Growthmode

A digital marketing platform and growth agency that puts technology, AI and automation at the forefront. 

Built by a highly experienced team of entrepreneurs and academics with strong backgrounds in AI, marketing and enterprise SaaS, our aim is to help modern marketing teams keep up with the pace of change through accessible technology and data-driven strategic marketing services. 

Products and services include:

- Data-driven growth marketing services
- AI and automation development platform
- SaaS growth marketing platform

Talk to us if you're looking for:

- Ways of unlocking rapid growth, when you've already tried traditional channels
- Support in navigating and implementing AI to support your marketing
- More accessible, simplicity-focused technology tools to manage your growth

CEO & Founder

Alvius

Groundbreaking recruitment technology platform.

Early Investor and Advisor

Umi Digital

Digital marketing agency focused on the hospitality sector.

Co-Founder, Board Member

Firedrop

Generative AI startup on a mission to automate design.

Launched the world's first automated website builder in 2017, using highly innovative proprietary AI technology to generate website designs witout templates.

Pivoted to enterprise design automation, ultimately delivering multi-million dollar design automation and AI transformation projects for multinational FMCGs in the area of packaging design and ad creatives.

Founder & CEO

Likeminded Media

Online dating portfolio company featuring popular niche sites for redheads, plus-sized people and more. Grew from 0 to 50,000 subscribers entirely organically.

Alterity

Software development agency. Sold the business after 18 months.

Founder & Managing Director

Integral Search

Headhunting senior consultants in the risk management sector.

Head of Risk Management Search

Imprint Plc

Headhunting senior consultants in the accounting and management consultancy space.

Executive Search Consultant

Omnia Music

An online music platform focused on discovery of unsigned artists. Grew from infancy to over 4,000 registered artists and over 3 million visits a month.

With over 20 years of visionary leadership in AI, marketing, and enterprise SaaS, I am deeply passionate about driving innovation and growth. As the CEO and founder of Growthmode, my mission is to empower modern marketing teams to thrive in this era of rapid technological change by offering accessible tech and data-driven strategic marketing support, equipping teams with the necessary tools and insights for rapid growth and AI integration.

My journey includes co-founding several successful startups, notably a generative AI company that spearheaded multi-million dollar AI projects before the era of ChatGPT.

At Growthmode, I lead a highly experienced team of entrepreneurs, marketers, creatives and academics with backgrounds in AI, marketing, and enterprise SaaS. We are building a digital marketing platform and growth agency that puts technology, AI, and automation at the forefront. Our goal is to help businesses do 30% more.

Beyond Growthmode, I lend my expertise as an advisor and mentor to early-stage startups and entrepreneurs.

Marc Crouch

Kevin Payne

GTM Engineer at LawVu | Building Revenue Systems For Impact Startups | A16z, YC & Techstars Portfolio

From the intersection of AI, Startups, and Web3. The future of tech belongs to impact driven creators and operators.

In 2020, I was a full-time contributor for publications with 200+ bylines in HubSpot, Crunchbase, Blavity and major tech publications. In 2025, I joined a Series A/B SaaS company as a GTM Engineer building AI-powered revenue systems.

Most operators choose one path: stay employed OR go solo. I'm proving you can do both without burnout. Build a portfolio career and diversify your sources of income one small bet at a time.

How I can help...

FOR CREATORS: If you're building a personal brand or content business, my frameworks show you how to learn in public (Learn > Create > Share) and monetize your expertise through the 5C model. Join 23K+ creators who follow my systems for scalable content and portfolio income.

FOR OPERATORS: If you're at a Series A/B company wanting to build portfolio income alongside your full-time employment, I show you how to prototype revenue systems in your operator role, document what works, then package that knowledge into content and coaching offers.

My Approach: I don't consult from the sidelines. I prototype AI systems in my operator role, document what works, then teach operators how to implement for founders. Learn as an operator, share as a creator.

Current Focus: Building portfolio career through the 5C model:

- Clone
- Content
- Courses
- Community
- Conferences

Learning in Public: Learn in operator role > Create content documenting what works > Share with operators and founders. Get paid to learn, get paid to share.

Three Core Interests:

1. The Operator Stack: Leverage AI systems for sustainable growth
2. Portfolio Income: Small bets optimized for social impact
3. The Five-Pillars: Integrated life philosophy

Meta-Advantage: Teaching AI workforce implementation while using AI workforce to scale personal brand… authentic proof-of-concept.

Join 1K+ creators and founders getting their minds renewed. No fluff, just Growth Guides that merge biblical wisdom with proven AI systems.

