Mark Kosoglow

Chief Revenue Officer (CRO)

Are your customers your growth engine? They should be.

As a teenager, watching 14 videos in the back storeroom on a 7" black and white TV to learn how to sell shoes at the mall was a great foundation. Running a small business with 200+ employees showed me how to be organized. Creating a highly profitable sales territory from one dead for a decade was hard work. Managing 12 salespeople across 9 states cemented my sales philosophy. Getting one of the fastest growing SaaS companies in history to $250M+ in revenue taught me how to balance scaling and delivering a world class customer experience.

Working with a customer-centric team with, by far, the best, smartest, hardest working people I've ever worked with....well, that's an honor and privilege I get to enjoy everyday.

CFO - Chief Fun Officer

Doing dad stuff, exploring interesting and fun opportunities...cookin' up something SPICY!

I love coaching reps. Quick debrief after a call? Yes please!
But, it always felt a bit random, which makes me feel I'm missing something.

What's worse is coaching is the most high value activity managers do.

I tried to use call scorecards several times, but they never lasted long.

Create a rubric, listen to the call, score the call on the rubric, review with rep.

Sounds so easy and organized, BUT it never worked. WHY?

1. Rubrics are hard to create →

How many items is too many? Not enough? What’s most important? How do we make this complete but not so cumbersome it’s a pain to do? What did we forget? When was the last time this was reviewed? Did we launch anything that impacts the rubric?

2. Rubrics are hard to score →

They are often subjective (one manager thinks it’s a 5, but another give is a 3). 

There are also “roll ups” of things that were dynamic through the call. Example: How was the rep’s tone in the call? What happens if it was bad but got better?

3. Too many calls, too much time →

If a manager has 8 AEs, and each AE has an an average of 15 calls per week. 
Team has 120 total meetings.
Each call is 30 mins of average.
A manager has 60 hrs of calls (30 hrs if you run on 2x).
There are discos, demos, negotiations, onboarding, roadmap calls.
So, 30 hrs of 6-8 diff types of calls to score.
Got different regions and segments and it gets worse quickly.

4. Hard to sustain →

Sure, you can do a couple call coaching blitzes, but over time, I've found getting managers to do this consistently and thoroughly never lasts. 

I finally gave up on scorecards.



Then, came AI. The always-on manager that can score anything.

I recently saw Glyphic has AI scorecards.

The AI looks at every call, scores it, creates rep insights.

This is what sales management was always supposed to be, but it never got there.

If the future of buying is a personalize, relevant, self-paced engagement that teaches the buyer everything they need to make the best decisions, then the future of selling is a personalized, relevant, self-paced engagement that coaches the seller on everything they need to help buyers make the best decisions.

You couldn’t do this.

It was impossible...took too long, too subjective, too random.

Now, you can.

If you stop learning as a rep, that’s on you.

If you aren’t being coached by a manager, that’s on them.

Will there be some wrong advise and scoring? Sure.

But, the consistency in it being down and how it is being done outweighs that.

We should #neverstoplearning.

This is a perfect application of AI to overcome a human problem.

Everyone has AI. Humans are the differentiators.

Jonathan Moss

Experity

Leading a team of senior Growth, GTM and Data leaders focused on establishing new growth vectors, building go-to-market strategies for the established and new business, and optimizing the system (data, process, and technology) that underpins the revenue machine. 

2024 results: 
↳ Grew top and bottom line by double digit % (first time in since 2021)
↳ Exceeded annual plan in revenue and EBITDA 
↳ CAC payback <12 months 
↳ LTV/CAC 5:1 
↳ Increased volume and conversion rates at every stage of the bowtie while decreasing sales cycle time across all product and service mixes 
↳ Reduced time to install by 2.5x 
↳ Reduced churn by 50%
↳ Launched 2 new revenue streams 

My teams are responsible for the following: 

Growth: establish growth vectors (e.g. adjacencies, new categories, product innovation), build out a 0 to 1 business within our incubator, and launch go-to-market 

Go-to-Market Strategy & Planning: planning and execution to accelerate durable growth across our platform of products/services 

GTM Operations: building, managing, and optimizing our GTM system that underpins our revenue factory. 

AI: building and implementing AI strategies across the GTM team, product and service lines. 

Data: structuring, aggregating, analyzing and providing insights for the GTM teams that drive our strategic decisions and operations. 

Revenue Enablement: accelerating employee productivity and knowledge across all GTM teams.

EVP - Growth, GTM Strategy and Operations

Established and lead the Executive AI committee comprising CTO, SVP of Strategic Partnerships, Chief Medical Officer and SVP of Technology. 

Implemented 5 AI initiatives 
2024/2025 roadmap for 10+ initiatives

Executive Chair - AI committee

OpenAI

Forum Member

Pavilion

Co-Chair AI in GTM

Executive Member

Winning by Design

Revenue Architect & GTM Ambassador

AI Business Network

Co-Founder

MetaVRse

✅ 140+ Metaverse / Web3 Products and Projects 

🌎 9 World-Firsts

💻 1 Award-Winning, Proprietary 3D Creation Platform (Metaverse, Web3)

👩‍💻60k+ Creators

♟️Established strategic partnership with Microsoft

Founding Team, Executive Board Member & Strategic Advisor

Led global vision, strategy, and execution for all facets of revenue generation, go-to-market, operations, customer success and product management within a high growth technology industry.

🚀Launched the Metavrse Engine from stealth April 2020 including SaaS and professional services. 

✍️Negotiated and Signed deals with Siemens Healthineers, Samsung, MasterCard, JP Morgan and other Fortune 500 companies. 

📚$2M+ booked revenue in the first 12 months. 

♟️Established strategic partnerships including NVIDIA, Qualcomm, EPIC Games, Meta (formerly Facebook), and Autodesk. 

🛣️Developed product roadmap with feature prioritization, UX/UI design, and technical integrations.

Booster

Booster is a clean technology company that provides mobile energy delivery, including renewable and sustainable fuels, through its marketplace. 

