full playbook. Skill templates. MEDDPICC scoring rubrics. 

Everything you need to build these yourself this week.

Want to see it in action and get the full playbook?
-Like this post
--Comment "Cowork Playbook"
---Repost if you know a GTM leader who needs this

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

Enjoy!

Anthropic just dropped a jobs report that should have everyone's attention. Want the link? Comment below.

"Labor market impacts of AI: A new measure and early evidence" (Massenkoff & McCrory, March 5, 2026)

The headline findings everyone will talk about: 
→ Computer programmers: 74.5% observed AI exposure 
→ Customer service reps: 70.1% 
→ Sales reps (wholesale/manufacturing): 62.8% 
→ Market research & marketing specialists: 64.8%

The finding most people will miss: 

There's a massive gap between what AI CAN theoretically do to a job and what it's ACTUALLY doing. The "observed exposure" is a fraction of the theoretical capability.

Translation: We're still early. The wave hasn't crested.

This report looks at broad BLS occupational categories. It doesn't break down what's happening inside GTM specifically.

So I did it.

I took the same logic, theoretical vs. observed exposure, task-level analysis, automation vs. augmentation weighting—and mapped it across 7 GTM roles:
🔹 Revenue Leaders 
🔹 Customer Success 
🔹 RevOps 
🔹 Enablement 
🔹 Sales 
🔹 Business Development 
🔹 Marketing

The results are... uncomfortable in the best way.

Some roles are seeing 60-70%+ task coverage RIGHT NOW. Others have massive theoretical exposure but almost zero observed deployment.

That gap? That's your window.

If you want the original report and my GTM version, then....
-Like this post
--Comment "GTM" 
---Repost for others who wanna see

Or get it here: https://lnkd.in/e23zQNMs

I'll send you the full GTM AI Exposure Report and the original Anthropic report.

Here is how I describe how I feel with AI right now and keeping up.

I love it first off, but its overwhelming and crazy with all the constant changes.

I feel like I am trying to hit a dart board, while spinning in space, with cats surrounding me, and debris everywhere.

Anyone else feel this way sometimes?

We dug deep with Scott Wueschinski about his AI playbook that created millions in pipeline in 3 months, want to see it? Comment below.

Most GTM teams are managing 15-30 tools.

And still can't get a personalized email out without a "GTM engineer."

That's not a tech stack. That's a tax on your pipeline.

Scott joined Jonathan Moss on GTM AI to demo something different.

He built $100M in qualified pipeline in 3 months at his last company. 

Now he's productized that playbook at GTMFI. 

Here's what he showed us live:
An AI agent that builds your entire GTM foundation in 5 minutes. 

ICP, personas, pain points, use cases, competitive intel, qualifying questions. What agencies charge $5K-10K for.

Specialized outbound agents for every channel and intent type. Not one AI writing everything. Separate agents for email (onsite intent), email (offsite intent), LinkedIn, SMS, WhatsApp. 

Because a prospect who hit your pricing page needs a completely different message than one who read a competitor comparison.

A meeting follow-up automation that drafts your emails before your next call starts. Circleback to n8n to Anthropic to Slack to Gmail draft. No more 6 PM email scrambles.

The thesis that ties it together: content drives intent, intent drives outbound, outbound drives revenue.

Not the reverse. And most teams have it backwards.

Want the podcast and playbook?

-Like this post
--Comment "GTMify"
---Repost for others cuz you are nice

or here https://lnkd.in/gwF8fkrE

And I will send you all the goods

#GTM #AI #GoToMarket #SalesAutomation #B2B #GTMAIAcademy

Socure's RevOps team just showed me how they sped up sales cycles without adding heads with AI, want the playbook? Comment below

They showed me how they wired 4 tools into one orchestration layer that gives complete deal intelligence to every human who touches the account.

Evan Morgan and Colin Gerber came on the GTM AI Podcast and walked through the exact wiring:

→ How they killed the "can you loop me in?" message forever
→ Why they plugged Glean into Salesforce and it was useless (then how they fixed it)
→ The two councils that prevent tool sprawl before it starts
→ Where AI selling actually works vs. where humans still own the relationship

Most teams buy great tools that operate as isolated islands.

The orchestration layer is the entire game.

I turned this into a full GTM AI Orchestration Playbook:
-The 5-layer stack architecture
--The O-A-R maturity model
---Implementation templates with week-by-week timelines
----The 4-gate evaluation framework before any new AI capability
-----The cheat sheet you can pin and execute weekly

This isn't theory. It's how a team running $700K enterprise deals actually operates.

