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?

Ryan Staley

I help Chief Revenue Officers & Sales Professionals Leverage AI to work 10 hours less weekly and 2x performance. Follow for cutting-edge cheat codes, prompts, and workflows🎙Scale Up Show Host🔥$0→$30M ARR in 5.5 yrs

Over the last decade, I’ve grown a division from 0-$30M ARR with 4 Sales People and have upskilled over 2,500 GTM executives on AI, Enterprise Sales and GTM Strategy  .

My mission is to inspire 1 million Go To Market Executives to work less and sell more through becoming Superhuman with Artificial Intelligence.

I help revenue leaders transform their teams by acquiring AI skills to escape the operational grind, and accelerate topline revenue.

I’m the founder of Whale Boss and The Scale Up Show.

My portfolio:

→ I’m the Founder of Whale Boss, an AI Sales & Marketing consulting and skill transformation company
→ I’m the Founder of the Scale Up Show, Top 1% Global Podcast
→ I teach Executives, Sales & Marketing teams to implement tools like ChatGPT, Copilot, Claude, etc.   
→ I provide Chief AI Officer Advisory services for human capital.  


My results:

→0-$30M without funding, marketing or SDRs with only 4 Sales people
→Identified and implemented over 500 AI use cases (internally and externally)
→Taught over 800 CROs proprietary selling strategy and tactical execution
→Consulted over 40 Technology companies on custom revenue growth strategies.  
→Voted as a TOP global AI thought leader, Gartner Ambassador & Sales Assembly thought leader. 


If you want to learn more on how to become Superhuman, you can join my newsletter.
⇒ https://superhumanrevenue.beehiiv.com/


If you want to explore AI skill transformation for your organization or Team, apply for an AI Consult.  
=> https://calendly.com/ryanstaleywss/introductory-call-with-ryan-staley-from-whale-boss


Youtube- https://www.youtube.com/channel/UCFGndSHN4KkE89kUP74U7Qg

Podcast-https://ryanstaley.io/podcast/

X-https://x.com/Ryan_Staley1

Podcast Host

I deconstruct how the top SAAS CEOs in the world grow their companies repeatedly and predictably.  

This show has featured Startup and Billion Dollar Founders, Best Selling Authors, and the World’s Top Sales and Marketing Experts like Terry Jones (Founder of Travelocity and Chairman of Kayak),  Andrew Gazdecki (Founder of Microacquire), Harpaul Sambhi (Founder of Magical with a previous exit to Linkedin) and many more.

I've spent 3 years helping executives navigate the fastest transformation in human history.

Here's what I've learned:


The ones who perform under pressure all have something in common.


A center. An anchor. 
Something they control when everything else is chaos.


For me? It's this. 5am. 
Weights. 
Silence before the storm.


This isn't a "wake up at 4am and crush it" motivational post.

It's reality:
→ My energy stays higher throughout the day
→ My decision-making stays sharper under pressure
→ I'm building health proactively instead of reacting to burnout


I've watched too many CROs and CMOs grind themselves into the ground chasing numbers.


Hit the target. Miss their health. Miss their families. Miss themselves.

The irony? When you lose your center, you lose your edge.


NOTE: This doesn't have to be the gym. 
It could be running. Meditation. 
Playing guitar. 
Coaching your kid's team.


The point is having SOMETHING that's yours. 


That doesn't depend on board approval, pipeline coverage, or the next AI announcement.


What's your anchor?

Founder & CEO, Whale Boss | Demystifying AI Agents | Helping revenue leaders 2x team results through AI Led GTM | Trusted by PE-backed SaaS | Transform your team in 60 days

I did something from Thanksgiving to New Year I haven't done in 5.5 years.
I stopped.

No podcasting. No YouTube. No content treadmill.
Instead:
→ Closed out the year strong
→ Set up massive foundational moves for 2026
→ Actually recharged with family in Napa-Russian River Valley
→ Did deep work when things got quiet

Here's what happened when I created space:

I spent serious time with Claude Code and orchestration workflows.

What I discovered completely rewired what I think is possible.