🛠️ Elevated go-to-market strategy, built the revenue engine and led sales team that secured $56M in bookings with 79% net revenue growth and 215% Enterprise segment growth

⚙️ Implemented sales motions with aligned systems across the customer journey, automated workflows and insights that led to a 60% more outbound lead volume, 2.5x meetings, and 4.6x opportunity creation, 56 day reduction in sales cycle time, 25% conversion rates and a 45% increase in deal size

✅ Transformed top of funnel strategy through diversification of marketing channels and building infrastructure which led to 10x inbound lead volume accounting for 30% of new business, up from 2%

🚀 Implemented Customer Led Growth strategy that led to a better customer experience, 2x faster growth and improved LTV/CAC

🤝 Series C ➡️ D Senior Leader

🎯 Built annual operating plan and forecasts (quarterly, monthly, weekly) with 96%+ accuracy  

🎙️Built brand and content strategies that elevated Booster to over 60%+ Share of Voice and 90%+ positive Brand Sentiment

🚀 Launched platform business (including a marketplace) with complete P&L ownership

📈Grew platform from $0 to $6M+ in ARR since launching in December 2022

🔋Built partnerships that enabled Infrastructure as a Service for EV Charging, Hydrogen, LNG, and CNG delivery 

🚚 Drove product roadmap strategy that allowed for mixed energy fleets to manage energy consumption, vehicles insights and payments for both sides of the marketplace 

📍Developed geo-spatial models and algorithms for route and location based density to enable effective dynamic pricing, sales and marketing prioritization

💰Developed pricing strategy transitioning legacy margin model to a product (SaaS) and services (Consumption) model

Vice President - Revenue & Revenue Operations

REEF

REEF is a technology company that transforms urban real estate into thriving mobility and logistical hubs via a marketplace.

🎯 Led GTM strategy and execution across sales, revenue operations, and customer success that secured $70M in new bookings and expansion that increased revenue from $30M to $100M+ throughout the United States, Canada, and Europe.

⚙️ Implemented sales process with aligned systems across the customer journey, automated workflows and insights that led to a 36 day reduction in sales cycle time, 40% increase in conversion rates and a 25% increase in deal size.

📱Developed and implemented product led growth strategies that led to a 3x revenue growth expansion from the demand side of the marketplace.

⛓️Interconnected and consolidated technology stack to increase efficiency and efficacy across marketing, sales, and customer success (Salesforce, ZoomInfo, Marketo, LinkedIn Sales Navigator).

Executive Vice President - Revenue & Global Revenue Operations

OYO USA

OYO is a global travel technology company that connects guests to owner-operators via a marketplace. It offers full-stack hospitality software and revenue management solutions to hotel owner-operators.

🎯 Led strategy and execution for go-to-market, revenue operations, and sales training that led to growth from $17M to $200M throughout the United States, Canada, and India.

📈Developed and implemented product led growth strategies that led to a 2x revenue growth and 3x margin expansion from the demand side of the marketplace.

🛣️ Create and maintain data and insights across the entire customer journey that increased direct sales close rate by 2.5x, increased deal size by 50% and decrease sales cycle by 45 days.

⛓️ Interconnected and built the sales tech stack to align with the customer journey and automated work flows to drive 2x sales efficiency and efficacy.

Vice President Revenue Operations & Enablement

T-Mobile

A demonstrated record of success and achievement, marked by a promotion to a position of increased influence, authority, and accountability. Fulfilled a critical role building and executing strategies to govern sales, communication, and training professionals in franchise stores, small business team, national retail team, and in Direct to Consumer teams encompassing over 40,000 individuals responsible for 80% of all revenue. Maintained accountability for $25M in P&L.

•Supervised a 190-person team engaged in sales enablement, learning (e.g. in-person, virtual, eLearning, and AR/VR), strategy/planning, sales tech stack, process design, LMS, other Learning technology and communication tools. 

•Recognized for Performance (Top 5%)

•Successfully accelerated the ramp-up period for new sales personnel from six months to one month, as measured by five key performance metrics - New Customer Acquistion, Renewal Rate, Average Revenue Per User, Value Added Service Attach Rate, and Emerging Product Attach Rate.  

•Positioned the company to be listed in the Great Places to Work list for 2018 to 2019.

•Sparked a 25% increase in revenue per subscriber as a result of training programs for existing sales representatives, improving their ability to promote and secure agreements for products, services, and accessories.

•Developed quantitative and qualitative analytics and dashboards to measure sales intelligence, performance, deal/funnel intelligence, and trainer performance across 40,000 employees. 

•Scope of training curricula and collateral spanned New Hire, Management Fundamentals, Advanced Leadership Programs, Behavioral Skills Workshops, Gamification, and over 250 sales and leadership videos.

•Recognized as the first training team in the world that formed a co-development partnership with the Apple Master’s Program.

Senior Director Sales Enablement | Learning

Responsible for operations encompassing 15,000 employees and 8,000 points of distribution across 22 states, representing $14B in revenue. Directed teams engaged in Go-To-Market strategy, Communications, Retail Technologies, Customer Support & Success, Performance/Competitive Analytics, Workforce Planning, Facilities and Distribution strategy.

•Developed operational and customer success strategies that led to $825MM increase in profit and 2,100 basis points in NPS.

•Executed over 20 go-to-market projects with budgets in excess of $10MM.

•Led Go-To-Market and Operational implementation of new channels including Direct to Consumer, Uber, Walgreens, and AAA. 

•Managed $1B+ budgets for labor, facilities, service and repair centers, new store builds, remodels, and equipment.

•Developed automated dashboards for 40+ operational and customer experience metrics with tangible costs and actions. 

•Implemented a variety of technology solutions to assist the sales, operations and customer support teams, including intelligent process automation, digital chatbots, Microsoft Office 365, Salesforce, and a number of SaaS tools.

Director Multi-Channel Sales Operations

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

You know how companies either burn a ton of cash trying to scale or hit a growth ceiling they just can’t break through?

What I do is step in, audit their GTM strategy, financials, and operations, and then engineer a smarter, more scalable system (typically including AI). Whether that’s digitizing their physical business, refining their go-to-market motion, or unlocking new revenue streams, I build the strategies and systems that fuel sustainable, high-impact growth.

Companies I’ve worked with have seen a median growth rate of 87%

My journey has given me diverse experiences across many different industries, business models, products, and functions at different scales. 

➡️ Experience across multiple industries including Software (SaaS), Healthcare, Retail, Real Estate, Hospitality, Transportation/Logistics, Telecommunications. 