Want the playbook + podcast episode?

-Like this post
--Comment "ORCHESTRATE"
---Repost for your network

Let me know what you think!

or get it here: https://lnkd.in/eU3qFm9u

Sendoso fired 73% of their BDR team. Pipeline doubled. Want the playbook? Comment below.

This is literally one of the most meaty and valuable podcasts I have done cuz the Sendoso team laid out what they are doing which is pretty incredible

15+ BDRs.

Less than 15% of pipeline.

That was Sendoso's reality 18 months ago.

Today?

4 BDRs. 30%+ of pipeline. 2x growth.

And the CoCEO and Cofounder Kris Rudeegraap, their Director of Growth Austin Sandmeyer 🔥🎁, and their "secret weapon" GTM engineer Egan Callahan just broke down exactly how they did it live on the GTM AI Podcast.

Here's what nobody talks about:
Most AI pilot programs die in Week 2.

Not because the AI was bad.

Because there was no harness around it.

No session recovery. No validation layers. No rollback. Just vibes and a ChatGPT wrapper.

Sendoso built 3 production agents that actually run:
→ A contract scraper that saves 45 min per renewal 
→ A deep research engine that surfaces win/loss patterns from every deal ever 
→ An AI proposal generator that pulls from Salesforce + call transcripts automatically

They also shared the full tech stack (26 tools), the build vs. buy decision tree, and a 30-day roadmap any GTM team can follow.

We turned it into 2 free playbooks.

No fluff. No theory. Just the exact architecture, checklists, and frameworks they used.

🎙️ Full podcast episode dropping this week.

Want it? 
You know what to do... 
-Like this post 
--Comment SENDOSO 
---Repost for others who would love this

and I'll send you both playbooks + the full episode link directly.

or get it here: https://lnkd.in/e_au77rz

What's the one manual task your team does every day that AI should already be doing?

Perplexity Computer replaced $225K/yr in marketing tools in a single weekend.

They built an AI marketing agent that scans hourly, manages budgets, detects fatigue, and coordinates several campaigns end to end.

In one test run, it made 224 micro-optimizations to Perplexity’s own ad stack.

Crazy

Skills.md files are taking over the old way (6 months ago old) version of multi-agent frameworks. 

Let me paint a picture every sales leader knows too well:
Your rep needs to research ACME Corp before their demo call. 

The manual process? 45-60 minutes of tab-switching chaos:
 → LinkedIn stalking
 → Company website deep-dive
 → News scanning
 → Crunchbase hunting
 → CRM data entry
 → Email drafting

Then came the “AI solution”: Multiple specialized agents!
-Research Agent pulls company data
--Analysis Agent scores the lead
---Writer Agent drafts the email
----QA Agent checks for errors

Sounds smart, right? Wrong.

Here’s what actually happens:
 • Context gets lost between agents (like a game of telephone)
 • Your rep waits for 4 different AI systems to sync
 • Something breaks? Good luck debugging the handoff chain
 • Each agent has its own “memory” – none of them see the full picture

The Game-Changer: 
Single Agent + Multi-Skills Architecture
Instead of orchestrating 4 separate AI agents, imagine ONE agent that dynamically loads the exact skills it needs:
-research.skill.md - Pulls LinkedIn + company data
--analysis.skill.md - Scores fit + buying signals
---writer.skill.md - Crafts personalized outreach
----qa.skill.md - Verifies accuracy

The difference?
 → ONE coherent context throughout
 → No information lost in translation
 → Your rep gets a complete, accurate brief in minutes
 → Easy to debug, update, and improve

Why This Matters for Revenue:
Traditional approach = Your team wrestles with AI coordination overhead

Skills approach = Your team gets a reliable research co-pilot that ACTUALLY scales

Think of it like this: You don’t want 4 junior researchers passing sticky notes. You want ONE senior researcher with 4 specialized playbooks they can reference instantly.

Bottom Line:
The future of sales AI isn’t about having more agents. It’s about having BETTER skills that compose seamlessly within a single, reliable system.

We’ve cut lead research time from 45 minutes to 5 minutes. Not with more AI. With smarter AI architecture.

What do you think about this? how are you using skills vs a multi agent framework?

This new world is blowing my mind a bit....
#SalesAI #RevenueOperations #GTM #SalesEnablement #AIforSales #LeadResearch #SalesProductivity #B2BSales

AI agents won't fix your broken pipeline. There is no magical tool that you push and magically takes care of years of undefined fundamentals.