Subscribe at https://substack.com/@kevintpayne or follow @kevintpayne.

Founder

Building a portfolio of digital real estate. Everyone’s familiar with real estate investments. It’s as simple as buying a house or land to rent or sell for profit. 

Digital real estate works the same way. Every domain name, every website and every blog you see online is internet property. They represent digital real estate you can buy and sell just like houses, apartments and lots.

I specialize in writing and speaking about solopreneurship, artificial intelligence, and minimalism. My mission is to document my journey and impart wisdom pertaining to wealth and health.

"Don't paste sensitive data into AI."

That is not a policy. That is a wish.

Real AI governance has explicit categories, clear ownership, and documented consequences.

Here is the red/yellow/green framework every operator should have:

Two types of companies are deploying AI right now.

The ones with a documented data classification policy for AI usage.

And the ones hoping nothing goes wrong.

The second group is larger than anyone wants to admit.

The risk is not just legal.

It is trust.

One incident where sensitive data ends up in a prompt log or an AI training set and the damage to your company, your customers, and your career does not recover quickly.

Serious operators treat AI data policy the same way they treat security policy. Non-negotiable.

RED means never goes into a prompt. No exceptions.

Personally identifiable information without explicit consent. Contract terms or deal economics. Internal org structure or strategic plans. Customer compliance records. Anything covered by confidentiality obligations.

If it is red, it stays out. Full stop.

YELLOW means requires review before use.

Aggregated metrics without individual attribution. Anonymized case study material. Industry trends derived from internal sources. Competitive intelligence gathered internally.

Yellow means: ask before you paste. Get explicit approval from whoever owns that data category.

GREEN means safe to use with no additional review.

Publicly available information. Your own original writing and frameworks. Anonymized examples with no reverse-engineer risk. Approved marketing copy and positioning. Published product documentation.

Green means: proceed. No escalation needed.

How to enforce this in practice:

Every prompt template gets a data classification label at the top.

"This prompt accepts GREEN only."

"This prompt accepts GREEN plus approved YELLOW."

The label takes 5 seconds to add. It prevents significant incident response time down the road.

Most operators do not have this documented.

They are relying on individual judgment, call by call.

That works fine until someone new joins the team, or moves fast under deadline pressure, or simply forgets.

Policies exist because humans are inconsistent under pressure. Build the policy before you need it.

The gap between a serious AI operator and an AI hype consumer is often one document.

A clear, enforced data classification policy.

It does not need to be long. It needs to be specific, adopted by the team, and reviewed quarterly.

That is what separates trust-building deployment from reckless experimentation.

I build and share operator-level AI frameworks from someone actively deploying this in real B2B SaaS environments.

Follow if you want practical ethics, not performative ones.

What is the one data boundary your team currently does not have written down? Drop it below.

GTM Engineer at LawVu | Building AI-Powered Systems | 200+ Publication Bylines | Operator at A16z, YC & Techstars Startups

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Want more trust? Stop asking for meetings.
Start shipping weekly clarity.

I haven't requested a status meeting in months.

My stakeholders know exactly what I'm working on, what's at risk, and what I need.

Here's the format I use:

Most operators earn trust by being available.

That is the wrong model.

Available means reactive. Reactive means you are always catching up.

The highest-trust operators are the ones whose stakeholders never have to wonder what is happening.

Meetings feel like trust. They are usually the opposite.

If your manager has to schedule time with you to understand your progress, that is a gap in your communication system. Not a relationship moment.

The weekly impact report closes that gap before it opens.

My report has 5 sections. Takes 20 minutes to write. Sends on Fridays.

This Week's Output. Metrics That Moved. Blockers and Risks. What I Need. Next Week's Focus.

That is it. No narrative. No filler. No "I've been very busy."

This Week's Output.

Not activities. Not effort. Output.

"Launched X." "Completed Y." "Shipped Z."

If you can't list 3 completed outputs on a Friday, that is information worth knowing too.

The discipline of writing this section forces prioritization all week.

Metrics That Moved.

Pick 2 to 3 KPIs relevant to your work. Report the number. Report the delta.

That is one line per metric. It tells your stakeholder where you are, where you were, and where you are going.

Numbers are trust signals. Narrative is a substitute for them.

Blockers and Risks.

If something is going sideways, your stakeholder should read about it here before they hear about it elsewhere.

What I Need.

This is where you make cross-functional asks without scheduling a meeting.

One line. Clear owner. Clear deadline. Done.

