Jonathan Moss

Turning GTM into a revenue factory through recurring impact & helping GTM leaders do the same | GTM Executive leveraging AI ✨ | Keynote Speaker & Advisor | Podcast 🎙️ Host |

I created the Revenue Nervous System™ a framework for building intelligent, self-improving growth engines that learn, adapt, and scale. The IMPACT+ Framework™ is how you implement it: a structured approach for embedding AI across the revenue lifecycle and turning strategy into execution.

I help Founders, Business Executives and Go-to-Market leaders transition to AI-native GTM and architect Agentic Growth Systems that eliminate friction and turn AI into measurable performance outcomes across their business.

At Experity, I serve as EVP & GM for Patient Engagement and Care Agent, the industry's first AI-native clinical intelligence platform. We're transforming how urgent care connects with patients through a multi-agent system that automates communication, personalizes engagement, and unifies clinical + commercial workflows across 3,000+ clinics.

Think of it as the Orchestration and Routing Layer for Healthcare: where data, context, and intelligence finally work together.

Beyond Experity, I work with founders, executive teams, and AI-native startups on growth architecture, GTM execution, and multi-agent system design.

Quick professional stats:
→ 87% median growth rate across all of my roles
→ $1B+ capital raised across portfolios
→ Scaled revenue $0 → $15B
→ Backed by Sequoia, Lightspeed, GTCR, Warburg Pincus, SoftBank

Core expertise: Revenue Nervous System™ | IMPACT+ Framework™ | Agentic AI Growth Strategy | AI-Native GTM | Patient Experience & Healthcare AI

Executive Member

2.5 million patient conversations in 60 days. Not over a year. Not projected. Done. 𝗪𝗲 𝗮𝗿𝗲 𝗵𝗶𝗿𝗶𝗻𝗴 𝗮 𝘁𝗲𝗮𝗺!
 
Care Agent just crossed that mark....1M+ users, 2,000+ clinics....and we're still in the opening act.
 
Here's what nobody talks about in healthcare AI: Everyone's building copilots. We went after something different.
 
The 60% of staff time lost to phone tag. Manual workflows. Printing paper that goes straight into the recycling bin.
 
When you see that same pattern across thousands of clinics and millions of patient visits? It stops being a local problem. It's an industry design flaw.
 
So we built the solution. 
 
𝗡𝗼𝘄 𝘄𝗲'𝗿𝗲 𝗵𝗶𝗿𝗶𝗻𝗴 𝗼𝘂𝗿 𝗳𝗶𝗿𝘀𝘁 𝘁𝘄𝗼 𝗿𝗼𝗹𝗲𝘀 𝘁𝗼 𝘀𝗰𝗮𝗹𝗲 𝗶𝘁:
 
→ 𝗕𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁 –  Lead acquisition and growth initiatives across current and emerging market segments
 
→ 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝗶𝗰 𝗔𝗰𝗰𝗼𝘂𝗻𝘁 𝗠𝗮𝗻𝗮𝗴𝗲𝗿 – Drive impact and value realization across our install base through growth, retention, adoption, usage, and product feedback aligned with our customers business goals and top priorities. 
 
What makes this different:
 
You'll operate like you are building a startup, but with the resources of a platform company behind you. 

Lean team. Big mandate. Shared services across marketing, RevOps, enablement, support, etc.
 
Being AI-native in how you work isn't a nice-to-have. It's table stakes. 

You'll be selling an AI-powered future while using AI to outperform traditional approaches in your own role every day.
 
This is the fastest-growing business in our organization. One of our strategic big bets.
 
If you want to transform how 40M+ annual patient interactions happen...reducing friction for both patients and the people who care for them.. let's talk.

🚨 I am not going to answer LinkedIn DMs or Comments. Here is the assignment. 

→ Find someone in my network, that I know, that will vouch for you. 𝘜𝘯𝘭𝘦𝘴𝘴 𝘺𝘰𝘶 𝘢𝘭𝘳𝘦𝘢𝘥𝘺 𝘬𝘯𝘰𝘸 𝘮𝘦. 
→ Have them reach out to me on your behalf as a recommendation. 
→ Include why you want to tackle this problem for the industry, how you are using AI today, and what impact your experience and skillset can make with our customers and team. 

#𝗛𝗶𝗿𝗶𝗻𝗴, #𝗥𝗲𝗰𝗿𝘂𝗶𝘁𝗺𝗲𝗻𝘁, #𝗝𝗼𝗯𝗦𝗲𝗮𝗿𝗰𝗵, #𝗖𝗮𝗿𝗲𝗲𝗿𝘀, #NowHiring

EVP @ Experity | AI-Native GTM Architect | Revenue Nervous System™ | Care Agent | Healthcare AI

Look ma I made it! 

84% of executives believe agentic AI will transform their business.

Only 5% have defined success metrics for it.

Harvard Business Review just dropped their agentic AI research—623 executives surveyed on expectations, readiness, and results.

The gap between belief and execution is massive.

→ 74% say AI is very important to their organization 
→ Only 26% say they're effective at using it for business outcomes

That's a 48-point gap between PowerPoint strategy and operational reality.

The report also confirms what I've been saying: this isn't a technology problem.

The biggest barriers cited? 
→ Lack of talent/skills (48%) 
→ No clear roadmap (46%)

Not infrastructure. Not budget. People and strategy.

The technology works. The human systems often don't.

The winners won't have the best technology.

They'll be the ones who understood earliest that agentic AI requires reimagining processes—not just automating them.

This is not a technology project but a business transformation. 

What you don't want to do is take an existing business process and put it on a shiny new toy of a tech stack. Then all you've done is made an expensive business process.
 
I see this constantly. Companies automate broken workflows and wonder why they're not getting 10x results.
 