🚀 Company Stages: Venture Capital, Private Equity, and Bootstrapped at Pre-Revenue, Seed, Series A, Series C, Series D, Publicly traded 

💵 Investment Partners: Sequoia, Lightspeed, Madrona, GTCR, Warburg Pincus, Mubadala Capital, AirBnB, Microsoft, JP Morgan, UBS Asset Management, Softbank and JC2 Ventures 

💰Company Revenue: $0-$15B (87% median growth rate; over $1B of capital raised)
     👉 $0-$6M+
     👉 $0-$7M+
     👉 $17M-$200M+
     👉 $30M-$100M+
     👉 $80M-$150M+
     👉 $165M-$260M+
     👉 $190M-$2XX (Current role)
     👉 $13B-$15B+

📍Geographies: North America, LATAM, EMEA, and APAC

✅ Team Size: 1-200

🤓 Experience
👉 AI: 6+ years     
👉 GTM: 20+ years
     👉 Sales: 12 years (B2B); 10 years (B2C)
     👉 Revenue Operations: 13 years
     👉 Customer Success / AM: 9 years
     👉 Product: 9 years
     👉 Revenue / Sales Enablement: 5 years
     👉 Marketing / Demand Generation: 3 years
     👉 Business Development/Partnerships: 3 years

𝟲𝟬𝟬,𝟬𝟬𝟬 𝗽𝗮𝘁𝗶𝗲𝗻𝘁𝘀. 𝟮,𝟬𝟬𝟬 𝗰𝗹𝗶𝗻𝗶𝗰𝘀. 𝗢𝗻𝗲 𝗯𝗿𝗼𝗸𝗲𝗻 𝗲𝘅𝗽𝗲𝗿𝗶𝗲𝗻𝗰𝗲 𝗰𝗼𝘀𝘁𝗶𝗻𝗴 𝗵𝗲𝗮𝗹𝘁𝗵𝗰𝗮𝗿𝗲 𝗯𝗶𝗹𝗹𝗶𝗼𝗻𝘀.
 
We are fixing it with AI agents and AWS quietly handed us the infrastructure to scale it industry-wide.
 
Here's what nobody talks about: Healthcare AI isn't about replacing clinicians. It's about eliminating the administrative sludge drowning them.

In the same way GTM isn't about replacing people. Its about eliminating the drag that keeps people from serving customers or showing the impact with potential buyers.
 
Everyone's building AI copilots. We went after something bigger: 𝘁𝗵𝗲 𝟲𝟬% 𝗼𝗳 𝘀𝘁𝗮𝗳𝗳 𝘁𝗶𝗺𝗲 𝗹𝗼𝘀𝘁 𝘁𝗼 𝗽𝗵𝗼𝗻𝗲 𝘁𝗮𝗴, 𝗺𝗮𝗻𝘂𝗮𝗹 𝘄𝗼𝗿𝗸𝗳𝗹𝗼𝘄𝘀, 𝗮𝗻𝗱 𝗽𝗿𝗶𝗻𝘁𝗶𝗻𝗴 𝗽𝗮𝗽𝗲𝗿.
 
The pattern I kept seeing at Experity:
 
7,000 locations. 40M patient visits annually. Same issues everywhere:
 
→ Patients repeating information across five systems 
→ Staff buried in admin work instead of care 
→ Turnover from burnout, not capability 
→ Clinics losing time, revenue, trust
 
When the market leader sees this across 7,000 locations? It's not a local problem. It's an industry problem.
 
𝗪𝗲 𝗯𝘂𝗶𝗹𝘁 𝗳𝗿𝗼𝗺 𝘁𝗵𝗲 𝗽𝗮𝘁𝗶𝗲𝗻𝘁 𝗯𝗮𝗰𝗸𝘄𝗮𝗿𝗱𝘀.
 
Mapped every friction point. Built agentic workflows that actually solve problems.
 
The result? 𝗖𝗮𝗿𝗲𝗔𝗴𝗲𝗻𝘁 — a 24/7 AI patient concierge on Amazon Web Services (AWS).
 
Multi-agent orchestration: 
→ LLM-powered conversational AI 
→ Dual-channel (SMS + web) — no apps, no passwords 
→ Secure identity verification 
→ Deep integration into clinical + billing systems
 
One tap. Everything: discharge, labs, follow-ups, payments, nudges.
 
𝗢𝗻𝗲 𝗺𝗼𝗻𝘁𝗵. 𝗥𝗲𝗮𝗹 𝗻𝘂𝗺𝗯𝗲𝗿𝘀:
 
→ 600K patients, 2,000 clinics 
→ Zero app downloads 
→ Existing staff 2M+ automated messages → Millions of pages gone

 
𝗪𝗵𝘆 𝗔𝗪𝗦 𝗯𝗲𝗰𝗮𝗺𝗲 𝗼𝘂𝗿 𝘂𝗻𝗳𝗮𝗶𝗿 𝗮𝗱𝘃𝗮𝗻𝘁𝗮𝗴𝗲:
 
Most healthcare AI hits a wall at scale. Compliance kills speed.
 
AWS gave us: 
→ Production-grade agentic AI (not LLMs in a wrapper) 
→ Healthcare compliance baked in 
→ Agent orchestration at enterprise scale 
→ Collaboration treating us like co-builders
 
We shipped in months.
 
𝗧𝗵𝗲 𝗚𝗧𝗠 𝗹𝗲𝘀𝘀𝗼𝗻 𝗲𝘃𝗲𝗿𝘆𝗼𝗻𝗲'𝘀 𝗺𝗶𝘀𝘀𝗶𝗻𝗴:
 
This isn't just healthcare transformation. It's what's coming for every B2B business:
 
→ Agent orchestration replacing linear workflows 
→ AI-native products vs. bolted-on features 
→ Infrastructure partnerships as moats 
→ Scale through intelligence, not headcount
 
The Winning by Design and Jacco van der Kooij research nailed it: We're moving from "AI-assisted" to "AI-native."
 
That gap is about to become a chasm.
 
𝗪𝗵𝗮𝘁'𝘀 𝗻𝗲𝘅𝘁:

 Make healthcare feel like the rest of your digital life.
 
AWS is building the rails. We're building the experience.
 
And yes... I traded makeup for keeping the hat on! That is my signature, it ain't coming off!

𝟳𝟮% 𝗼𝗳 𝗕𝟮𝗕 𝗯𝘂𝘆𝗲𝗿𝘀 𝗽𝗿𝗲𝗳𝗲𝗿 𝗮 𝗿𝗲𝗽-𝗳𝗿𝗲𝗲 𝗲𝘅𝗽𝗲𝗿𝗶𝗲𝗻𝗰𝗲.
 
Yet 95% of vendors still force buyers through rep-led qualification calls.
 
We're watching the biggest disconnect in B2B history unfold in real-time.
 
