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

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.

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

𝗪𝗲 𝗮𝗿𝗲 𝗵𝗶𝗿𝗶𝗻𝗴 𝗮 𝘁𝗲𝗮𝗺!
 
Care Agent just crossed that mark....1.5M+ 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, sales, RevOps, enablement, support, product, finance, 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

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.

$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?

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

Anthropic just open-sourced plugins for Cowork.
 
Most people will see this as a product update. It's not. It's the first real crack at solving the biggest problem in enterprise AI.
 
Here's the problem nobody talks about: every AI tool we use has context amnesia.
 
You spend 45 minutes teaching Claude your sales process on Monday. 

By Tuesday, it's forgotten everything. 

You re-explain your brand voice on Thursday. 

Gone by Friday. 

You're basically training a new employee who wipes their memory every night.
 
Plugins fix this at the architectural level.
 
You encode your workflows, your data sources, your slash commands—once. 

Then Claude operates like it already works at your company. Not as a chatbot. As a department-specific operator.
 
What this actually means for GTM teams:
 
→ Sales plugin connects to your CRM and runs your exact discovery framework. Every rep, every call prep, same intelligence.
 
→ Marketing plugin embeds your positioning and brand voice. Content comes out consistent without the 3-page style guide nobody reads.
 
→ RevOps plugin standardizes pipeline hygiene and reporting as executable commands anyone can run.
 
The real move? Building your own custom plugins. Anthropic says they're "easy to build, edit, and share" without deep technical expertise. 

Organization-wide sharing is coming in weeks.
 
Think about what that means. New hire installs the team plugin on day one. 

Immediately operates with the accumulated intelligence of the entire department. 

No 90-day ramp. No tribal knowledge dependency.
 
This is what AI-native architecture actually looks like—not adding AI to your existing process, but encoding your institutional knowledge into your AI infrastructure.
 
The companies that figure this out first don't just move faster. They compound advantages that become nearly impossible to replicate.
 
What department would you build a plugin for first? Curious where people see the highest leverage.

Dataminr

•Project Manager for automating ARR from an offline spreadsheet to using Salesforce CPQ and Snowflake/Looker.  Work includes leading bi-weekly working sessions across data analytics, FPA, and RevOps, and weekly updates to the CFO and SVP of RevOps.  Final product reduced 12 weeks’ worth of manual reporting annually, enhancements for CPQ including Renewal creation process, and restatement of methodology & results for the Board adhering to industry standards & SOX compliance to align ARR to revenue and service delivery. 

•Built a sales productivity model for existing business based on historical Net Dollar Retention (NRR).  Findings informed a structural change to the commissions plan, as well as 5 key levers with OKR goals to drive NRR ranging from “repricing” for higher ASP to off-cycle expansion.

•Owned the territory and quota model for Private Sector AMs & CSMs, 50+ heads, covering $48M+ of ARR and 700+ Accounts.  The model also leveraged the new automated ARR data model and created a bottoms-up NRR per territory to compare to tops down ARR quota.

Senior Manager, Sales Strategy

UiPath

•Lead the data & analytics strategy for Customer Success, Renewal, and Partner organizations.  Focused on data ingestion, creation, and delivery across each group and partnered with a respective Operations Director for each function to structure analytics deep dives and new initiatives. 

•Owned the redesign of Gainsight’s data architecture for objects, reports, and ETL-based rules during the mitigation to the new instance.  During the pre-mitigation work, led the impact analysis that allowed the team to deprecate 90%+ of the old instance, deliver the new platform 2-3 months early, and completed the project $1M below the original vendor professional services budget.

•Owned the coordination of data-related workstreams for the launch of the new Partner Program in FY’24.  Workstreams include the calculation of rebates and MDF across systems, revamping internal Power BI views for external groups via the Partner Portal, and sales re-segmentation to move accounts under “Hack Deck” to be owned by Partners vs UiPath sales reps.

Customer Analytics Manager

•Established PTC’s first Customer Success Analytics team under the Field Analytics COE.  Responsibilities included developing the overall Analytics development strategy & platform, supporting the CSM Leadership reporting cycle, and FY’2022 CSM Strategy & Planning Lead.  

•Managerial responsibilities included managing a global team of 3 analysts, with one based in Boston, and two in Bucharest, Romania.  The individual members' work ranged from CSM Dashboarding & Exec Presentation, full support of paid CS offering, and system enhancements.

•Launched a new agile approach to BI solution development for Customer Success leveraging Power BI resulting in 4 new highly used Dashboards within a period of 3 months.

•Identified and took lead for the rearchitected Gainsight’s data model, with a particular focus on account creation and ensuring ARR and product hierarchies matched corporate standards.

•Developed standardized reporting materials for interlocks between Customer Success Management and Product Organizations.  Materials ranged from customer insights on use cases across different product lines, NPS highlights, churn risks, and 6 quarter renewal rate projections. 

•Lead FY’2022 CSM Strategy & Planning with a focus on CSM coverage in relationship to Sales, Products, and the future of PTC’s Paid Customer Success offering.  A combination of modeling, comparison to Industry Benchmarking, and internal interviews resulted in a final recommendation to shift away from Product level to Account level coverage.  This resulted in a 200%+ increase of ARR/CSM, shifting from 35% to 90% of direct ARR covered, and the end of life of paid customer success offering.