I use "directed autonomy"—where agents can act independently within defined parameters, but humans remain in the loop for oversight, exception handling, and learning.
 
→ For routine workflows: fully autonomous 
→ For context-dependent workflows: shared control 
→ For high-impact workflows: always a human in the loop with escalation pathways
 
The goal isn't to remove humans. It's to give them superpowers.

90% of executives expect their industry will use agentic AI.

Only 34% are actually using it today.

→ 36% achieved greater organizational productivity 
→ 35% achieved better data-driven decision making  
→ 33% achieved cost savings 
→ 26% achieved better customer experience
 
The differentiators aren't what you'd expect.
 
Leaders don't have better technology budgets. They have better clarity. 
 
Strategy integration: 13% of leaders have agentic AI fully integrated into organizational strategy vs. just 1% of laggards.
 
Success metrics: 10% of leaders have well-defined success metrics vs. 1% of laggards.
 
Workforce understanding: 66% of leaders say employees understand the value of agentic AI vs. 24% of laggards.
 
The technology works. The human systems often don't.
 
Thanks Harvard Business Review for having me.

A year ago, 80% of my AI usage went through ChatGPT.
 
Today? It's down to 10%.
 
Claude runs 60% of my workflow. Manus handles 25%. And the shift had nothing to do with which model is "smarter."
 
Here's what actually changed:
 
I stopped being a consumer. I started being a builder.
 
ChatGPT is incredible for conversations. Ask a question, get an answer, move on. It's the best consumer AI product ever made.
 
But I'm not having conversations anymore. I'm building systems.
 
With Claude, I can: 
→ Create skills that persist across every conversation (my voice, my frameworks, my preferences—always loaded) 
→ Connect to real databases through MCP servers 
→ Build and deploy actual web apps with Claude Code 
→ Execute tasks through Cowork instead of just talking about them
 
With Manus, I can: 
→ Deploy multi-agent teams that research, synthesize, and format complex deliverables 
→ Automate recurring tasks with agents that coordinate 
→ Generate presentations with proper visuals—not just text
 
The mental model shift:
 
ChatGPT = AI that answers questions 
Claude = AI that builds systems 
Manus = AI that orchestrates agents
 
The "which AI is best" debate misses the point entirely. It's like asking whether a hammer or screwdriver is the better tool.
 
The answer depends on what you're building.
 
If you're still routing 100% of your AI work through a single tool, you're probably leaving massive value on the table.
 
The question isn't "which AI should I use?"
 
It's "what am I trying to build—and which tool was designed for that?"

I have 14,000 followers on LinkedIn and I can't reach most of you.
 
That's the quiet frustration nobody talks about. 

You made a deliberate choice to follow me. 

You said "yes, I want to see this person's content." 

And LinkedIn's algorithm basically shrugged and said "cool story, here's a post from someone's click bait asking you to comment for something you rarely receive."
 
I've been creating content for years now. 

Sharing frameworks from 21 years of scaling companies. 

Breaking down AI-native GTM strategies that are actually working. 

Having real conversations about what's broken in how we build revenue systems.
 
And maybe 3-5% of you see any of it.
 
So I'm changing the model.
 
I'm moving my daily content to Substack. 

Not because LinkedIn is dead—it's still where the professional conversation happens. 

But because I want to actually reach you.
 
Here's what's shifting:
 
𝗦𝘂𝗯𝘀𝘁𝗮𝗰𝗸 → Daily content. AI news analysis that cuts through the hype. AI-Native GTM frameworks. Deeper dives on what I'm seeing across GTM, Technology, and Business. The stuff I used to post here but couldn't guarantee you'd see.
 
𝗟𝗶𝗻𝗸𝗲𝗱𝗜𝗻 → A couple posts a week. The highlights. The bigger takes. The conversations worth having publicly.
 
If you've found value in what I share here, I'd love to have you join me over there.
 
The newsletter is free. The content is the same me—candid, tactical, occasionally wrong in interesting ways.
 
Link is below and in the featured section.
 
And I'm genuinely curious: am I the only one feeling this? What's your experience trying to reach the people who actually chose to follow you?

Open article: J Moss | Substack by substack.com

Healthcare isn't losing patients to competitors. It's losing them to expectations.
 
My kids don't remember a world where you had to hail a cab on a street corner. Or call a hotel front desk to book a room. They tap an app. The car appears. The door unlocks.
 
They're not "channel-native"—they don't think about which website, phone number, or portal to use.
 
They're service-native. The interface IS the service.
 
Uber isn't competing with taxis on price or speed anymore. The app experience became indistinguishable from the transportation itself.
 
Same with Airbnb. The booking interface didn't just improve accommodations—it became the accommodation experience.
 
Healthcare hasn't figured this out yet.
 
We're still asking patients to: 
→ Navigate portal logins 
→ Call during "business hours" 
→ Fill out the same form five times 
→ Wait on hold just to ask a simple question
 
And then we wonder why they're frustrated.
 
The urgent care industry sees 200+ million patient visits a year across 14k+ clinics. 

What we've learned building 𝗖𝗮𝗿𝗲 𝗔𝗴𝗲𝗻𝘁 at Experity is pretty clear:
 
The future isn't omni-channel. It's no-channel.
 
Younger generations don't care 𝘩𝘰𝘸 they get care. Text, voice, chat—doesn't matter. They just want it to work. Instantly. On their terms.
 
The clinics winning right now aren't the ones adding more channels.
 
They're the ones collapsing all those channels into a single intelligent interface that adapts to the patient.
 
Not "there's an app for that."
 
"There's an interface for 𝘮𝘦."
 
What's the last healthcare experience that actually felt modern to you?

"Can we be your Guinea pig?"
 