Here's what nobody's saying about the buyer experience revolution:
 
𝗧𝗵𝗲 𝗢𝗹𝗱 𝗪𝗮𝘆 (𝘀𝘁𝗶𝗹𝗹 𝗵𝗮𝗽𝗽𝗲𝗻𝗶𝗻𝗴 𝗮𝘁 𝗺𝗼𝘀𝘁 𝗰𝗼𝗺𝗽𝗮𝗻𝗶𝗲𝘀): 
→ Buyer visits your website at 11pm 
→ Fills out a form and waits 
→ Gets called 3 days later during a meeting 
→ Sits through a discovery call repeating everything from their form 
→ Waits another week for pricing 
→ Deal dies from friction
 
𝗪𝗵𝗮𝘁 𝗯𝘂𝘆𝗲𝗿𝘀 𝗮𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝘄𝗮𝗻𝘁: 
→ Instant answers when THEY'RE ready 
→ Self-service that doesn't feel like abandonment 
→ Human help available, not mandatory 
→ Buying on their timeline, not yours
 
Buyers are done being held hostage by our sales processes. 6sense 2025 Buyer Experience report aligns with that. 
 
But here's where it gets interesting...
 
𝗠𝗼𝘀𝘁 𝗰𝗼𝗺𝗽𝗮𝗻𝗶𝗲𝘀 𝗮𝗿𝗲 𝗿𝗲𝘀𝗽𝗼𝗻𝗱𝗶𝗻𝗴 𝗯𝘆 𝗮𝗱𝗱𝗶𝗻𝗴 𝗰𝗵𝗮𝘁𝗯𝗼𝘁𝘀 𝘁𝗵𝗮𝘁 𝗷𝘂𝘀𝘁 𝗳𝗿𝘂𝘀𝘁𝗿𝗮𝘁𝗲 𝗯𝘂𝘆𝗲𝗿𝘀 𝗳𝗮𝘀𝘁𝗲𝗿.
 
Companies like 1mind are taking a completely different approach: AI agents that actually KNOW your product, understand context, and can have real conversations 24/7.
 
Not "click these buttons" chatbots. 

Not "let me connect you to sales" dead ends. 

Real agents that can qualify, educate, and even close deals while your team sleeps.
 
The mind-bending part? These agents are becoming BETTER at discovery than most reps because they: 
→ Remember every conversation across every buyer 
→ Never forget to ask the impact questions 
→ Always have the right case study ready 
→ Scale infinitely without quality degradation
 
𝗪𝗲'𝗿𝗲 𝗻𝗼𝘁 𝗿𝗲𝗽𝗹𝗮𝗰𝗶𝗻𝗴 𝘀𝗲𝗹𝗹𝗲𝗿𝘀. 

𝗪𝗲'𝗿𝗲 𝗿𝗲𝗺𝗼𝘃𝗶𝗻𝗴 𝘁𝗵𝗲 𝗮𝗿𝘁𝗶𝗳𝗶𝗰𝗶𝗮𝗹 𝗴𝗮𝘁𝗲𝘀 𝘄𝗲 𝗯𝘂𝗶𝗹𝘁 𝗯𝗲𝘁𝘄𝗲𝗲𝗻 𝗯𝘂𝘆𝗲𝗿𝘀 𝗮𝗻𝗱 𝘃𝗮𝗹𝘂𝗲.
 
Think about it: the buyer experience transformation isn't about automation.
 
It's about respect.
 
Respecting that buyers: 
→ Do research at midnight 
→ Want answers immediately 
→ Prefer self-service until they need you 
→ Will choose vendors who meet them where they are
 
The companies winning aren't the ones with the best sales teams.
 
They're the ones who let buyers buy how they want to buy.
 
The future isn't "AI or humans."
 
It's AI handling the 80% buyers want to do alone, so humans can focus on the 20% that actually requires judgment, creativity, and relationship building.
 
If buyers prefer rep-free experiences but still need help making complex decisions, what does that tell us about how we should be deploying AI?
 
Drop your takes below 👇

I am excited to be a Founding Member of the Growth Institute at Winning by Design!

𝗚𝗿𝗼𝘄𝘁𝗵 𝗜𝗻𝘀𝘁𝗶𝘁𝘂𝘁𝗲 𝘁𝗿𝗮𝗶𝗻𝘀 𝗿𝗲𝘃𝗲𝗻𝘂𝗲 𝗹𝗲𝗮𝗱𝗲𝗿𝘀 𝘁𝗼 𝗯𝘂𝗶𝗹𝗱 𝗿𝗲𝗽𝗲𝗮𝘁𝗮𝗯𝗹𝗲 𝘀𝘆𝘀𝘁𝗲𝗺𝘀 𝗶𝗻𝘀𝘁𝗲𝗮𝗱 𝗼𝗳 𝗿𝗲𝗹𝘆𝗶𝗻𝗴 𝗼𝗻 𝗵𝗲𝗿𝗼𝗶𝗰𝘀.
 
Most companies scale by hiring more people and praying they figure it out. Growth Institute teaches you to scale by designing the actual system – the frameworks, processes, and playbooks that turn chaos into consistency.
 
Here's what you're actually getting:
 
𝗢𝗽𝗲𝗿𝗮𝘁𝗼𝗿-𝗟𝗲𝗱 𝗖𝗼𝗵𝗼𝗿𝘁𝘀 You join 8-12 revenue leaders working on the same challenges. Each cohort is led by someone who's already built what you're trying to build – not a consultant, an actual operator with battle scars.
 
𝗥𝗲𝗮𝗹 𝗜𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻, 𝗡𝗼𝘁 𝗧𝗵𝗲𝗼𝗿𝘆 You're not sitting through slide decks. You're building live systems with your actual team while getting coaching. Week 1 you're diagnosing. Week 4 you're testing. Week 8 you have something running that's producing results.
 
𝗧𝗵𝗲 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸𝘀 𝗧𝗵𝗮𝘁 𝗔𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝗠𝗮𝘁𝘁𝗲𝗿 Buying journey mapping. Pipeline architecture. Retention mechanics. Customer success systems. The stuff that separates $10M companies from $100M companies.
 
𝗔𝗰𝗰𝗼𝘂𝗻𝘁𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝗧𝗵𝗮𝘁 𝗙𝗼𝗿𝗰𝗲𝘀 𝗥𝗲𝘀𝘂𝗹𝘁𝘀 Your cohort sees your progress every week. You can't hide behind "I'll get to it next quarter." You're either building or you're explaining why you're not.
 