AI will just break it faster.

I've been in rooms with revenue leaders across the country. Dinners. Roadshows. And the gap between teams seeing real pipeline impact vs teams just burning credits comes down to one thing.

The foundation.

Here are the 5 things that will KILL your AI pipeline strategy:

1. Undefined ICP
If you can't tell your AI exactly who to target, it will target everyone. Which means no one. I also do not mean just the industry, size, company etc, you need meat.

2. Unmapped processes
AI agents follow workflows. If your workflows don't exist or live in someone's head, the agent is just guessing. 

3. Messy CRM data
Garbage in, garbage out. Except now it's garbage in, garbage out at 10x speed with 10x confidence.

4. Misaligned team definitions
If sales calls it a "qualified lead" and marketing calls the same thing an "MQL" and ops calls it something else entirely... your AI will be just as confused as your team.

5. No intentional workflows
Random automations ≠ strategy. If you haven't designed the workflow with intention, AI just automates chaos.

When the foundation IS there? When you've done the unglamorous work of aligning teams, cleaning data, and designing workflows with intention?

That's where AI agents start to change the math.

I have the pleasure of breaking all of this down on an upcoming webinar with ☁️ Cliff Simon, Ryan Staley, and Manoj Ramnani hosted by SalesIntel.io.

The full playbook on what NOT to do and how to actually build the foundation that makes AI work.

Want in?
→ Like this post
→ Comment "Pipeline"
---Repost for others who are nerds for this kind of thing.

I'll send you the webinar details.

Ok so selfish Market study.... Can you all tell me your thoughts? Recently I had the idea to change the GTM AI Podcast into more of a "behind the scenes look" at how GTM pros were using AI in various ways instead of the classic interview style... I swung the idea to Jonathan Moss my partner/cohost in AI crime and he loved it so we went all in.

3 months later, we have some REALLY meaty stuff and I am genuinely curious, has it been helpful? Do you like the change? Any topics you would love to see or guests we should have on?

Literally open to any feedback of any kind. Gimme your thoughts yo.

It’s this kind of thing that starts to blow my mind a bit. 

The internet want built for AI agents, so Google made the WebMCP. 

Banking isn’t ready or built for ai agents. 

What makes you think the modern GTM or business is? 

It requires looking at it and to build from the ground up. 

The future we will experience in less than 5 years will make us look back at 2026 as an unrecognizable year. 

Crazy times.

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Helping revenue leaders turn sales training into seller learning.

When I first started coaching B2B enterprise sellers,
I’d never actually closed an enterprise deal.
I felt like a fraud.

I was Head of Global Sales Enablement at a billion-dollar tech unicorn.
Our win rate on $70K+ deals was stuck at 17%.
The SVP of Revenue brought me in because his sales leaders “didn’t have time” to coach.

I had loads of experience with SMB and mid-market sellers.
But enterprise? Not so much.

⸻

So I outworked my self-doubt
→ Watched 500+ hours of Gong calls
→ Studied win/loss patterns and buyer conversations
→ Built a 3-month certification on product knowledge, call scripts, and MEDDICC

The team loved it.
But our buyers didn’t.

That 17% win rate?
It didn’t budge.


That’s when it hit me
Enterprise sales isn’t just scaled-up Mid-Market.
It’s a completely different beast.

I’d been coaching sellers on what to say during calls.
But larger, more complex deals hinge on what buyers do between them.


So I shifted my focus
→ Studied consulting practices at Bain, Deloitte, and BCG
→ Learned financial modeling
→ Reverse-engineered business cases from our closed-won deals

Then I coached sellers on how to write strategic narratives, 
and gave leaders the tools to reinforce them.


That’s when everything changed
We qualified fewer opportunities… but deals moved faster.
Win rates climbed.
Contract value jumped by 15%.


Now I help B2B software sales leaders:
→ Pinpoint exactly where deals stall using AI and their actual CRM data
→ Train reps to create deal-specific business cases (not generic pitches)
→ Measure rep performance beyond just pipeline metrics and win rates


Client results:
→ Veriff → +12% win rate in 6 months
→ Qminder → 26% shorter sales cycles
→ Airship → “We would have paid 3x for this type of work”

⸻

Bottom line:
Stop telling enterprise sellers what to do.
Start showing them how to think.

If your deals are stalling and your 1:1s feel like status updates, send me a DM.

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👨‍🔬 Lawrence Wayne O'Connor

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