Jonathan M K.

Momentum.io

https://www.amazon.com/dp/B0FRXGSDSN
Ignite Your GTM with AI: The Definitive Playbook for AI-Driven Growth

AI is no longer optional. For go-to-market leaders, the question is not if, but how fast you will adapt. Ignite Your GTM with AI is the first practitioner-led book that shows revenue leaders exactly how to embed AI across sales, marketing, customer success, and RevOps.

Inside, you’ll find:

Frameworks for transformation: Learn how to prioritize, size opportunities, and build your AI roadmap.

Build vs. buy clarity: Decide what should be proprietary and what belongs with trusted vendors.

Function-by-function playbooks: Market intelligence, demand generation, sales execution, forecasting, coaching, customer expansion, and more.

Actionable insights: From AI-driven ICP definition and deal management to predictive forecasting and expansion briefs.

Real-world voices: Contributions from industry leaders including Kyle Norton (Owner), Amanda Whiteside (Uber), Mica Oliveira (Amplemarket), Mark Roberge (Stage 2), and many more.

Each chapter pairs exclusive interviews with GTM leaders and research-backed analysis to deliver a field guide for revenue organizations in the AI era.

Whether you’re a CRO, RevOps leader, marketer, or board member, this book gives you the tools to move beyond experimentation into execution.

Co-Author

VP of GTM Strategy & Marketing

Head of GTM Growth

GTM Advisor, Ambassador, Event Host

GTM AI Academy

Business Impact & Results>Learning tools.
As Founder of the GTM AI Academy, I spearhead a pioneering educational platform dedicated to empowering Go-To-Market (GTM) professionals with cutting-edge AI insights and strategies. At the core, the Academy is a catalyst for transformation, equipping GTM individuals and teams with the tools to harness AI's potential, driving innovation, and sculpting the competitive edge of tomorrow. Working with me, GTM professionals will unlock a trove of knowledge, tailored learning experiences, and a collaborative environment where their skills will not just grow, but flourish.

CEO and Chief AI Nerd

Bryant University

Will be teaching night classes to professionals on how to use AI. 
https://accelerator.bryant.edu/courses/ai-100-foundations-artificial-intelligence

Adjunct AI Professor

GTM Buddy

Podcast Host

GTM Advisor

MindStudio

Happy to be an Ambassador for Mindstudio building custom AI Agents. Use it for free here: https://lnkd.in/gNEBqXYs

GTM AI Ambassador

Empler AI

https://www.empler.ai/
Data-Driven AI Agent Teams 
for Go-To-Market!
Achieve better results through collaboration between human teams and AI agent teams.  It's time to level up the way you work with Agentic Automations.

Strategic Advisor

Spiky.AI

Hustle Fund

Angel Squad Member

Revenue Magazine

I write spicy things on revenue and AI ;) https://revenuemagazine.com/

Contributing Author

AI Circle

Founding Member

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.

The IGNITE book is launching next week! Want a copy? We spent months interviewing 30+ GTM leaders who are actually transforming their revenue engines. 

Not theories. Not vendor promises. Real transformations that left competitors scrambling.

That's why we wrote IGNITE.

This isn't another "AI is changing everything" book. It's the definitive playbook for GTM leaders who are done playing small and ready to build systems that multiply revenue.

What's Actually Inside:

**→ The 90-Day Foundation Sprint**
Week-by-week roadmap to establish your data foundation and deliver measurable wins in Q1. No rip-and-replace drama. Just intelligent layering that works with what you have.

→ Pain-Qualified Segments (PQS) & Permissionless Value Propositions (PVP)
The breakthrough framework from Jordan Crawford that make your outreach so valuable, prospects would pay to receive it. This alone changes everything about demand gen.

→ The Build vs. Buy Decision Matrix
Stop wasting budget on shiny objects. Evaluate every tech choice based on its ability to create competitive advantage, not just automate tasks.