Growth Institute is for revenue leaders who are done attending training that doesn't change anything. It's designed to turn your team into a machine that grows predictably instead of a group of individuals grinding their way to quota.
 
If you want to stop hiring your way out of growth problems and start building your way through them, this is the play.

Thanks Jacco van der Kooij Shari Johnston Dave Boyce for letting me tag along.

5 founders told me they burned $150K+ each on AI SDRs.

Same story every time:
• Week 1: "This is revolutionary!"
• Week 3: Deliverability tanks
• Week 8: Zero pipeline to show for it
• Week 12: Back to cold calling

So we brought in 🤙Neil Weitzman  Fractional CRO / GTM Operator / Advisor at 
weitzmanGTM who's actually running these experiments in the trenches.

No vendor pitch. No cherry-picked case studies. 

Just the truth about what's working (and what's complete BS) with AI sales development.

Here's what nobody talks about:The "AI SDR" isn't replacing your team.

It's exposing whether you had real GTM fundamentals in the first place.

Neil's seen it play out dozens of times: 
→ Companies with solid ICP definition, messaging, and offer? AI scales what's already working. 
→ Companies hoping AI will "figure it out"? Expensive disappointment.

The killer insight: AI SDRs work best as awareness marketing, not direct pipeline generation.

Think about it...when you're spending $2-5 per conversation instead of $200 per human SDR hour, the math completely changes. 

You're not buying meetings. 

You're buying thousands of relevant brand impressions at scale.

One client is using AI to touch 10,000 accounts monthly at a fraction of traditional costs. 

Are they booking 100 demos? No. 

Are they building category awareness that feeds their inbound motion? Absolutely.

But here's the uncomfortable truth: Most "AI SDR" tools are just fancy email automation wrapped in LLM prompts. 

The real work...list building, signal identification, message testing and still requires strategic thinking.

And if you're selling $500K+ deals? You still need humans building relationships. 

AI handles the top-of-funnel grunt work so your actual sellers can focus on the $1M opportunities.

The future isn't AI sellers OR human sellers.It's AI handling awareness and qualification at scale, while humans close complex deals.

We're already seeing AI buyers evaluate AI sellers. 

The entire motion is changing faster than most GTM leaders realize.

Want the full breakdown? 🤙Neil Weitzman, Jonathan M Kvarfordt, MBA and I went deep on when AI SDRs work, when they fail spectacularly, and what you need in place BEFORE you spend a dollar.

Link in comments 👇

What's your experience with AI SDRs? Working brilliantly or burning budget?

I used ChatGPT Voice to prep my AWS re:Invent presentation and interview.
 
Walking around my hotel. Inside. Outside. Hands-free. No typing. No switching screens.
 
I've been obsessed with conversational UX/UI since before it was cool.
 
My thesis: Voice is how humans naturally communicate. So voice will become how we interact with computers.
 
Not as a novelty. As the primary interface.
 
I now talk to my computer and phone about 80% of the time instead of typing.
 
Not because I'm trying to be futuristic. Because it's faster. More natural. Less friction between thought and action.
 
And what ChatGPT just shipped? Validation that this shift is real.
 
Voice is now just... part of the conversation. No toggle. No separate mode. You talk. Text appears. You see images, maps, code—all while speaking.
 
Mobile. Web. Everyone. Right now.
 
Here's what I actually did:
 
Presentation tomorrow. High stakes.
 
I opened ChatGPT and just... talked. While walking around the hotel. Through the lobby. Outside. Back to my room.
 
"Help me practice my presentation."
 
Then I delivered my intro. Out loud. Moving. Like I would on stage.
 
ChatGPT listened. Analyzed. Gave me specific feedback: 
→ "Your hook is strong but arrives 30 seconds late" 
→ "You used 'basically' three times in 90 seconds" 
→ "Your transition to the data point needs more punch"
 
Then we iterated. Live. Conversationally. Like having a coach beside me.
 
This isn't about voice recognition getting better.
 
It's about eliminating the cognitive load of interacting with technology.
 
What's different now:
 
Before: "Hey Siri, SET TIMER for 10 minutes" (command) 
Now: "I need to prep for a meeting in about 10 minutes" (intent)
 
The computer figures out what you need.
 
This is about eliminating context switching in the workflow that matters most.
 
Current reality:
 
A rep has a call in 12 minutes with a VP of Sales at a $50M ARR company.
 
They need to: 
→ Pull up account history
→ Review past conversations 
→ Check recent company news 
→ Find relevant case studies 
→ Draft discovery questions 
 
Five tools. Four logins. Twelve minutes.
 
That rep walks into that call underprepared. Every. Single. Time.
 
Now imagine this:
 
11 minutes before the call, they open one interface and just... talk:
 
"What do I need to know about Acme Corp before my call with their VP of Sales?"
 
And they get—while putting on their headset—account context, recent news, suggested questions, similar customer stories, and potential objections.
 
No tabs. No logins. No mode switching.
 
The best GTM tool isn't the one with the most integrations.
 
It's the one that disappears into the flow of actual selling.
 
For the last decade, we've built GTM tech like this:
 
"Here's a dashboard for analytics. Here's a portal for content. Here's a tool for sequences. Here's another tool for intelligence."
 
The future looks like this:
 
"Just talk. We'll handle the rest."

Most GTM leaders think they're building "AI-native" teams.
 
But they're actually just automating Cloud-era workflows.
 
And the gap is costing them their competitive edge.
 
I just realized we've lived through this exact pattern three times in 15 years. But most revenue leaders missed it because they were focused on hitting this quarter's number.
 
𝗖𝗹𝗼𝘂𝗱 𝗡𝗮𝘁𝗶𝘃𝗲 𝗚𝗧𝗠 (𝟮𝟬𝟭𝟬𝘀): We learned to think in scalable systems, not hero sellers. Repeatable playbooks, not one-off deals. Data-driven decisions, not gut instinct.
 
The companies that won? They didn't just "add Salesforce." They redesigned their entire revenue engine around connected systems.
 
𝗚𝗲𝗻𝗔𝗜 𝗡𝗮𝘁𝗶𝘃𝗲 𝗚𝗧𝗠 (𝟮𝟬𝟮𝟯): We learned to think in prompts, not templates. AI assistants, not manual research. Smart summaries, not endless note-taking.
 
But most teams stopped here. They think they're done.
 
𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗡𝗮𝘁𝗶𝘃𝗲 𝗚𝗧𝗠 (𝟮𝟬𝟮𝟱): 
→ Autonomous planning (systems that build account strategies without human input) 
→ Dynamic tool use (AI that decides when to research, when to personalize, when to reach out) 
→ Persistent memory (context that follows deals across months and handoffs) 
→ Multi-agent orchestration (SDR agents → AE agents → CS agents working as a team) 
→ Feedback loops (systems that learn from wins and losses to improve autonomously)
 
𝗧𝗵𝗶𝘀 𝗶𝘀𝗻'𝘁 𝗷𝘂𝘀𝘁 "𝗯𝗲𝘁𝘁𝗲𝗿 𝗔𝗜 𝘁𝗼𝗼𝗹𝘀."
 
It's a completely different way to architect revenue growth.
 
𝗧𝗵𝗲 𝗲𝘃𝗼𝗹𝘂𝘁𝗶𝗼𝗻 𝗹𝗼𝗼𝗸𝘀 𝗹𝗶𝗸𝗲:
 
• Cloud Native GTM → You standardize processes and track in CRM
• GenAI Native GTM → You use AI to speed up manual tasks
• Agentic Native GTM → AI runs entire workflows autonomously and improves itself
 
Most revenue teams are stuck trying to "prompt" their way to pipeline while competitors are building systems where AI agents actually 𝘳𝘶𝘯 the pipeline.
 
It's like trying to hit $100M ARR with a spreadsheet when everyone else has a revenue engine.
 
𝗧𝗵𝗲 𝗾𝘂𝗲𝘀𝘁𝗶𝗼𝗻 𝗶𝘀𝗻'𝘁 "𝗪𝗵𝗶𝗰𝗵 𝗔𝗜 𝘁𝗼𝗼𝗹 𝘀𝗵𝗼𝘂𝗹𝗱 𝗜 𝗯𝘂𝘆?"
 
It's "How do I redesign my GTM motion around autonomous agents?"
 
Because here's what's actually happening:
 
→ Your competitors aren't adding AI features 
→ They're rebuilding their entire revenue architecture 
→ With a 10x cost and speed advantage you can't replicate by just "doing AI better"
 
The companies that crack agentic-native GTM in 2026 won't just grow faster.
 
They'll have a fundamental systems advantage that looks like magic to everyone still optimizing GenAI prompts.
 
Know a GTM team that's actually building agentic-native systems? Tag them below. 👇

Image Source: AWS re:Invent

Everyone's building AI agents. But here's what nobody's talking about...
 
While OpenAI and Anthropic battle for model supremacy, Amazon Web Services (AWS) just made the most strategic move in AI infrastructure—and most GTM leaders missed it completely.
 
At re:Invent 2025, they didn't announce new models. They announced 𝘁𝗵𝗲 𝗼𝗽𝗲𝗿𝗮𝘁𝗶𝗻𝗴 𝘀𝘆𝘀𝘁𝗲𝗺 𝗳𝗼𝗿 𝗮𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜.
 
𝗧𝗛𝗘 𝗜𝗡𝗙𝗥𝗔𝗦𝗧𝗥𝗨𝗖𝗧𝗨𝗥𝗘 𝗡𝗢𝗕𝗢𝗗𝗬 𝗪𝗔𝗡𝗧𝗦 𝗧𝗢 𝗕𝗨𝗜𝗟𝗗
 
Building production-ready AI agents requires solving 7 problems: 
→ Runtime orchestration 
→ Persistent memory 
→ Identity management 
→ Gateway routing 
→ Code interpretation 
→ Browser automation 
→ Real-time observability
 
AWS just packaged all of it into Amazon Bedrock AgentCore.
 
𝗧𝗛𝗘 𝗦𝗧𝗔𝗖𝗞 𝗧𝗛𝗔𝗧 𝗖𝗛𝗔𝗡𝗚𝗘𝗦 𝗘𝗩𝗘𝗥𝗬𝗧𝗛𝗜𝗡𝗚
 
𝗙𝗼𝘂𝗻𝗱𝗮𝘁𝗶𝗼𝗻 𝗟𝗮𝘆𝗲𝗿: Nova models + 3P models (Claude, GPT-4, etc.) with guardrails and knowledge bases
 
𝗔𝗴𝗲𝗻𝘁 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁 (𝗔𝗴𝗲𝗻𝘁𝗖𝗼𝗿𝗲):
 
• Memory that persists across sessions
• Runtime for autonomous goal execution
• Gateway for multi-agent routing
• 13 evaluation systems for safety
• Secure identity per agent
• Browser tools for web navigation
 
𝗔𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀: Pre-built agents (Kiro, Amazon Q) you deploy in hours
 
𝗪𝗛𝗬 𝗧𝗛𝗜𝗦 𝗠𝗔𝗧𝗧𝗘𝗥𝗦
 
AWS isn't competing with foundation models. They're becoming the infrastructure layer ALL agentic AI runs on.
 
The evolution:
 
• Cloud-native → deterministic workflows
• Generative AI → prompt-driven augmentation
• Agentic AI → goal-driven loops with persistent memory
 
The shift from "context window" to "persistent memory" is the unlock. Your agents don't forget.
 
𝗧𝗛𝗘 𝗚𝗧𝗠 𝗜𝗠𝗣𝗟𝗜𝗖𝗔𝗧𝗜𝗢𝗡𝗦
 
Lyft deployed this stack:
 
• 87% reduction in resolution time
• 70% driver adoption growth
• Sub-3 minute resolutions
 
The barrier to production agents just dropped 90%.
 
Time to build went from quarters to weeks.
 
Werner Vogels' closing quote:
 
"𝗙𝘂𝘁𝘂𝗿𝗲 𝗼𝗳 𝘄𝗼𝗿𝗸 𝗶𝘀 𝘁𝗵𝗮𝘁 𝗲𝘃𝗲𝗿𝘆 𝗽𝗲𝗿𝘀𝗼𝗻 𝗶𝗻 𝗮𝗻 𝗼𝗿𝗴𝗮𝗻𝗶𝘇𝗮𝘁𝗶𝗼𝗻 𝗵𝗮𝘃𝗲 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝘁𝗲𝗮𝗺𝗺𝗮𝘁𝗲𝘀 𝗮𝗺𝗽𝗹𝗶𝗳𝘆 𝗵𝘂𝗺𝗮𝗻 𝗰𝗮𝗽𝗮𝗯𝗶𝗹𝗶𝘁𝗶𝗲𝘀"
 
𝗪𝗛𝗔𝗧 𝗖𝗛𝗔𝗡𝗚𝗘𝗦 𝗙𝗢𝗥 𝗚𝗧𝗠:
 
Your marketing team won't "use AI for content."  They'll have an agent maintaining persistent memory of every brand interaction.
 