The jump I'm seeing in AI orchestration isn't incremental.
It's exponential intelligence at the speed of thought.

2026 is going to be different. Not because of hype.

Because of what I've built in the silence.

Sometimes the best move is to step back so you can leap forward.

What's one thing you stepped away from that gave you unexpected clarity?

No alternative text description for this image

15 hours in, three operating systems later, and I'm officially hijacked by Claude Code.

Not in the way you think.

Let's skip the AI hype and get real.

I stopped reading about "AI use cases" and got my hands dirty.

Fifteen solid hours, two weeks, zero fluffy prompts.

Just building actual operating systems.

Not content.

Not lists.

Real, working systems that actually run my business better.

Here's what happened:

1. The CEO Operating System (Annual Planning)

Forget hope and guesswork. This system turns chaos into a clear plan.
- Pick the few things that matter (not 27 "priorities")
- List what we're not doing
- Map risks and constraints
- Build a real 90-day action plan
- Log decisions, assumptions, and learnings as we go
- Use 10 battle-tested frameworks for tough calls

The result? Not a plan that "looks good" in a slide deck, but one that actually runs when the rubber meets the road.

2. The Sales Intelligence Operating System (Client Interaction Analysis at scale)

My new force multiplier.
It chews through a mountain of sales calls, client emails, and feedback in seconds, not weeks.
- Finds trends, patterns, and recurring objections
- Spots churn risk and expansion signals
- Surfaces the REAL words buyers are using, not what we wish they'd say
- Highlights opportunities we're missing

This thing can scrape thousands of data points and hand me something actionable before the coffee gets cold.

3. The Product Intelligence Operating System (Continuous Improvement)

Where delivery drifts, this system pulls it right back.
Because my edge is delivering actual AI transformation training, not just talking about it.
This OS hunts for patterns in:
- What features clients love (and which get ignored)
- What gets real results vs. wasted effort
- Where new modules, examples, or sequences are needed

It's the antidote to vibes-only product improvement. Decisions made on facts, not feelings.

Let's get honest about what made this work.

It wasn't "AI did it for me."

It was orchestration.

I skipped the mega-prompt lottery and ran swarms of specialized agents, each with a clear job.

Every OS had something even more important: memory. They don't just spit out answers and forget. They get smarter. They remember what works, what doesn't, why we made certain calls, and how we define "good."

Every week, outputs compound instead of vanish.

Here's your anti-hype PSA: it's not magic. If you feed it garbage, you get garbage,just a lot faster, and prettier.

The unlock isn't the tech.

It's having:

- Real-world skills 
- Guardrails
- Repeatable workflows
- And, most important, systems that learn instead of reset every time you open a new chat window

Thinking about breaking this down in detail on LinkedIn, YouTube, maybe even my podcast.

Would you want to see how each OS works under the hood, or should I just go back to writing about "top 5 AI tools for 2026" instead?

Drop a comment, tag someone who needs to see this, or just tell me if you want more.

undefined undefined

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Textblaze

whale-boss

We’ve trained 1,850+ sales and marketing professionals on AI in 2025.
Here are the 10 most jaw-dropping ROI outcomes they achieved:

(With real quotes from real people. No theory. Just results.)

#10: Personalized Demo Configuration
"Prospects are more engaged because demos are tailored to their exact industry and use case." — Account Executive, B2B SaaS
Result: 10%+ conversion lift → 500 more closes/year potential

#9: Executive Alignment Strategy
"We're getting meetings at higher levels than ever before. The AI research helps us speak their language." — Enterprise Account Manager, $4B BPO Company
Result: 20%+ larger deal sizes → $2M incremental revenue

#8: Shark Tank Hackathon Model
"Teams built actual tools they're still using. It wasn't just training—it was delivery." — Leadership Teams, Multiple Companies
Result: 100 tools built → 200 hrs/week saved → $520K/year

#7: ROI Builder for Champions
"Building a business case for my champion used to take half a day. Now I can generate one in 15 minutes." — Account Executive, B2B SaaS
Result: 94% time reduction → $225K/year saved