Those five words stopped me in my tracks.
 
We were mid-demo with Midwest Express Clinic —one of the fastest-growing urgent care operators in the Midwest.
 
I'd just shown them our Patient Engagement solution with Care Agent. 

Lab results delivered via webapp powered by a LLM. 

Discharge docs sent automatically. 

One unified chat instead of different ones that make clinics "look like scammers" (their words, not mine).
 
And before I could even finish the walkthrough, their team jumped in:
 
"How soon can we have this?"
 
Here's what struck me about the conversation.
 
They didn't ask about features first. They didn't ask about pricing first.
 
They went straight to the pain points that actually matter:
 
→ Patients calling back 24 hours after a visit asking for work notes 
→ Lab results getting lost in the shuffle 
→ Wasted time playing phone tag 
→ Higher costs without higher reimbursement leading to lower margins
 
Then they said something that reminded me why we built Care Agent in the first place:
 
"Reducing calls, improving experience for patients and staff, improving margins all would be a huge win."
 
That's it. That's the whole game.
 
Not "give us shiny AI." Not "we want a chatbot."
 
Just: help our people more time taking care of patients and us be able to run our operations more efficiently.
 
What I love about working with operators like Midwest Express is they don't need to be sold on the vision. They're living the chaos every day. They just need tools that actually work.
 
So yeah—we're building this thing together. 

Customer feedback shaping every feature. Real clinics testing real scenarios with real patients.
 
No vaporware. No press release promises. Just working software solving actual problems.
 
There's nothing better than a customer who says "we want to be first."
 
That's how you know you're building the right thing.

Snow day! Will be stuck here for the next 3-4 days! 

Excited to hang with the fam, sled, work with Claude Code and Cowork, and watch some movies/shows! 

Much needed after a long week.

450+ GTM leaders signed up.
 
That should tell you something about the state of AI training right now.
 
Here's the problem: 

Most AI education is still stuck in 2023. "Intro to prompting." Generic overviews. Death by slides.
 
Meanwhile, the operators actually transforming their revenue engines? 

They're operating at a completely different speed. Building systems. Automating workflows. Shipping agents that do real work.
 
The gap is widening every month.
 
So Andy Jolls and I wanted to build something different.
 
𝗔𝗜 𝗶𝗻 𝗚𝗧𝗠 𝗦𝗰𝗵𝗼𝗼𝗹 𝗮𝘁 Pavilion launches January 21st. Hands-on-keyboard. Practical. Built for leaders who need to implement—not just understand concepts.
 
We assembled a faculty of practitioners who are actually doing this work:
 
→ Liza Adams 
→ Crystal Black 
→ Josh Carter 
→ Jonathan M Kvarfordt, MBA 
→ Ryan Staley 
→ Scott Wueschinski 
→ John Williams  
 
90-minute interactive sessions. No passive learning.
 
What we're covering:
 
• AI GTM Team Hiring & Organizing
• AI Literacy Credentialing
• AI Tools in Action (real workflows, not product demos)
• Agents: From Prompt to Autonomous Action
• Growth Strategy Design
• Recurring GTM Process Automation
• The AI-First Revenue Engine (90-day implementation plan)
• Vibe Coding 101 & 201
• State of AI in 2026
 
This isn't theory. It's the playbook.
 
Link in comments. (Pavilion members only—if you're not a member, this might be your reason to join.)
 
What's the one AI skill you wish your GTM team had built six months ago?

40 million Americans are asking ChatGPT health questions every single day.
 
Not Google. Not WebMD. ChatGPT.
 
And here's the part that should wake up every healthcare executive: 7 out of 10 of those conversations happen after clinic doors close.
 
They're telling us something we don't want to hear.
 
The healthcare system has created a massive demand vacuum. And AI just walked in to fill it.
 
Look at the data from OpenAI's report:
 
Wyoming—the least populated state—has the highest rate of health-related AI messages from "hospital deserts." 

Rural communities are sending 600,000+ health messages per week. 

Nearly 2 million weekly messages are just about insurance and billing—people trying to decode medical bills and fight claim denials.
 
This isn't WebMD symptom-checking. This is AI-assisted self-advocacy.
 
The conventional take? "AI is coming for healthcare." That misses the point entirely.
 
AI isn't replacing healthcare. It's showing up where healthcare isn't.
 
11 PM on a Tuesday when your kid has a fever. 

Saturday morning trying to understand what "copay after deductible" actually means. 

The 30-minute drive to the nearest hospital when you need answers now.
 
That's when patients turn to AI. Not because they prefer it. Because they have no other option.
 
And here's what's really happening: 66% of physicians now use AI too (up from 38% last year). 

This isn't a patient-versus-provider story. 

It's the entire healthcare ecosystem saying the current system can't meet demand.
 
OpenAI just launched ChatGPT Health—encrypted, HIPAA-aligned, connected to your actual medical records. 

They built it with 260+ physicians across 60 countries.
 
The front door to healthcare is changing. 

The question is whether we're going to fight it or redesign around it.

We are trying to help solve this for patients and providers at Experity with Care Agent! 🫡

The_AI_Patient_Ally

The Blockbuster vs. Netflix Moment for Enterprise Intelligence. 

Blockbuster had more stores. More employees. More capital.
 
None of it mattered.
 
Netflix won because they understood something Blockbuster couldn't see: the value wasn't in physical distribution. It was in knowing what you wanted to watch before you knew it yourself.
 
That same moment is happening right now in enterprise software.
 
But instead of physical vs. streaming, it's scattered knowledge vs. unified context.
 
Foundation Capital just published their thesis on this. They're calling it "AI's trillion-dollar opportunity." 

It validated the context layer of the Revenue Nervous System that I have been sharing.
 
