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

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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.

Carnegie Mellon: Comparing AI Agents to Human Workflows

Jesse Marseille

Nooks

Growth Marketing

Storytelling at Scale

The Storytelling at Scale podcast features conversations with leaders, innovators, and freethinkers of all kinds. We talk about life, business, overcoming challenges, and the journeys that made our guests who they are today.

Storytelling at Scale is also the name of my marketing & sales consultancy which works with businesses to refine their message, understand their audience, and produce content that converts.

I specialize is growth for companies in the SaaS space.

Founder and Host

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Business Development Manager

Portland Guitar Academy

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Kaiser Permanente

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Podcaster | Telling real stories with real people

Average jiu jitsu player

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Increases Revenue and Market Share: Conducts market research to identify best suited buyer segments. Executes lead generation strategies based on data-driven insights.

Enhances Product Market Fit: Studies user engagement and contributes to product development roadmap map so that it best meets customer needs.

Promotes Brand Recognition and Reputation: Analyses market trends and positions Truebase branding to meet desires and expectations of current market. 

Forges Customer Insights and Relationships: Gains deep insight into customer behavior and preferences, building stronger more personalized relationships with customers.

Leads cross functional growth team: Facilitates collaboration between product, marketing, sales, and customer support teams.

So excited to break the news about what LiveX AI has planned for 2026 and 2027 here in California! 

I'm not allowed to say specifics yet, but if you look up the biggest sporting events in the world happening over the next 24 months, you'll get an idea ;)

All I can say is LiveX AI Holograms are coming to a stadium near you. Get ready world!

Our new AI Concierge Liam is ready for primetime and he has perhaps the smoothest accent of any AI Agent on the planet 😂

Digital channels are saturated. 

For retailers with brick & mortar stores, 2026 will be about creating a physical retail experience that cuts through the noise. 

This is what LiveX AI is here for.

Working with with LiveX AI Kiosk today, which we call the Gateway 

So cool watching all the agentic use cases we can deploy now

-ordering items
-getting directions around a mall
-checking which local restaurants have promotions 
-Onboarding into a follow up list or booking an appointment 
-Fully conversational branded interactions (yes we have a T-Swift avatar 😂 )

Over the next 6 months we'll get more and more opportunties to see the experience come to life in malls, music venues, sports arenas, and retail locations. 

Super exciting way to close out the year!

For all of my #dubai connections in #ai

We just released our new brochure for physical AI in retail + luxury mall settings. 

If interested let me know

Incredible work from our ceo and sales leader, brining LiveX AI to the Nasdaq closing bell!


Payom Niazmandi Jerry Li

⭐ There are a few truly epic highlights I have had in my career. Yesterday was one of them.

It was honor to be able to join the stage with such legends. Big thank you to Zaileen Janmohamed from the Bay Area Host Committee for inviting an ambitious Silicon Valley AI startup — just across the street from Stanford University — to attend the Nasdaq Bell Ringing Ceremony for the National Football League (NFL) Super Bowl LX. 

Meeting the legendary Roger Goodell, Commissioner of the NFL, and showing him our work in transforming the fan experience was inspirational. There’s an immense amount of care he has in bringing innovation to the sport and its fan base.

Shaking hands with corporate leaders and Jed York, owner of the San Francisco 49ers, was surreal.

Being part of LiveX AI working along side the founders Jia Li, Jerry Li, Yao Zhao has - thank God - opened up so many doors. A truly forward-thinking, AI-first team. While many AI companies throw away the waste, we threw away the brakes — bringing the era of Physical AI to life. Digital Humans / Employees / Athletes / Influencers / Concierge - Are here.

There is more to do in NYC... more updates to come. 
#SuperBowlLX #NFL #Nasdaq #NVIDIA #LiveXAI #DigitalHumans #PhysicalAI #Innovation #SiliconValley

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