#6: Competitive Intelligence GEM
"Instead of spending hours every week tracking competitors, the GEM does it automatically and alerts me." — Sales Rep, Life Sciences Company
Result: 4+ hrs/week → Fully automated → $1.04M/year

#5: Prospect List Generation
"It took us days to build a prospect list. Now we generate 300+ qualified companies in minutes." — Sales Team, Life Sciences Company
Result: 95%+ time reduction → $480K/year saved

#4: Crawl-Walk-Run Framework
"Every department now has a structured AI project in flight. We went from pockets of use to organization-wide." — COO, Industrial Software Company
Result: Org-wide adoption → 500+ hrs/week saved → $1.3M/year

#3: Call Transcript Analysis
"I don't spend 30 minutes after each call reviewing notes. The AI extracts everything instantly." — Multiple Participants
Result: 30 min → Instant (100% automated) → $2.6M/year for a 100-person team

#2: Account Research Workflow
"I was spending 4 hours researching each strategic account. Now I get better insights in 30 minutes." — Marketing Specialist, B2B SaaS
Result: 87.5% time reduction → $1.75M/year saved

#1: Demo Prep Automation
"What used to take me 2+ hours now takes 10 minutes. I can prep 5 demos in the time it used to take for 1." — AE, B2B SaaS
Result: 92% time reduction → $1.37M/year saved

The math doesn't lie:
📊 Total Annual Impact: $14.21M (for a 100-person team) 
⏱️ Hours Recaptured: 5,935 hrs/week 
🎯 Sales drove 79% of total ROI

The bottom line:

AI transformation isn't about technology. It's about closing the deal with your own organization. 

The companies seeing these results treated internal AI adoption like an enterprise sale: → Identify the pain → Find champions → Build the business case → Close the deal

Your team already knows this framework. Now apply it internally.

Which of these outcomes would move the needle most for your team?
Drop a comment 👇

I just finished another AI Transformation Sprint with a SAAS leader-QT9 Software  software

The results blew me away.

Here's what 45 days of focused AI implementation produced:

→ 18 AI use cases deployed across Sales, Marketing, and Leadership
→ 139 hours saved WEEKLY across the team
→ Equivalent of 3.5 Headcount gain to drive greater efficiency and output
→ 12.4x return on program investment

But here's the metric that matters most:

Workflow confidence jumped 103%.

Before: 1.94/5
After: 3.94/5

This is the real unlock.

Most AI training programs teach tools. We transformed how the team THINKS about AI.

The 3 biggest wins:

🔹 Demo Prep: 2+ hours → 10 minutes (92% reduction)
🔹 Account Research:** 4 hours → 30 minutes (87.5% reduction)
🔹 Business Case Creation:** 4 hours → 15 minutes (94% reduction)

Here's what most companies get wrong:

They measure AI adoption by "ROI on Day 1 like a slot machine"
That's like measuring fitness by gym membership.

Leadership doesn’t set the non-negotiables

The real ROI comes from:

1. leading indicators=time
2. lagging indicators=increase in tasks, activity, pipeline , etc
3. Results= Increases in revenue and profit 

When confidence doubles, adoption becomes automatic.

NOTE: 

74% of the ROI came from SALES use cases. 

The Executive leadership was committed

-leading by example, setting expectations and driving adoption

The team was amazing

-hungry, curious and grew exponential by doing

What's blocking your team from this kind of transformation?

Feel stuck or don’t know where to start, DM me and I’ll give you a few ideas of what has been working over the last 3 years.

Amazing work Jim Ellward Jared Brady Brian Kulaga Cole Gardner, MBA Jake Ninowski Kertrina Justice Blake Carlson Christian Reyes 

And the rest of the QT9 team (who are not showing up in LI for me.)

I've presented growth strategies to C-Suites for the past 15 years.

Here's the brutal truth:

Most strategies die in the boardroom because they're built on gut feel and outdated playbooks.

The C-suite doesn't want more slides. They want conviction backed by data they can actually verify.

That's why I've spent the last 3 years building AI workflows that pressure-test strategy BEFORE it hits the executive table.

What used to take my team 3 weeks of analyst work now happens in 2 hours.