Here's what everyone's missing:
 
Your most important data doesn't live in a database. It lives in Slack threads, deal desk calls, and the tribal knowledge passed between employees.
 
"We always give X companies an extra 10% because their procurement cycles are brutal."
 
That rule isn't in your CRM. When the person who knows it leaves, it walks out the door.
 
Foundation Capital calls this the "decision trace" problem.
 
Rules tell you what should happen. Decision traces capture what actually happened—the exceptions, overrides, and cross-system context that explain 𝘸𝘩𝘺.
 
The reasoning connecting data to action was never treated as data in the first place.
 
Here's what it looks like in practice:
 
An AI agent proposes a 20% renewal discount. Policy caps it at 10%. But the agent pulls three SEV-1 incidents from PagerDuty, an open escalation in Zendesk, and a prior thread where a VP approved a similar exception.
 
The CRM stores one fact: "20% discount."
 
A context graph captures the entire story—the policy, the cross-system data, the precedent, the approval. That's what makes it auditable. That's what makes it compound.
 
Why can't incumbents build this?
 
Salesforce and Workday prioritize current state. They know what a deal looks like now—not the reasoning that justified it.
 
Snowflake and Databricks sit in the read path, not the write path. By the time data lands there, the decision context is already gone.
 
Capturing decision traces requires being in the execution path when decisions are made. Not bolting on governance after the fact.
 
"What could you achieve if the context carried by your RevOps, DevOps, or Security Ops teams became a queryable, permanent asset?"
 
That's the trillion-dollar question.

This article is validation of the Context Layer in the Revenue Nervous System that I have been sharing. 

If you want to learn more, check out the links in the comments.

Anthropic just shipped the most important AI product since ChatGPT.
 
It's called Claude Cowork. Released Sunday. Mac only. Max subscribers only.
 
Here's what early adopters are reporting:
 
→ Taxes filed in 15 minutes (normally 40 hours) 
→ Two months of admin backlog cleared in 2 hours 
→ Gmail inbox cleaned and categorized in 10 minutes 
→ Scattered notes transformed into first-draft quarterly plans
 
This isn't "AI assistant that helps you write emails."
 
This is AI that actually does the work.
 
You give it access to a folder. 

It reads, edits, and creates files. 

It connects to your Drive, Notion, browser. 

It loops you in when it matters. 

Executes independently when it doesn't.
 
The shift is subtle but massive:
 
From AI-assisted → AI-orchestrated.
 
From "help me with this task" → "here's the task, handle it."
 
But here's what most people will miss:
 
The bottleneck isn't the tool. It's your ability to give clear instructions.
 
Most executives are terrible at this. 

We've spent careers delegating to humans who fill in the gaps. 

Who ask clarifying questions. 

Who bring context we forgot.
 
AI doesn't do that. Yet.
 
So the executives who learn to "steer" AI effectively? 

They'll operate at a completely different speed.
 
The ones who dismiss this as "another chatbot upgrade"? 

They'll wonder why competitors keep pulling ahead.
 
We're moving from "there's an app for that" to "there's an agent for me."
 
The busy work is ending. 

The question is: what will you do with the time you get back?

I will be posting more as I dive in.

𝟰𝟬 𝗺𝗶𝗹𝗹𝗶𝗼𝗻 𝗽𝗲𝗼𝗽𝗹𝗲 𝗮𝘀𝗸 𝗖𝗵𝗮𝘁𝗚𝗣𝗧 𝗵𝗲𝗮𝗹𝘁𝗵 𝗾𝘂𝗲𝘀𝘁𝗶𝗼𝗻𝘀 𝗲𝘃𝗲𝗿𝘆 𝗱𝗮𝘆.
 
Not Google. ChatGPT.
 
OpenAI just dropped their "AI as Healthcare Ally" report. The numbers stopped me mid-scroll:
 
→ 5% of all ChatGPT messages globally are healthcare-related 
→ 70% of those conversations happen outside clinic hours 
→ 580,000+ weekly messages come from "hospital deserts"
 
(Source: OpenAI, January 2026)
 
Here's what most healthcare leaders are missing—the generational math.
 
70% of all generative AI users are Gen Z or Millennials (Salesforce). 

Daily AI search usage has more than doubled in six months (HigherVisibility, August 2025).
 
These aren't edge cases. These are your patients.
 
Millennials are 24-43 now. Gen Z is 13-28. 

Combined, they're your fastest-growing patient population.
 
They grew up in the on-demand economy. 

Uber when they want a ride. 

DoorDash when they want food. 

Netflix when they want entertainment.
 
Why would they expect healthcare to be different?
 
They don't.
 
39% of Gen Z say they'd delay seeing a doctor if AI tells them their symptoms are low-risk—that's actually more than the 36% who'd book an appointment. (Mesothelioma Center/SurveyMonkey, October 2025)
 
And Gen Beta? Born 2025 onward? They won't remember a world without conversational AI.
 
We're watching patient behavior shift in real-time. 

The practices that figure this out—meeting patients where they already are—will own the next decade.
 
The ones waiting for patients to "come back" to traditional channels?
 
They'll keep wondering why acquisition costs keep climbing.
 
We are building Care Agent at Experity and working with partners to interconnect the patient journey and make it intelligent from end to end to solve the fragmented and lack of transparency that happens across the healthcare journey

Care Agent will "always be available" for the patient... meeting them where they are today and staying with them for every part of their health journey.

Anthropic released 55 pages of data on how people actually use Claude.
 
The finding that should kill the "AI is coming for your job" narrative:
 
𝟱𝟮% 𝗮𝘂𝗴𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻 𝘃𝘀 𝟰𝟱% 𝗮𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗼𝗻.
 