→ AI-Driven Coaching Systems
Architect enablement that predicts performance gaps before they happen. Deliver "just-in-time" skills at scale. Your reps improve faster than competitors can react. Christina Brady gave us the goods on her Predictive Enablement.

→ The Triangulation Forecast
Combine AI analysis with human intelligence for 95%+ accuracy. Because boards don't care about your "AI experiments" - they care about predictable revenue.

→ The Intelligence Architecture Stack
The complete blueprint for layering data, insights, and action across your entire GTM motion. From first touch to renewal. This is what creates the moat.

Here's what kills me:

Companies spend millions on tools that do 10% of what's possible. Meanwhile, the leaders in IGNITE are building systems that see around corners.

The gap between AI-native GTM teams and everyone else? It's about to become insurmountable.

This book teaches you to build for the future, not patch the present.

Comment "IGNITE" and I'll send you:
- Early access link!

Or get it here: https://lnkd.in/gJMuf5tg

Will be a fun week next week ;)

PS HAPPY HALLOWEEN

#GTM #AI #Revenue #IntelligenceArchitecture #IGNITE

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!

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.

👨‍🔬 Lawrence Wayne O'Connor

𝗖𝗙𝗢: “What’s this budget approval for again?”
𝗖𝗵𝗮𝗺𝗽𝗶𝗼𝗻: “It’s an AI call recording tool for our sales team.”

𝗖𝗙𝗢: “Why do we need this now? Seems like we’ve been just fine without it.”
𝗖𝗵𝗮𝗺𝗽𝗶𝗼𝗻: “Right now, there’s no real coaching that takes place because we can’t see what’s happening on the call.”

𝗖𝗙𝗢: “Seems like it just adds more to do for our sales leaders. Have you looked at how a coaching framework could solve this?”
𝗖𝗵𝗮𝗺𝗽𝗶𝗼𝗻: “We already have a coaching framework, but this would make it easier to apply.”

𝗖𝗙𝗢: “We’ve already spent most of the Enablement & RevOps budget for the year. Are there any other teams that could use this?”
𝗖𝗵𝗮𝗺𝗽𝗶𝗼𝗻: “Our product marketing team would get lots of value from something like this.”

𝗖𝗙𝗢: “Doesn’t Zoom have a feature that allows us to record calls? We already pay for that. Sellers just don't use it.”
𝗖𝗵𝗮𝗺𝗽𝗶𝗼𝗻: “Yeah, but this is more focused on… coaching… and has better AI functionality.”

𝗖𝗙𝗢: “I think we're okay for now. Let’s revisit in 6 months.”

Your deal just died 6 mins into a 15 minute call.

Not because your product was weak.
Because your champion wasn’t ready.

The worse part:

Your reps won’t find out the deal is dead for another 2 weeks.
After 3 “just checking in” messages that went unanswered.

Your forecast is built on a house of cards.

Here’s the real issue…

Your sellers can pitch.
But their champions can’t.

We teach reps what to say during calls. 
While large deals hinge on what buyers do between them.

3 questions your champion will face every time:

→ Why now?
→ Where’s the budget coming from?
→ Why this over what we already have?

If they can’t answer those in 90 seconds, your chances of winning are murky at best.

Sales leaders, next time one of your reps is prepping for a demo, ask them this question:

"What’s the buying team's internal conversation going to sound like afterwards?"

Start there and work backwards.

Lawrence Wayne O'Connor

Enterprise Playbooks for B2B SaaS Sales Teams | Founder @ Storytechr | Former Global Head of GTM Enablement

I’ve looked at nearly 20 sales coaching programs this year. They’re ALL designed for success.
The question is… WHOSE success are they for?

Most coaching programs are designed to help the business hit forecast.
So, they rely heavily on time-poor FLMs and qualification frameworks.
Not sustainable for leaders.

Some are designed to help sellers improve their skills.
So they over-emphasize “value-packed” knowledge dumps and motivation hacks.
Not sustainable for sellers. (Trust me, I’ve tried).

Here' the problem:
While you’re busy training your reps on the latest negotiation tactics,
Your champion still can't sell your solution to their exec team.