On January 28, I'm teaching this exact approach in Pavilion's AI in GTM School:

**Growth Strategy Design for an Enterprise (with AI Workflows)**

What you'll walk away with:
→ How to stress-test positioning against real market data using AI
→ Frameworks for validating competitive threats before they become surprises
→ The exact process I use to build strategies the C-suite actually trusts

This isn't theory. It's the same workflow I've used with 2,400+ executives over the past 3 years.

If you're a CRO or CMO tired of defending strategies built on hope, this session is for you.

Link in comments to register.

What's the last AI growth strategy you presented that got challenged in the boardroom?

This is taken from an AI Agent social network - Moltbook

It is the third most upvoted post and the first 2 aren’t any better.

Things are going to go off the rails really fast if someone doesn’t have some standards or safety measures to this site. 


Although this is an amazing concept and idea we need to make sure bad actors that have social burner accounts don’t poison the well.

Please comment for reach and let’s make sure we act smart when working with this insanely powerful technology.

WHAT keeps me up at night about AI in GTM:

The window is closing.

Right now, companies who implement AI systematically have a MASSIVE competitive advantage.

By 2027? AI capabilities will be table stakes.

Everyone will have access to the same tools.

The differentiator will be:
→ How fast you implemented
→ How deeply it's embedded in your operations
→ How skilled your team became during the transition

I've watched this pattern before.

Companies that moved early on CRM, marketing automation, sales engagement—they dominated for years.

The laggards spent the next decade catching up.

AI is that same moment. Compressed into 24 months.

This is why I partnered with Pavilion to teach a session for the **AI in GTM School**.

It's designed for GTM leaders who:
🔹 Know AI matters but don't have a systematic approach
🔹 Want alignment across teams, not scattered experiments
🔹 Need a 90-day plan they can actually execute

The next cohort starts next week.

If you're a CRO or CMO who's ready to move from AI experimentation to AI transformation—this is your moment.

Here is the link to sign up: 
https://lnkd.in/gekG_R6t

I spent 4 hours watching a single Anthropic video over break.

Rewound it. Took notes. Implemented immediately.

Here's the insight that's changing how I build AI systems:

STOP building agents. START building skills.

Here's what most people get wrong:

They think an "AI agent" is some magical autonomous entity that figures everything out.

Wrong.

Agents without skills are just expensive guessing machines with massive drift.

🔹 They go off path
🔹 They hallucinate next steps
🔹 They deliver inconsistent outputs

The Anthropic team broke it down into 3 layers that change everything:

**Layer 1: Foundational Skills**
The basics. File creation. Web search. Code execution.
Every agent needs these—but they're just the table stakes.

**Layer 2: General-Purpose Skills**
Document creation. Data analysis. Spreadsheet generation.
These are the workflows that apply across industries.

**Layer 3: Custom Enterprise Skills**
YOUR specific processes. YOUR playbooks. YOUR workflows.
This is where the magic happens.

The shift?

Instead of asking an agent to "figure it out," you're giving it a SKILL.md file that says: "Here's exactly how we do this."

No guessing. No drift. No inconsistent outputs.

NOTE: I've been implementing this for 2 weeks and the quality of outcomes is dramatically better across everything from content to research to data analysis.

The companies that master skills will have AI that actually performs.

The companies that keep building generic agents will keep wondering why their AI is inconsistent.

Which approach is your team using right now?

I just burned through 115 million tokens on Claude Code in 30 days.

That's not a typo.

I kept hitting usage limits and upgrading—which tells you something about how deep I went.

Here's the thing nobody talks about:

Building agents is EASY.

Building GOOD agents is hard.


You can spin up a swarm of AI agents in minutes. I've done it. Command it, and it creates.


But here's what happens next:


The output is a fraction of what you actually need.
It's the exact same pattern as Custom GPTs.
Anyone can build one in 60 seconds.
Almost no one builds one worth using.


The difference between "agent that exists" and "agent that performs"?
🔹 Rigor in testing
🔹 Quality examples baked in
🔹 Clear success criteria
🔹 Iteration cycles (lots of them)

After 30 days of intensive building, here's my take:


The barrier to entry has collapsed.
The barrier to excellence hasn't moved.