Your SDRs won't "use AI for research."  They'll have an agent that remembers every account touchpoint across your entire team.
 
Your CSMs won't "use AI for health scores."  They'll have an agent proactively reaching out before churn signals emerge.
 
𝗧𝗛𝗘 𝗖𝗢𝗡𝗧𝗥𝗔𝗥𝗜𝗔𝗡 𝗧𝗔𝗞𝗘
 
Everyone's betting on which LLM wins.
 
AWS is betting on becoming the infrastructure layer that powers them all.
 
The gold rush isn't in the gold. It's in the picks and shovels.
 
And AWS just opened the world's most advanced tool shop.
 
What AI agent infrastructure are you betting on?

Hope everyone had an amazing holiday, I took some time off and disconnected, hope you did as well. A lot happened and I am going to share thoughts on it all this week, plus some expansive thoughts on AI-native GTM. 

But first.. let's get into some of the things that happened over the last week or so. 
 
Meta just gave ChatGPT, Copilot, and Perplexity their eviction notice from WhatsApp Business API. Wonder if ChatGPT group chats accelerated this.. 
 
Official reason: "Business API is for transactional messaging, not AI assistant distribution."
 
Real reason: Meta wants Meta AI to own the playground.
 
Think about that.
 
This isn't a WhatsApp problem. It's a "building on someone else's rails" problem.
 
→ Meta wants Meta AI 
→ Google wants Gemini everywhere  
→ Microsoft wants Copilot in everything
 
That brief magical period where AI assistants could meet customers wherever they were? It's ending faster than anyone expected.
 
What does this mean for your GTM org?
 
If you're using AI chatbots on third-party platforms for customer conversations, you've got three immediate problems:
 
→ Platform risk you can't control. That Slack integration, that LinkedIn automation, that WhatsApp bot—they can all disappear with 90 days notice. Your workflows, your data, your customer relationships. Gone.

→ Data you can't move. Most of these integrations don't let you export conversation history. When the platform pulls the plug, you're starting from scratch. Every insight, every customer interaction, every qualification step—locked in someone else's system.

→ GTM strategies built on borrowed time. If your outbound motion, your customer success workflows, or your sales enablement depends on tools you don't control, you're one terms-of-service update away from scrambling.
 
The companies winning right now? They're building AI on infrastructure they own, and integrating strategically where it makes sense. But they never confuse the two.
 
The question for every GTM leader:
 
Where are you building your AI integrations?
 
Because if the answer is "on platforms we don't control," you need to answer three more questions:
 
1. What happens when they change the rules overnight?
2. Can you actually migrate your customer data if you need to?
3. Who owns the conversation history with your customers?
 
The pattern here is old. The speed is new.
 
Every platform eventually prioritizes its own ecosystem. MySpace did it. Facebook did it. Twitter did it.
 
But the AI platforms are doing it in months, not years.
 
My take:
 
Build where you own the rails. Integrate where you don't. But never confuse the two.
 
And if you're relying on third-party AI integrations for customer conversations? Audit that data portability. Today.
 
Because the walls are going up faster than anyone thought possible.

"We thought anyone could code now."
 
Week 1: "We built a customer portal in 2 hours!"  
Week 4: "Why does it keep crashing?"  
Week 12: Hiring the engineers we thought we didn't need.
 
The AI coding revolution promised to eliminate developers.
 
Instead, it's eliminating companies that believed that promise.
 
The $30 Billion Bait-and-Switch
 
Cursor: $29B valuation  
Replit: $16M → $253M ARR in 12 months
 
Everyone sees these numbers and thinks: "The citizen developer era is here."
 
Wrong.
 
Those valuations are built on selling to 𝘀𝗲𝗻𝗶𝗼𝗿 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝘀 𝘄𝗵𝗼 𝗰𝗼𝗱𝗲 𝟭𝟬𝘅 𝗳𝗮𝘀𝘁𝗲𝗿—not marketing teams playing developer.
 
That's the difference between tools that compound value and strategies that compound debt.
 
Why "Anyone Can Code" Is a Ticking Time Bomb
 
When AI generates code, it makes hundreds of invisible architectural decisions:
 
→ How customer data is stored (impacts scale)  
→ How authentication works (determines security exposure)  
→ How systems integrate (affects every future feature)
 
Senior developers orchestrate these strategically.  Non-technical builders don't even know the decisions exist.
 
The House Analogy That Makes This Click
 
𝗧𝗵𝗲 "𝗔𝗜 𝗽𝗿𝗼𝗺𝗶𝘀𝗲": Describe your dream house, get blueprints instantly. No contractors needed!
 
𝗥𝗲𝗮𝗹𝗶𝘁𝘆: You get gorgeous 3D renderings in 30 minutes.
 
But when you build:  
→ Foundation can't support the second floor  
→ Electrical isn't up to code  
→ Plumbing makes renovations impossible
 
Now you're hiring contractors to rebuild, not build.
 
2x the cost. 3x the timeline.
 
What Actually Changed
 
Before AI:
 
1 architect + 10 builders = 6 weeks
 
With AI:
 
1 architect + AI = 3 days
 
Execution happens 10x faster. But you still need someone who understands architecture.
 
The Math That Should Terrify You
 
"Citizen developer" path:  
→ $50K in tools  
→ $400K rebuilding when it breaks  
→ 6 months lost
 
AI-amplified technical team:  
→ $150K (senior hire + tools)  
→ Shipping in weeks  
→ No compounding debt
 
The "cheap" path costs 3x more.
 
What Dies vs. What Thrives
 
𝗗𝗶𝗲𝘀:  
→ Marketing teams building production systems  
→ "No-code means no expertise needed"  
→ Strategies without technical oversight
 
𝗧𝗵𝗿𝗶𝘃𝗲𝘀:  
→ Technical teams eliminating grunt work  
→ Senior developers orchestrating 5-10x output  
→ Companies investing in leadership AND tooling
 
The Bottom Line
 
"Vibe coding" isn't dead.
 
The fantasy that anyone can build production software without technical expertise? That was always fiction.
 
AI amplifies expertise.
 
But amplifying zero still equals zero.
 
The question isn't "Can we replace developers with AI?"
 