More than half of AI usage is humans working 𝘸𝘪𝘵𝘩 AI. Not AI working 𝘪𝘯𝘴𝘵𝘦𝘢𝘥 𝘰𝘧 humans.
 
But here's what makes it interesting:
 
This ratio 𝘀𝗵𝗶𝗳𝘁𝗲𝗱 𝗯𝗮𝗰𝗸 𝘁𝗼𝘄𝗮𝗿𝗱 𝗮𝘂𝗴𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻 from August.
 
As AI capabilities improved — file creation, memory, Skills — users moved toward collaborative use. Not replacement use.
 
The more sophisticated AI gets, the more we're using it as a partner.
 
This inverts the automation anxiety narrative completely.
 
Other findings that matter for business:
 
→ Headline productivity gains (1.8pp) drop to ~1.0pp when adjusted for actual success rates. Cut in half.
 
→ Complex tasks = higher speedup (12x for college-level work) BUT lower success rates (66% vs 70% for basic tasks)
 
→ AI is a force multiplier for expertise. Not a replacement. The people extracting value already know the work.
 
→ When AI handles tasks, remaining job requirements either upskill or deskill by role. Property managers upskill (AI handles admin, humans need more judgment). Travel agents deskill (AI handles complexity, humans handle routine).
 
→ Geographic adoption converging 10x faster than 20th century tech. 

Your "we'll adopt before competitors" window is closing every day.
 
Three things for GTM leaders:
 
1. Success rates cut ROI projections in half. API success is 49%. Consumer AI is 67% — with human iteration.
2. Multi-turn extends what's possible. Single prompts hit a wall at ~3.5 hours of equivalent human work. Iteration pushes it dramatically further.
3. Design for augmentation when humans are in the loop. Save automation for back-office workflows.
 
The companies that win won't chase "AI replaces everything."
 
They'll build human-AI systems:
 
• Where does AI amplify our best people?
• Where do humans catch AI's mistakes?
• Which tasks stay human because they're our moat?
 
The replacement narrative is wrong.
 
The collaboration data is in.

Your company is probably extracting 20% of the value from your AI investments.
 
Not because you picked the wrong model. Because you picked the wrong architecture.
 
Here's what I mean:
 
Microsoft's Excel agent uses Claude 4.5. Anthropic's Claude in Excel uses Claude 4.5. 

Same model. Same underlying intelligence. 

Completely different business outcomes.
 
AIG compressed business review timelines by 5x. Data accuracy jumped from 75% to over 90%. 

Norway's $1.6 trillion sovereign wealth fund saved 213,000 hours.
 
Meanwhile, most enterprises are getting... marginally better formulas.
 
The difference isn't the AI. It's how the AI was integrated.
 
Microsoft built Claude as a formula writer. Ask a question, get a VLOOKUP. 

The AI is capped at what Excel could already do—just faster.
 
Anthropic built Claude as the analyst. 

The AI actually thinks through the problem, performs the analysis, and uses Excel to display results.
 
One approach automates your existing process. 

The other replaces it with something fundamentally better.
 
This is the question every executive should be asking about their AI vendors:
 
𝗜𝘀 𝘁𝗵𝗶𝘀 𝘁𝗼𝗼𝗹 𝘂𝘀𝗶𝗻𝗴 𝗔𝗜 𝘁𝗼 𝗱𝗼 𝘁𝗵𝗲 𝘄𝗼𝗿𝗸? 

𝗢𝗿 𝘂𝘀𝗶𝗻𝗴 𝗔𝗜 𝘁𝗼 𝘄𝗿𝗶𝘁𝗲 𝗶𝗻𝘀𝘁𝗿𝘂𝗰𝘁𝗶𝗼𝗻𝘀 𝗳𝗼𝗿 𝗹𝗲𝗴𝗮𝗰𝘆 𝘀𝗼𝗳𝘁𝘄𝗮𝗿𝗲 𝘁𝗼 𝗱𝗼 𝘁𝗵𝗲 𝘄𝗼𝗿𝗸?
 
Because that architectural choice determines whether you get 5x productivity gains or 1.2x.
 
Companies that bolt new technology onto old processes get incremental improvements. 

Companies that rebuild processes around new capabilities get exponential ones.
 
Most enterprise AI deployments right now are bolt-ons.
 
The vendors selling you "AI-powered" everything are often just routing requests to an LLM, then constraining the output to what their legacy software can handle.
 
You're paying for the model's full intelligence. You're getting a fraction of it.
 
Three questions to ask your AI vendors:
 
→ Does the AI perform the analysis, or does it write code for your existing tools to execute?
→ What's the ceiling—the AI's capability or your legacy system's capability?
→ Can you show me results from customers who rebuilt workflows vs. customers who automated existing ones?
 
The companies figuring this out now are about to create gaps their competitors can't close.

Urgent Care is evolving and with AI we have the ability to reimagine the patient journey to become connected, intelligent and always on… that’s exactly the experience patients demand. 

Come check out what we are up to and how we are tackling all of the challenges in navigating and providing healthcare today!

MORE UCC HEADLINERS ✨ 

During this main stage session, Jonathan Moss and Ian Lyman will share the latest Forrester + Experity Clinical Intelligence data and define what it takes to become an intelligent urgent care organization.

Immediately following, Aaron Weber, a stand-up comedian, Grand Ole Opry performer, and co-host of The Nateland Podcast, will bring the day of learning and networking to a fun and memorable close.

Join us in Nashville. Tickets here >> https://lnkd.in/e2qX44PQ

Your top seller just closed another massive deal.
 
Again.
 
Slack lights up. Everyone claps. But behind the smiles? Quiet frustration.
 
The rest of the team is still stuck chasing dead-end prospects. Wondering why the magic never transfers.
 
Here's the hard truth: you're not scaling performance.
 