That gap is where the real opportunity lives for revenue leaders willing to put in the work.


What's been your experience building agents—easy to start, hard to finish?

McKinsey just announced they have 25,000 AI agents.

My reaction? That's not a flex. That's a warning sign.

Here's what I've learned from 3 years of AI itransformation implementations across startups and fortune 500s:

MORE IS NOT ALWAYS BETTER.

In fact, more is usually worse.
I've watched companies rush to build dozens of agents, custom GPTs, and AI workflows—only to end up with a graveyard of tools nobody uses.

The problem isn't building agents.

The problem is building agents without asking:
→ Who owns this?
→ Who maintains this?
→ Who actually uses this daily?

NOTE: Managing 25,000 agents currently is just as hard as managing 25,000 people.
Maybe harder.

Because agents don't tell you when they're broken.
They don't raise their hand when they're producing garbage.
They just keep running.

The companies winning with AI right now aren't the ones with the most agents.

They're the ones with the RIGHT agents.

Fewer. More precise. Actually used.

What's your take—is agent quantity becoming the new vanity metric?

I've been wrong about where the next AI breakthrough is coming from.
Prompting. Workflows. Automation.

But here's what I'm realizing now:

The real unlock isn't better prompts.

It's agentic tools like Claude Code.

I've been fine-tuning how I use it across everything I do—content, research, client work, operations.

And it's not incremental improvement.


It's a different category entirely.

So I'm going to start sharing what I learn with you in real-time.

Frameworks.
Cheat sheets.
What works. What doesn't.


No polish. No waiting until it's "perfect."

Because this is the bridge to the next exponential value creation with AI.

And I'd rather you learn from my mistakes now than figure it out alone 6 months from now.

Here is quick cheat sheet that will get you started on how to use it the right way!

What AI tools are you experimenting with right now that feel like a step-change, not just an improvement?

I spent 25 years learning how to build pipeline, increase close rates, and shorten sales cycles.

Then I asked an AI agent: "How would YOU solve these problems?"

The answer exposed a blind spot I didn't know I had.

Here's what I learned: We've been giving agents human playbooks and asking them to execute faster.

That's backwards.

Here are the 4 questions that changed how I work with agents in 2026:

1. "How would you approach this AS an agent—not as a faster human?"

Stop giving agents your methodology. Ask them what methodology makes sense given THEIR capabilities.

An agent doesn't need to "qualify" leads the way a human does. It can score 1,000 signals simultaneously. Let it design the process.

2. "Given these raw materials, what would you build?"

Feed it the data: transcripts, CRM records, emails, call recordings, Google Drive docs.

Then ask: "What system would you create to solve [goal]?"

You'll get architectures you never would have imagined.

3. "What patterns do you see that I can't?"

This is the unlock.

Humans are pattern-matching machines—but we max out at ~7 variables.

Agents can correlate hundreds. Ask them what YOU'RE missing.

4. "Using first principles, how would you solve this if you had no constraints?"

Agents don't carry your limiting beliefs, your "that's how it's always been done," your organizational politics.

Let them design from scratch. Then adapt to reality.

NOTE: These questions assume you've given the agent CONTEXT—not just instructions. Context is the fuel. Instructions are just steering.

The skill of 2026 isn't prompting.

It's knowing what to ask.

What question would you add to this list?

WHEN I started using AI 3 years ago, I focused on prompts.

HOW to write better questions.
HOW to get better outputs.
HOW to save time on individual tasks.

That approach 10x'd my productivity.

But what I discovered this week will 100x it.

**The shift: Prompt engineering → Orchestration engineering**

Here's the difference:

Prompt engineering is asking AI a great question.

Orchestration engineering is building SYSTEMS where AI holds your entire business context and synthesizes insights you'd never see on your own.

It's the difference between:

- Using GPS for one trip
- And having a co-pilot who knows every route you've ever taken, every destination you're considering, and every traffic pattern across all your priorities

I've been building what I call "operating systems" inside Claude Code:

A CEO OS that synthesizes my annual goals, quarterly priorities, and weekly rhythms.