The best programs aren’t behavior-first.
They’re buyer-first.

Before you ever think about a new technique or template…
Try mapping these four things:

1/ Buyer Journey
What key decisions do buyers need to agree on in order to buy from you?

2/ Sales Process:
What seller outputs do you consistently create to aid in those decisions?

3/ Success Metrics
What leading indicators can we measure that show that it’s working?

4/ Competency Framework
What key activities do sellers need to take to achieve our desired standard?

The questions are simple, but they’re not easy.
Get them right, and you’ve built a program rooted in your buyer’s reality rather than your business’ fantasies.

When you enable buyers,
Seller adoption and forecast accuracy follow.

Only 17% of buying time happens with sellers in the room.

Here’s how I build sales playbooks for the other 83%. 
 
Picture this:
Your champion walks into a C-suite meeting to pitch your solution.

What questions will they be asked?
What blockers will they face?
Why should anyone care?
 
These are the questions that should inform your sales playbook.
But instead, we build a rulebook filled with SOPs and ROE.
Then wonder why sellers don’t revisit them after onboarding.
 
The Buyer Decision Canvas flips this.
 It maps exactly what buyers decide when you're not in the room.
 
I use this framework with every new client I work with and have iterated on it multiple times. My goal is for it to be so clear, you can use it with your team without my help.
 
Step 1: Map the buying committee

Don't just list titles. Go deeper:
→ What are their priorities?
→ What's blocking them right now?
→ How does solving this change their world?
 
Identify the thread that connects them all.
 
Step 2: Prioritize decision criteria
 
There are 4 distinct pillars of every buying decision, but they can look wildly different from deal to deal.
→ Look at your closed won deals
→ Pick a mix of the top 2-4 components for each pillar
→ Provide decision frameworks and examples for each one.
 
This gives champions a menu of frameworks they can use.
And gives sellers the confidence to guide them.
 
Step 3: Add external proof
 
Once you’ve mapped internal consensus and outlined decision logic, it’s time to build your supporting evidence.

→ Customer references for each stakeholder type
→ Research that backs your claims
→ Buyer stories that mirror their situation

Now you’ve gone from static rulebook collecting dust in your CMS
to a living, strategic guide that your sellers keep coming back to.
 
When done right, this serves as the source code for all the messaging on your enterprise deals.
 
Which underrated deal component do you see most on your closed won deals?

AI is making your sellers faster, 
but it’s also making them dumber. 
Here's the 3-part system I use to fight brain rot.
 
AI promised sellers it would handle the "admin", so they could "focus on selling."
But when you automate the output, you also delegate the thinking.
 
So you end up with: 
→ Beautiful business cases that champions don’t use. 
→ Well-structured call notes, but no clear plan of action. 
→ Leaders wasting time watching calls to find the reality the AI missed.
 
Giving reps AI without guardrails is like strapping a rocket launcher to a tricycle.
It doesn't matter how powerful the engine is if they can't steer.
 
To aim that power, you need a guidance system.

Here’s mine:
 
1. The Vehicle (The Business Case) 
The shared narrative your champion drives internally.
 
Begin co-creating this with your buyer after the first meeting. If you can’t fill in the sections, you don’t have a deal yet. This becomes the diagnostic tool that guides your champion between meetings.
 
2. The Map (The Strategic Account Plan) 
The roadmap for advancing the deal.
 
The Business Case is brief; the Account Plan is thorough. 
This is your internal project plan. It forces you to map stakeholder dynamics, identify gaps, and gather the evidence you need to win.
 
Without it, you’re sending reps into battle with a sling shot.

3. The Dashboard (The Deal Review) 
The objective reality check on deal health.
 
I’ve seen both sellers and buyers lie to themselves about timelines. This removes the emotion. It creates an unbiased report on if and when the deal will actually get done, so you can forecast without the guesswork.
 
How they work together:
 
1. The Business Case highlights the deal gaps (Buyer).
2. The Account Plan creates the plan to fill them (Seller).
3. The Deal Review tracks if the plan is working (Leader).
 