You're scaling luck.
 
I spent years throwing training, comp plans, and pep talks at this problem. It never worked.
 
Because the gap between your top 20% and everyone else isn't talent.
 
It's intelligence.
 
Your best reps know which accounts to prioritize. Which products to position. Who actually makes the decision. And exactly when to deliver the message.
 
That intelligence lives in their heads—not in your systems.
 
ICONIQ's 2025 research shows the gap clearly:
 
→ High AI adopters: 61% of AEs hit quota 
→ Everyone else: 56%
 
That 5-point gap represents millions in revenue.
 
But here's what keeps me up at night:
 
→ High adopters close in 20 weeks. Others take 25. 
→ High adopters convert 41% of free trials. Others convert 36%. 
→ High adopters see 38% productivity gains. Others see 28%.
 
Every metric compounds. Every advantage multiplies.
 
While your systems stay static, theirs get smarter every single day.
 
The companies winning right now aren't working harder.
 
They've built what I call an Intelligence Layer—the pattern recognition engine that makes everyone as effective as their top performers.
 
Instead of 9-month ramp times, new hires are productive in week one.
 
Instead of guessing on deals, reps get guided to the right accounts with the right message in real-time.
 
Instead of pipeline being a mystery, it becomes predictable.
 
Your stars aren't superhuman.
 
They're just working with better intelligence.
 
The future of sales isn't about more reps. It's about smarter systems.
 
And the companies that figure out how to scale that intelligence across every rep?
 
They win.
 
What would change if every rep on your team could see the patterns your best performers see?

Google ads target what you typed.
 
ChatGPT ads will target what you 𝘮𝘦𝘢𝘯.
 
OpenAI announced ads coming to ChatGPT. 

800 million users, testing in the US "in coming weeks."
 
Most coverage focused on revenue models. 

"OpenAI needs money." "Subscriptions aren't enough."
 
They're missing the story.
 
When someone Googles "best CRM for small business," that's a keyword match. The algorithm knows what you typed. That's it.
 
When someone asks ChatGPT the same question? 

The AI knows context. 

Your team size from three messages ago. 

Your budget sensitivity from how you phrased things. 

Your integration needs from last week's conversation.
 
This isn't search advertising. It's intent advertising.
 
And it gets weirder.
 
OpenAI's announcement showed users can "start a direct chat" with the advertiser's AI. 

The funnel becomes: ChatGPT conversation → ad → brand AI conversation → purchase.
 
Your brand doesn't just need an ad. It needs an AI that can hold a real conversation.
 
Here's what changes for GTM operators:
 
→ Conversational brand presence becomes a strategic asset 
→ New metrics: conversation depth, AI-assisted conversion, conversational share of voice 
→ Attribution gets messy (good luck tracking that funnel) 
→ Two-tier AI is official: paid = clean, free = ads
 
OpenAI made the standard promises. 

"Ads won't influence answers." 

"Your conversations are private." 

"You control your data."
 
Every platform has said this. Watch how it evolves.
 
The last major ad platform—social media—created trillion-dollar companies.
 
This one will be bigger. And it's happening now.

Check out the link in the comments for more.

You don’t wanna miss Dannielle Sakher sharing the goods!!

View newsletter: GTM AI Podcast & Newsletter

Open article: 1/28/26: 5 AI Workflows MindStudio’s GTM Leader Runs Daily to 3x Pipeline  by Jonathan M Kvarfordt, MBA • 7 min read

1/28/26: 5 AI Workflows MindStudio’s GTM Leader Runs Daily to 3x Pipeline

$357 billion.
 
That's how much Microsoft lost in a single day.
 
Same day, Meta gained $100 billion.
 
Both companies announced essentially the same thing: massive AI spending for 2026. Somewhere in the $65-80B range each.
 
The market's reaction wasn't just different. It was a brutal verdict.
 
I've been thinking about this all weekend. 

I think what happened is the an important signal for anyone building with AI right now.
 
Here's the thing: Wall Street has stopped caring 𝘩𝘰𝘸 𝘮𝘶𝘤𝘩 you spend on AI.
 
They only care about one question: Does AI make your core business stronger, or does it make your core business obsolete?
 
Meta passed that test. Their core business is advertising. AI makes ad targeting better. More precise. More profitable. And you can't use AI to generate 3.58 billion daily active users. Their moat is people, not software.
 
Microsoft failed that test. Their core business is software and cloud. The very AI they're building—the code generation tools, the agents, the automation—threatens the licensing model that funds it all.
 
That's the question every company needs to answer right now:
 
Are you AI-Enhanced or AI-Threatened?
 
There's no middle ground anymore. Half a trillion dollars moved in 24 hours because the market has made up its mind about how to value these two camps.
 
If you're leading an AI initiative, this is your wake-up call. The ROI reckoning is here. 

"We're investing in innovation" without clear returns? That era just ended.
 
What's your honest assessment? Is your company building moats or flooding them?

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.

Every vendor promises autonomous AI for GTM. None of them can actually deliver it…. Yet. 

Here’s why and what you need to build instead.

The gap between AI hype and reality isn’t about model capability. It’s about architecture.

We go deep in this concept in the IGNITE book released just barely.

Most companies are trying to buy autonomous GTM from a single platform. 

That’s like expecting one tool to handle your entire tech stack. It won’t happen.

What actually works is a 5-layer architecture where each layer does what it’s built for:

Layer 1: Data Foundation
Your CDP, CRM, data lakehouse, and identity resolution create the ground truth. Without clean, unified data, everything built on top collapses. This is where 70% of AI projects fail, not because the AI is bad, but because the data is garbage.

Layer 2: Integration Layer
MCP (Model Context Protocol), APIs, and connectors let your systems talk to each other without building custom integrations for every combination. This is the “USB-C for AI” one protocol that connects everything.