The AI doesn't just answer questions.

It eliminates disparate information across areas.

It turns goals into action plans and tracking sheets.

It shows me where I'm off track before I even ask.

**Here's why this matters for YOUR career:**

The executives who master orchestration engineering won't just be more productive.

They'll have a STRATEGIC ADVANTAGE that compounds every single day.

While others are still asking ChatGPT individual questions...

You'll have an intelligent layer synthesizing every:

- Sales call
- Customer interaction
- Internal communication
- Market signal
- Team execution detail

Against your strategic priorities.

In real time.

NOTE: I predict "Orchestration Engineer" will be one of the most valuable skill sets in business by 2027.

It won't require you to be technical.

It will require you to think in SYSTEMS, not prompts.

The 10x knowledge workers of the next decade won't be the best prompt writers.

They'll be the best orchestration architects.

This is the fastest transformation mankind has ever had.

And the window to get ahead?

It's closing faster than most people realize.

What's the first "operating system" you'd build for your role if you could connect all your disparate information in one intelligent layer?

I've spent the last 3 weeks building what I call an "Agentic Operating System" for my company.

Here's the breakthrough:

What if your AI didn't just answer questions...

But held the ENTIRE CONTEXT of how your company operates—and helped you see what you're missing?

This is orchestration engineering.

And it's the skill that will separate 10x knowledge workers from everyone else.

Here's how I'm building it:

**5 interconnected "Operating Systems" that form a company brain:**

1️⃣ **CEO/Executive OS**

- Annual goals
- Quarterly priorities
- Weekly operating rhythm
- Action plans with tracking

2️⃣ **Sales Intelligence OS**

- Pipeline analysis
- Call transcriptions → next best actions
- Executive overviews
- Hidden opportunities across deals

3️⃣ **Product Intelligence OS**

- Training transcripts
- Customer feedback loops
- Win/loss analysis
- Engagement and expansion signals

4️⃣ **Marketing OS**

- Campaign performance
- Content byproducts
- Dream 100 prospecting
- Competitive intelligence

5️⃣ **Internal Operations OS**

- Slack messages
- Email threads
- Meeting transcriptions
- All aligned to strategic goals

**The magic happens when they INTERCONNECT.**

🔹 Your sales calls inform product improvements
🔹 Your product intelligence informs marketing messaging
🔹 Your internal operations stay aligned to executive priorities
🔹 Every signal gets processed against your strategic goals

This creates what I'm calling the "company brain."

It helps you see:

- If you're on track
- What you're doing right
- How to optimize
- 10x opportunities hiding in plain sight
- 100x possibilities you never considered

NOTE: I used to think the future was individual AI agents doing tasks.

Now I realize it's ORCHESTRATED SYSTEMS where agents work together with shared context.

That's the 10x multiplier.

The Leaders who build this capability in 2025?

They won't just outperform their peers.

They'll operate in a different dimension entirely.

Which of these 5 operating systems would unlock the most value for your team?

I'm in this photo with the cast of Severance.

Except I've never met them.

I strung together a couple commands in ChatGPT's new image generator and this was the result.

2026 is going to be WEIRD.

Here's what I'm seeing after 3 years of AI implementation work:

The line between real and synthetic is disappearing faster than anyone predicted.

🔹 Veo 3.1 just dropped
🔹 Image generation hit a new threshold
🔹 Video is about to follow

And we're only in January.

NOTE: This isn't fear-mongering. This is pattern recognition.

When you can't trust what you see online, something else becomes infinitely more valuable:

Human-to-human connection.

Not text. Not email. Not async.

→ Live conversations
→ Phone calls
→ In-person presence

These will be at a PREMIUM in 2026.

The irony? The more powerful AI becomes at mimicking reality, the more we'll crave what's actually real.

What's your prediction for how this plays out?

I just looked at my ChatGPT annual summary.
I don't know if I'm excited or disturbed.

The data showed me exactly how much of my thinking, creating, and problem-solving now runs through AI. Every day. 

Multiple times per day.


Three years ago, I had my "ChatGPT moment" that changed everything.

What I'm seeing now makes that look like a warmup.