Manager, Customer Success Analytics

VMware Carbon Black

Senior GTM Planning & Analytics Manager

•Security BU’s GTM Planning Lead for Customer Success, Channel & Dell Sales Organizations.  Responsibilities range from the creation of Quarterly Business Reviews, development of executive & rep level KPIs, Planning & Target setting, as well as various ad-hoc analysis.

•Built and owned modeling for the Security BU’s FY-2021 Renewal Bookings Operating plan totaling over $180M in Bookings.  Modeling inputs included historical renewal rates across Geos, Products, & Customer Spend, sensitivity modeling for new & Add-on business, as well as balancing in-year and out-year expiring Annual Recurring Revenue (ARR).

•Responsible for the Quarterly the Renewal Bookings & Renewal Rate forecasting.

•Launched self-service BI leveraging Power BI & Tableau for Customer Success to understand the distribution of Customer Health across the SBU full ARR portfolio, and the impact of the Top 100 Accounts.  This tool was leveraged for meetings between GTM & Product leadership.
  
•Constructed the first ever forecasting and reporting packages for Carbon Black’s OEM Dell On-The-Box business launched one month after acquired by VMware in October 2019.

•Coordinated launching customer usage metrics for Dell-On-Box customers.  The team included members of Product, Data Science, and GTM Operations.

•Assisted in modeling of transitioning Carbon Black’s Partner Program into VMware boarder program.  Provided special attention around Carbon Black’s Channel Lead Security routes-to-market; Managed Security Service Providers (MSSP) and Incident Response (IR). 

•Modeled new account assignment for Customer Success Managers (CSMs) based multi-variables, ranging for Sales segmentation, customer ARR, and corresponding channel partners.  Additionally scoped and modeled shifting sub $10K ARR accounts to a 3rd  vendor.  

•Responsible for the quota creation, design, and Callidus requirements for 100+ Sales Engineers, 30+ Channel Sales Reps, and 50+ Customer Success Managers.

GTM Planning & Analytics Manager

Rapid7

•	Designed and built a productivity and staffing model for the Global CSM Organization based on CSM to customer ratios. The customer ratios were based on a forecast of new customer acquisition as well as churn and resulted in the headcount recommendation of 40+ CSMs. 

•	Designed and built a customer level view of Rapid7’s Global Portfolio and leveraged the work to build 40+ CSM territories based on total Customer Annual Recurring Revenue and regional alignment.  Senior management also leveraged the model to redetermine team and reporting structures through the Global CSM Organization.  

•Managed the redesign of the Customer Success Compensation plan, shifting the plan from a Cohort Renewal Rate to a Renewal Annual Recurring Revenue (ARR) Dollar Target and Lead Generation Quota.  Presented my 2018 recommended changes to the Chief Operating Officer and coordinated with the Legal, Commissions and FP&A teams for the final implementation.    

•Modeled and owned the allocation of a $10 Million Closed Won ARR Lead Generation Quota across the Global CSM Organization based on CSM territories and historical contribution to closed won pipeline across Global Sales Channels. 

•	Own the distribution and ongoing maintenance of CSM territories and compensation plans.  My responsibilities range from developing and enforcing account transitions to assigning all net new accounts to the correct CSM based on the customers total ARR and location.

Senior Analyst - Customer Operations

LogMeIn

•Assisted with the development of the annual sales plan and strategy for global renewals including information such as pricing, territories, team structures, compensation plans, etc.

•	Regularly collaborate with teams from Sales, Marketing, and Corporate Strategy on ad hoc analysis, as well as long-term projects related to renewal bookings and customer retention.

•	Meet weekly with a database engineer to enhance Tableau Server Data Sources to increase the accuracy of Retention Reporting, as well as improve Retention self-service analytics across the company.  This work includes writing business requirements and assisting in regular data QA before live deployment.        

•	Owned and distributed a monthly presentation to the Executive Team that summarized and gave commentary on how Renewal Bookings and Retention Rates were tracking toward the plan, as well as highlights important trends.  

•	Developed an automated model using Tableau Desktop to determine which future renewals required coverage from a Renewal Rep based on multi-variables, ranging from full account spend, future re-pricing strategies, and potential up-sell opportunities upon renewal.  My model determined that across the global portfolio, there were 38K+ accounts worth an estimated $500M+ that met the assignment threshold for a Renewal Rep in 2017.

•	Built and balanced Renewal Territories for 35 Renewal Reps, ranging from the Major Reps to Specialized Product Reps and International Reps with language and market focuses.  

•	Used historical data to build an interactive model based on dollar and account churn to help determine the 2017 Pricing Strategy for the 21 different tiers of LogMeIn Central and five different packs of Pro Access.  This model and analysis were used to help forecast the combined goal for these product lines of $200M+ in bookings across all sales channels for 2017.

Renewal Operations & Strategy Analyst

Prudential Financial

•Use Excel and IBM Cognos to complete, review, and distribute daily, weekly, and quarterly reports on Prudential’s top credit and top counterparty exposures for Senior Management 

•Monitor daily compliance with single-issuer credit limits and perform the initial analysis via large Excel data sets 

•Regularly coordinate with Business Operations as well as Asset Management to better monitor, research, and resolve potential credit and asset class limit overages

•Contribute to Power Point presentations that outline the credit risk exposure for Prudential’s aggregate general account and each of Prudential’s major domestic subsidiaries for the Board of Directors as well as major credit rating agencies
 
•Create and update procedures for IRM’s daily, weekly, monthly, and quarterly reports

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