When you get these right,
AI goes from admin assistant that polishes your words.
to strategic sparring partner that punches holes in your logic.
 
Question for sales leaders: Which part of the system is your team weakest on?
 
1. The Map (Strategy)
2. The Vehicle (Consensus)
3. The Dashboard (Reality Check)

Here's how I make my clients' sales playbook stick.

Hint: It has nothing to do with content.

Your playbook doesn't stick from better slides.

It sticks from better reinforcement.

What most teams think is a content problem
Is actually a system problem.

One keeps you busy.
The other drives adoption.

Making your playbook stick isn't about building more content.

It's about matching the right reinforcement method to the right moment.

Here are 4 ways I do that with my clients:

⸻
🟦 On-Demand Info
The foundation: clear templates and frameworks
→ Examples: Business case template, champion prep checklist, stakeholder messaging guide, deal qualification framework
✅ Cost-effective, always accessible
❌ Sellers read it but don't use it
Use when: Sellers need a starting point or reference material

⸻
🟩 On-Demand Practice
Let sellers rehearse the playbook on their time
→ Examples: AI-powered champion prep simulator, interactive business case builder, objection handling scenarios, discovery call roleplay
✅ Builds muscle memory, scales easily
❌ Only works if sellers stay motivated
Use when: Skill levels vary, foundation is in place

⸻
🟨 Live Info Sessions
Explain the "why" behind the playbook
→ Examples: Playbook walkthrough, buyer journey training, champion enablement workshop, competitive positioning update
✅ Creates alignment, sellers can ask questions
❌ Doesn't change behavior by itself
Use when: Rolling out new methodology or updating strategy

⸻
🟥 Live Practice Sessions
Coach sellers to apply the playbook in real deals
→ Examples: Weekly deal reviews, champion strategy workshops, executive presentation rehearsals, in-call shadowing + debrief
✅ Highest impact on deal outcomes
❌ Most resource-intensive to scale
Use when: High-stakes moments, complex deals, behavior change needed

⸻
The teams with high playbook adoption don't have more content.
They have better reinforcement loops.
They build the foundation (bottom-left).
Then coach sellers to apply it (top-right).

Here’s how I used an internal podcast to drive sales adoption AND culture without adding more meetings.
(Save this for later)

When I audit operating rhythms, the first thing I do is cut unnecessary meetings.

One of the biggest culprits of wasted time?
The Sales Team Meeting

You know the drill:
→ Status updates sandwiched between pep-talks.
→ Roadmap progress that could’ve been a Slack message.
→ Sellers with camera off doing their laundry.

But here's what I'm learning: 
Even though it may seem useless.
In most scenarios, removing the team meeting is a huge mistake.

If you want to build team camaraderie, momentum, and shared focus, team meetings are one of your most powerful assets you have... IF you structure it right.

In my first Head of Enablement role, I dialed back our team meeting structure to its most essential components: 
Key metrics, strategic initiatives, team recognition.

What started as a short win story segment to close out meetings, turned into the most valuable 15 minutes of the week:

an internal "podcast" where we'd unpack a recently closed deal.

Here's why it worked:

1/ It made the sales process visible.

Most reps don’t follow your methodology because they don't trust it works in real life. This made it tangible.

2/ It built cross-functional trust.

When you hear from the SDR who sourced it, the AE who orchestrated it, the SE who ran the demo, the Legal associate who helped with redlines and CSM who's onboarding the customer. Suddenly, ops didn't feel so siloed.

3/ It provided real customer insight.

Instead of theory, we heard what actual buyers were thinking, what objections came up, and how the team overcame them.

Here's how to run it:

→ Give it a name (We called ours “Behind the Deal”)
→ Keep it to 10-15 minutes maximum
→ Feature 3-5 people involved in the deal (panel format)
→ Ask questions that highlight both process and customer insights
→ Call out specific moments where someone used your methodology
→ Record it or do it live with 2 -3 mins team Q&A at the end.