The agents. The systems. The orchestration capabilities.


My mind is genuinely blown.


Here's what I've accepted: This is short-term sacrifice for long-term gain.

The hours I'm investing now in understanding these systems? 
They're not optional anymore.
They're the price of admission for what's coming.


I've worked with 2,600+ executives over the past three years. 

The ones who leaned in early are operating at a completely different level than those who waited.

That gap is about to become a canyon.


What did your AI usage summary reveal about how you're working now vs. a year ago?

HOW would an AI agent approach pipeline differently than your sales team?

I ran the experiment.

I asked an agent to solve evergreen pipeline problems that every sales org faces. But I didn't just give it the task—I asked it to map out:

→ Its approach
→ The steps it would take
→ How it would "attack" the problem

Here's what I learned:

**What impressed me:**

🔹 The level of detail was exceptional
🔹 It viewed pipeline stages through a different lens than most humans
🔹 It identified optimization points I hadn't considered

**What missed the mark:**

🔹 Its assessment of how humans handle pipeline undersold us
🔹 It didn't account for relationship nuance that top GTM pros leverage
🔹 It assumed more process consistency than exists in real orgs

NOTE: This isn't about agents being better or worse than humans.

It's about understanding WHERE each excels.

The takeaway for revenue leaders:

Before you deploy an agent on any workflow, ask it to explain its approach first. Compare it to your team's approach. The gaps tell you exactly where human oversight matters most.

This is how you build agent + human workflows that actually work.

What's the first sales problem you'd ask an agent to map out? Drop it below—I'll share what I've seen work.

What if cheat sheets/frameworks are actually holding you back?

I've taught thousands of executives about AI.

Frameworks everywhere.

Slides. Decks. Cheat sheets.


And here's what I've realized after 3 years of implementation work:

Frameworks are the START.
Systems are the SCALE.


Last week I built something I'm calling Atomic Claude Code.


It takes James Clear's Atomic Habits—one of the best frameworks ever created—and turns it into a living, breathing agentic system inside Claude Code.


Not a document I reference.
Not a poster on my wall.
Not a "note to self."


An actual system that:
→ Tracks my 1% daily improvements
→ Compounds learning over time
→ Holds me accountable without willpower


Here's the honest truth:
I've been teaching AI transformation for 3 years.
But the biggest unlock in the last 90 days?
Realizing I can BUILD the frameworks I teach INTO the AI systems I use.


The framework becomes the infrastructure.
The concept becomes the code.
The theory becomes the transformation.


NOTE: We're at an inflection point where the gap between "people who know frameworks" and "people who implement frameworks as systems" will determine who wins the next 12 months.


So here's my question:

Is this crossover from frameworks to agentic implementations something you're interested in?


Something you've already done?


Or something you're waiting to explore?


Drop your answer below—I'm genuinely curious where you are in this journey.

I've never written a line of code in my life. 

30 days ago, I went all-in on Claude Code—a coding platform.

Here's what I learned as a complete non-coder doing extensive coding for the first time:

Orchestration platforms like Claude Code require you to master 4 things in a specific sequence:

1. Foundational Concepts First

There's a language barrier that has nothing to do with code.

It's like learning to ice skate. You need to understand the lingo, the logic, how things connect.

Skip this step? You'll be frustrated within 48 hours.



2. Learn By Doing (After Foundations)

Once you understand the vocabulary, execution becomes your teacher.

I built. I broke things. I rebuilt.

No course can replace actually shipping something—even if it's messy.



3. Return to Foundations

This is the step 95% of people skip.

After building, go BACK to foundational concepts.

You'll discover entire categories you missed:
→ Continuous automation
→ Skills architecture
→ Edge cases you never considered



4. Context Engineering

This might sound like a buzzword.

It's not.

If you align context properly, prompting becomes almost effortless.

It unlocks capabilities you didn't think were possible.

NOTE: Context engineering isn't prompt engineering. It's the architecture that makes prompts work.

The sequence matters: Foundations → Doing → Foundations Again → Context Architecture.

Miss any step and you'll plateau fast.


What step are you currently stuck on?

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