Layer 3: Agent Layer
This is where specialized AI agents live. The Analyst pulls insights. The Strategist recommends next moves. The Content Agent generates variations. The Optimizer runs experiments. Each agent is narrow and verifiable, not trying to do everything.

Layer 4: Execution Layer
Ads, email, web personalization, campaign launches. This is where AI decisions turn into real actions. Current systems handle this autonomously for narrow tasks (bid optimization, send-time) but still need human approval for creative and strategy.

Layer 5: Human Oversight
Governance, strategy, brand stewardship, guardrails. Humans don’t disappear, they move up the stack. You own the creative direction, relationship decisions, and high-stakes judgment calls that AI can’t verify.

The reality check:
Gartner predicts 60% of brands will use agentic AI by 2028. They also predict 40% of agentic AI projects will be canceled by 2027.

The difference? 

Companies that win aren’t deploying the most AI. They’re architecting human-AI collaboration with clean data, proper governance, and realistic expectations about what AI can actually do autonomously today versus three years from now.

The people winning last year and this one are the ones planning for this future so when the tech is available they are ready. 

Are you?

This 2026 LinkedIn Bible is the roadmap to 1M+ followers. My man Chris Donnelly is letting me share, want in?

You can either view LinkedIn like a content creation chore, 
Or, like so many successful founders, as part of your business strategy. 

I decided to go all in on LinkedIn 2 years ago, 
And the results have been unbelievable. 

Literally 1000s of warm leads and an entire 8-fig business since then.

All because I built systems that turned LinkedIn into a primary revenue driver.

And now, I'm giving you my complete framework.

This is everything you need to transform LinkedIn into a lead gen machine.

Inside, you'll learn:

✅ The 4 highest-performing post types for lead generation
✅ How to set your 2026 "why" and align content to business outcomes
✅ How to identify your ICP and what content actually resonates with them
✅ The 30/30/30/10 engagement rule that builds genuine relationships at scale
✅ The deplatforming strategy that protects you from algorithm changes forever
✅ Lead magnet to email segmentation flows that move people towards your offer
✅ How to create a community feel in your comments that drives deeper connection
✅ Content repurposing systems for Instagram that multiply your reach with minimal effort

You don't need a content team or years of trial and error.

You need the right positioning, post strategy, and engagement system.

When you grab The 2026 LinkedIn Bible, 
You'll also receive an invite to Lead Generation Live.

It's a free 4-hour masterclass, 
With some of the most respected entrepreneurs in the world:

- Daniel Priestley (Entrepreneur and author of "Key Person of Influence")
- Nathan Barry (Founder of Kit)
- Sara Davies MBE (Founder of Crafter's Companion and Dragon's Den investor)
- Eric Partaker (CEO Coach of the Year with 1.1M LinkedIn followers)

We're covering exactly how to turn attention into actual business growth in 2026.

Want in? 
-Like this post
--Comment 2026 
And I will send you the link

They are limiting live attendance to 15,000 people, 
And hey expect this to go quickly due to the size of our guests' audiences. 

If you can, join us on January 15th from 12:30 pm to 5:00 pm GMT !

If you're ready to turn LinkedIn into a lead generation machine in 2026...

♻️ Repost to help your network make real progress on LinkedIn in 2026.

Day 3 of 4! More amazing peeps coming at you for the Momentum.io 2025 GTM AI/2026 Ones to Watch awards where we show off the dudes!

Again we sent out the nominations to our community and email list and had A TON of nominations, we widdled them down based on the quality and amount of nominations.

The awards are about those who are innovative, push boundaries, and are execution focused or are the doers. I am pleased to celebrate the first round of men.

Lokendra Khandelwal
Jose Zuma
Frank Nardi
Micael Oliveira of Amplemarket
Arnaud Belinga 🧊🔨
Shelton Banks
Oleg Tomenko
Navin Persaud
Glen Torregiani
Alex Kutsishin
Hussain Al-Shorafa
Toby Carrington
Doni Lerner
Jose Carlos Cortizo Perez
Fivos Aresti
Yanir Zamir
Mavon Tran
Dan Rosenthal
Eric Fisico
Mitch Osborne
Thanbir M.
🟠 Nick Thomas
Brandon Smith
Alex Sloan

Congrats and more tomorrow!

Jonathan M Kvarfordt, MBA

Day 2 of 4! The second half of the amazing ladies who won the 2025 GTM AI/2026 Ones to Watch awards!

We sent out our nomination list to our connections and email list, we received thousands of replies and went through to find the most impressive and most repeated people, yesterday we released this list: https://lnkd.in/euCghk9W

Today we have our second half! To win, people were featured because of their innovative Doer mentality, people who lead and also get shi! done.

We have:
Katie Nocerino (Hillman) 
Dione Hedgpeth
Tessa Whittaker
Patricia De Nardi
Megan Prince
Nina LaRouche
Rupa Boddu
Sabika Hassan
Dannii Mathers
Rupa Boddu
Julia Nimchinski
Brigette Sjoboen
Julia MacDonald
Alina Vandenberghe 🌶️
Ellie S.
Meeta Pandey, Ph.D
Gail Behun
Liza Adams
Azzy Maizer
Tahnee Perry
Prasangika Rai
🐰 Jen Igartua
Amanda Kahlow
Sally Weiss
Donna McCurley
Christa Brutus, PMP, MM, LSSGB, ASCP(C)
Ashley Blackwell-Guerra
Dannielle Sakher
Jessica Meyers

Congrats! Men incoming tomorrow!

Hey friends in #enablement... here is a fun one ;)

Day 4 of 4 and last list from Momentum.io and the 2025 GTM AI/2026 Ones to Watch awards.