The result? 
Recognition for your team, proof your process works, and shared intelligence that makes everyone better.

Petition to kill the phrase “data-driven enablement”?

Too many janky Salesforce setups out there,
The only place most of that data is driving you… is crazy.

• Half-filled CRM fields
• Dashboards no one trusts
• Reports built to justify spend, not improve outcomes

“Data-driven” is the buzzword.
Data-fueled is the breakthrough.

Imagine this instead:
→ Champion = driver
→ Exec priority = GPS
→ Seller = co-pilot
→ Customer data = fuel

The #1 question every buyer is asking:
“What are other execs like me doing?”

If your sellers can't answer this question,
Your conversion rate isn't the issue.

Don't get me wrong.
Sales velocity is still the north star.

But, it doesn't tell you where your'e going.
Only where you've been.

If you want to hit your targets…
You need to help buyers hit theirs.

The sharpest sales leaders are focused on:
→ What top customers are doing differently
→ Where buyers lose momentum
→ Which insights give them confidence to move forward

You don’t need more data.
You need data with direction.
And the conviction to build enablement around it.

You prospected an account for months, booked a discovery call, and by the end of it, you have a 10/10 business case ready to go.

Then your alarm clock goes off.

Because in real life, this never happens.

When I'm working with enterprise teams, one of the biggest mental hurdles is understanding that, while the sales process may seem linear, the buyer journey is more like a spiral, circling back through the same questions at deeper levels.

While your reps are thinking: "What do I need to advance this deal to S4?"
Your buyers are thinking: "How confident am I that this will actually help us?"

Sales leaders: if you're naming your sales stages after seller activities (Discovery, Demo, Proposal), don't be surprised when your reps treat them like a series of checkpoints rather than an evolving set of agreements.

Discovery isn't a stage at the beginning of the sales process, and your business case isn't just a deliverable you create before the executive review.

Instead, discovery is an ongoing practice that uncovers decision criteria, and your business case is the tool to visualize the gaps in your discovery.

Instead of focusing on seller activities, here are 5 core buyer decisions to center your sales process around:

1. Problem Agreement → Do we have a problem you can solve?
2. Priority Agreement → Is this important enough to prioritize now?
3. Evaluation Agreement → What does success look like?
4. Value Agreement → Do we believe the promised value?
5. Commercial Agreement → Do terms work for everyone?

Reaching consensus on these agreements isn’t a linear process.
It’s an ongoing cycle that improves (or gets weaker) with every buyer interaction.

Your business case isn't a destination... it's the roadmap.

The term "Multithreading" actually comes from the computing world.
The OG definition reframed my sales approach.

When a software developer says they need to multithread, this is what they mean:

Running multiple tasks at the same time to reduce bottlenecks. 
→ Each thread handles a different part of the job 
→ All threads share the same resources 
→ The system gets more done with less friction 

But, for most sales teams, multithreading has become code for: 
“Talk to more people.” 

We're treating it like outreach 
When it's really orchestration. 

The best sellers I work with don’t just sell one-to-many. 
They sell many-to-many using voices from their own team to accelerate the deal. 

Here are 3 personas you should be threading in: 

1. The Translator 
→ Their role: de-jargon your pitch.

If you’re selling technical products, bring in someone who speaks your prospect’s language like your Head of InfoSec or a Solutions Architect. Prospects open up faster when they’re talking to someone who “gets it.” 

2. The Honcho 
→ Their role: collapse the power gap. 

When you’re blocked from senior stakeholders, borrow some internal credibility. Bring in your VP, CRO, or even CEO for a quick intro or even an async video mesage. It reframes the deal as strategic, not transactional. 

3. The Time Machine 
→ Their role: show the future. 

Buyers don’t want another testimonial. They want to see a version of themselves who already made the leap. Invite a current customer to share the story you can’t tell yourself. 

Multithreading isn’t about “adding more people.” 
It’s about assigning the right threads to the right jobs like a well-built system.

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Performance ScoreJonathan M K.'s LinkedIn Performance