It's: "Do we have technical leadership to orchestrate AI effectively?"
 
If the answer is no, buying AI coding tools is like buying a Formula 1 car when you don't know how to drive.
 
You'll go fast for 30 seconds. Then crash spectacularly.

$2 trillion gets burned every year on GTM operations that don't work.
 
Not because companies are lazy. Not because they're stupid.
 
Because they're goldfish swimming in circles.
 
Let me explain.
 
I've been obsessing over this pattern for months now, and it's wild how clearly it shows up once you see it:
 
𝗧𝗿𝗮𝗱𝗶𝘁𝗶𝗼𝗻𝗮𝗹 𝗰𝗼𝗺𝗽𝗮𝗻𝗶𝗲𝘀 = 𝗕𝗿𝗶𝗹𝗹𝗶𝗮𝗻𝘁 𝗴𝗼𝗹𝗱𝗳𝗶𝘀𝗵
 
They swim in circles. Every sales cycle is treated like a brand new event. Every campaign starts from scratch.
 
When your top rep leaves? The knowledge of how they closed that Fortune 500 deal leaves with them. The 47 objections they figured out how to handle? Gone. The pricing negotiation playbook they built over 3 years? Poof.
 
The company "forgets."
 
And here's the kicker – you're spending MORE to get LESS:
 
→ CAC is up 62% in five years  
→ Sales cycles stretched 23% longer  
→ The median company now spends $2.01 to acquire $1 of ARR
 
You read that right. Two dollars for one dollar.
 
𝗔𝗜-𝗡𝗮𝘁𝗶𝘃𝗲 𝗰𝗼𝗺𝗽𝗮𝗻𝗶𝗲𝘀 = 𝗘𝗹𝗲𝗽𝗵𝗮𝗻𝘁𝘀
 
They never forget. Every conversation feeds the system. Every win, every loss, every objection becomes institutional knowledge.
 
But here's what nobody's talking about...
 
The difference isn't just memory. It's the physics of how improvement happens.
 
Traditional GTM is linear. You get a better email template, maybe open rates bump 5%. You hire a new sales trainer, performance lifts 10%. These gains are additive.
 
AI-Native GTM is exponential. Every interaction makes the next one smarter. Automatically. Instantly. Across the entire organization.
 
Think about it: If your system improves conversion by 1% compound weekly because it's learning from every signal, and your competitor improves 5% annually through quarterly training programs...
 
The gap becomes impossible to close.
 
Week 12? You're ahead.  
Week 24? You're dominating.  
Week 52? They can't see your taillights.
 
This is why companies with worse products are starting to win against companies with better sales teams.
 
The software is remembering what the people forget.
 
And the scary part? Most executives I talk to are still trying to solve this with more training, more tools, more headcount.
 
More goldfish laps.
 
They're not seeing the fundamental shift: we're moving from people-powered compound growth to systems-powered compound intelligence.
 
The companies that figure this out in 2025 won't just be more efficient.
 
They'll be playing a different game entirely.

Are you building a goldfish or an elephant?

Walk through any sales tech stack today.
 
Most GTM teams are slapping AI onto their existing workflows. An AI SDR here. A chatbot there. A co-pilot everywhere.
 
Everyone's bolting AI onto old workflows.
 
The winners are rebuilding from scratch.
 
Here's what I mean:
 
Most GTM teams are adding AI features to existing processes. A tool here. An assistant there.
 
But they're missing the bigger picture.
 
Your brain doesn't work because you have really good eyes. It works because your eyes connect to a processing system that learns, remembers, coordinates, and acts—all in real-time.
 
That's compound intelligence. And it's the only moat that matters in AI-native GTM.
 
𝗧𝗵𝗲 𝘀𝗶𝘅-𝗹𝗮𝘆𝗲𝗿 𝗮𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 𝗻𝗼𝗯𝗼𝗱𝘆'𝘀 𝘁𝗮𝗹𝗸𝗶𝗻𝗴 𝗮𝗯𝗼𝘂𝘁:
 
𝗟𝗮𝘆𝗲𝗿 𝟭: 𝗗𝗮𝘁𝗮 (𝗧𝗵𝗲 𝗦𝗲𝗻𝘀𝗲𝘀) Capture every signal. Every customer interaction. Every signal in perfect fidelity. Real-time.
 
𝗟𝗮𝘆𝗲𝗿 𝟮: 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲 (𝗧𝗵𝗲 𝗕𝗿𝗮𝗶𝗻)  Process raw signals into predictions. Pattern recognition that gets smarter with every interaction.
 
𝗟𝗮𝘆𝗲𝗿 𝟯: 𝗖𝗼𝗻𝘁𝗲𝘅𝘁 (𝗧𝗵𝗲 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝘆) Apply your business logic and domain knowledge. This is where intelligence becomes relevant to your actual business.
 
𝗟𝗮𝘆𝗲𝗿 𝟰: 𝗠𝗲𝗺𝗼𝗿𝘆 (𝗧𝗵𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗦𝘆𝘀𝘁𝗲𝗺) Store and synthesize everything. Your institutional knowledge compounds over time instead of walking out the door with your VP of Sales.
 
𝗟𝗮𝘆𝗲𝗿 𝟱: 𝗢𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗶𝗼𝗻 (𝗧𝗵𝗲 𝗖𝗼𝗼𝗿𝗱𝗶𝗻𝗮𝘁𝗼𝗿) Coordinate all systems in real-time. No more handoffs. No more dropped signals.
 
𝗟𝗮𝘆𝗲𝗿 𝟲: 𝗘𝘅𝗲𝗰𝘂𝘁𝗶𝗼𝗻 (𝗧𝗵𝗲 𝗔𝗰𝘁𝗶𝗼𝗻 𝗘𝗻𝗴𝗶𝗻𝗲) Take actions autonomously. Learn from outcomes. Improve continuously.
 
The companies building this right now aren't optimizing their GTM motion.
 
They're redesigning it from the ground up.
 
And while everyone else is asking "which AI tool should we buy?" they're asking "how do we build a system that learns faster than our competitors?"
 
That's the real question.
 
What would change if your GTM system remembered everything, learned from every interaction, and got exponentially smarter—not just incrementally better?

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Mark Kosoglow

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Performance Score.css-goub6s{font-size:var(--chakra-fontSizes-md);font-weight:500;color:var(--chakra-colors-gray-600);display:block;margin-top:var(--chakra-space-1);}Mark Kosoglow's LinkedIn Performance

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