We sent this out to our community of thousands of people, received hundreds of nominations and we widdled them down those who were either:
1-Nominated multiple times with quality reasons why
2-Impressive and impactful stories

This is the list of innovative doers, the ones who execute and have vision, the balance between AI and human hard work.

Happy to celebrate the last but not least:
Umer Maqsood
Justin Parnell
Shawn Jahromi, MBA
John-Henry Scherck
Yogesh Kochhar
Brian Dietmeyer
Andrew Mezheritskiy, MBA
Andrew Cooper
Julian Teixeira
Rahul Jaglan 🧑🏽‍🚀
Alex Lindahl
Kumail Mukadam
Kevin Chu
Laurence Judah
Mike Y.
Seth Stover
Arjun Ganatra
Kyle Norton
Jacco van der Kooij
Jonathan Moss
Mark Turner
Collin Rhea
Daniel Clune
Ben Perceval
Connor Schaars
Patrick Kalie
Nathan Follen
Logan Atkinson-Wall
Omri Sela
Usher (Arshia) Razavi
Adarsh S Lathika
Mark Roberge
brendan short
Elio Narciso
Evan Morgan
Akash Bose
Oran Akron
Jordan Crawford

Well deserve list of amazing peeps who we deeply admire and respect!

Giving away more goodies on the GTM AI Podcast, want it? Comment below.

Most GTM teams are drowning in research and prep work. The top 5%? They automated all of it and closed 3x more deals.

I just sat down with Dannielle Sakher (VP of GTM at MindStudio) who walked me through the exact AI agents she uses to close enterprise level partnerships.

Not theory. Not "use cases." Actual workflows running behind the scenes at one of the fastest-moving AI platforms right now.

What she showed me:
→ LinkedIn outreach that's personalized at scale (200+ quality requests per week vs. the typical 50) 
→ Pitch optimization that pulls live prospect research in real-time 
→ Meeting prep that collapses 3 hours of work into 2 minutes 
→ Contract review that flags every high-risk term without legal bottlenecks 
→ Launch teasers that make partnerships feel real before they exist

Here's what hit me hardest: Dannielle is automating the high-value stuff that made her successful in the first place.

The secret sauce activities most people stop doing when they get promoted because there's "no time." AND she puts in her own flavor and genius to take it to the next level.

Personalized research. Deep prep. Creative pitch assets. She built agents to do them at scale.

The result? More pipeline. Higher close rates. Zero sacrifice on quality.

Want the AI Agents she shows me for free?
-Like this post
--Comment, "MINDSTUDIO" and I will send you the newsletter and 5 AI agents she shows off and the podcast video walk through
---Repost cuz your Mamma said to help peeps like me

You can have these up and running and use them within 15 min, lets get er done!

So I partnered with HSE and we created THE 2026 Trends report, want it?

As of today, AI methodology revolves around defining the stages of its market entry, changes in value propositions, and the construction of a completely new architecture.

In 2026, AI capabilities are set to advance faster than the methodologies designed to govern them, creating a widening gap between technological potential and effective use.

I’m excited to share my perspective and partner with Julia Nimchinski and Hard Skill Exchange with Momentum.io on the report, “2026: In Pursuit of AI Methodology”, focused on the trends and predictions shaping AI-native GTM.

The report brings together 80+ contributors – CXOs, VCs, founders, and analysts – examining how AI is moving from experimentation to operating models.

The analysis reflects what is actually working as AI transitions from isolated pilots to embedded GTM systems.

Fourteen trends emerged:
-AI becomes a managed role (3.41%)
--Narrative + intent as the control plane (3.41%)
---Enablement as the agentic operating layer (7.95%)
----Systems of action replace systems of record (5.68%)
-----Orchestration re-centralizes control (5.68%)
------Operating models shift to live loops (10.23%)
-------Personalization moves from rules to reasoning (3.41%)
------Incumbents split: embed AI vs. rebuild (11.36%)
-----Buyer-side AI + AEO as a core discipline (11.36%)
----Trust, proof, and human connection increase in value (7.95%)
---ROI discipline tightens (11.36%)
--Generalists outperform narrow specialists (5.68%)
-Governance and data readiness become scaling gates (9.11%)
AI enters through high-friction insertion points (3.41%)

Want access for free?
-Like this post
--Comment "HSE"
---Repost cuz its fun

And I will send you the link to get the report FOR FREE.

Huge thank you to Julia Nimchinski for being an amazing partner last year and this one, this report is kick a!!

I stopped building workflows in Zapier and N8N.

Now I just describe what I want done. 🤯

This week on the GTM AI Podcast, my man Jonathan Moss aka JMo walked through a 5-Part AI Framework that's saving him 15+ hours a week.

Here's what he covers:

→ How to replace complex automation tools with simple prompts
→ The framework structure that gets consistent results
→ Real examples of workflows that used to take hours... now done in minutes
→ Why "knowing what's possible" is now the only skill that matters

And we're also giving you access to the NotebookLM guide that goes even deeper - it's a resource on how to actually use these tools for unique applications.

I'm genuinely proud of this one. It's practical, and you can start using it today.

Want the podcast link + the guide?
-Like this post
--Comment "JMo" below and I'll send you the goods.

Or get it here: https://lnkd.in/e_KVqn-h

Thank you as always!

Welcome as per usual to our weekly Podcast and newsletter! This week our own Jonathan Moss goes through a 5 part AI Framework using several easy to us

Open article: 1/16/26: The 5-Part AI Framework That Saves GTM Teams 15+ Hours Weekly by Jonathan M Kvarfordt, MBA • 6 min read

1/16/26: The 5-Part AI Framework That Saves GTM Teams 15+ Hours Weekly

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