Next Week's Focus.

Two priorities. No more.

This signals intentionality. It also creates a natural accountability loop for the following Friday.

If you said you were focused on X and Y, then X and Y better show up in next week's Output section.

The weekly impact report is not a status update.

It is a trust infrastructure document.

It replaces uncertainty with clarity. It replaces interruptions with rhythm. It replaces availability theater with actual output.

High-trust operators do not get asked for more meetings. They get more autonomy.

I share operator frameworks like this every week, built from real GTM and RevOps experience.

Follow along if you want to build indispensability through systems, not hustle.

What does your current stakeholder communication look like? Drop it below.

80% of deals require 5+ touches to close.

44% of reps stop after one follow-up.

That gap is not a motivation problem. It's an architecture problem.

Here's the 5-touch sequence design that closes it:

This stat has been cited for years and nothing changes.

Why?

Because most teams treat follow-up as a task instead of a system.

Tasks get skipped. Systems run on their own.

The fix is not training your reps to follow up more. It's building a structure that makes follow-up automatic.

Working in B2B SaaS GTM, I've seen what breaks down at every touch point.

Touch 1: Cold. No context. Generic ask.
Touch 2: "Just checking in." No value.
Touch 3: Never happens.

The problem isn't volume. It's that each touch has no reason to exist on its own.

Every touch needs a job.

Touch 1 is the Signal Touch.

Triggered by a specific behavior. A page visit. A content download. A job change at a target account.

Not "Hi, I found your profile." But: "Noticed your team expanded into enterprise, we work with teams making that exact transition."

Relevance before anything else.

Touch 2 is the Proof Touch.

One case study. One result. Same vertical as the prospect.

Not a brochure. A single sentence that shows you've solved their specific problem before.

"We helped a team like yours cut their time-to-first-meeting by 40%. Here's how."

Touch 3 is the Insight Touch.

Share a non-obvious observation about their market, their competitor, or their customer segment.

This is not a pitch. It is a demonstration of the quality of thinking they'd get working with you.

The best touch 3s get forwarded internally.

Touch 4 is the Friction-Removal Touch.

Remove the most common objection before they raise it.

"I know timing might be off, completely understand. When would be a better window to reconnect?"

Give them an easy exit. The ones who aren't going anywhere will tell you.

Touch 5 is the Breakup Touch.

Be direct. Not passive-aggressive.

"This will be my last reach out. If it's not a fit, no problem. But if there's a sliver of interest, a quick 15 minutes could be worth it before I close your file."

Scarcity plus respect. These convert more than any other touch in the sequence.

The system behind this:

Intent data identifies the trigger for Touch 1. A CRM sequence automates Touches 2 through 4. A human sends Touch 5 personally.

AI handles volume. Humans handle judgment at the moment that matters most.

That's the architecture most teams are missing.

The reason 44% of reps stop after one follow-up: they don't have a reason to send the next one.

Fix that by giving every touch a specific job. Signal. Proof. Insight. Friction removal. Breakup.

When every touch has a purpose, there's no awkwardness in sending it.

If this changed how you think about outreach architecture, follow me.

I cover cold outreach systems, AI-assisted GTM, and revenue infrastructure for B2B operators every week.

Which touch in this sequence is your team currently skipping? Reply below.

I studied top GTM teams at unicorns.

Most scaled with burnout.

The winners did this instead:

The Pattern:

Most teams: Scale headcount

• Hire more SDRs
• Hire more AEs
• Hire more ops
• Burnout at scale

Winning teams: Scale systems

• AI agents first
• Human escalation
• Sustainable growth

The Anti-Pattern:

"We need more people to hit our number"

Result: Higher costs, same problems, now at scale

The Winner Pattern:

"What manual work can we systematize?"

Result: Lower costs, better quality, compound leverage

How to build scalable ops:

1. Document every manual process
2. Identify repetitive work
3. Build AI agents for volume
4. Keep humans for exceptions
5. Measure relentlessly

Most teams skip step 1 and wonder why AI fails...

Everyone says: "Pick one thing and go deep."

I did the opposite and built portfolio income.

Here's why the conventional wisdom is wrong:

The Conventional Wisdom: Specialize. Be the best at one thing. Focus beats diversification.

Why It Fails in 2026: 

Specialists are getting automated. 

AI handles narrow expertise better than humans. 
The future belongs to AI Generalists.

The Contrarian Thesis:

Portfolio careers > single-track careers
Multiple income streams > one employer
AI-powered leverage > manual hustle

The Reality: 

Most professionals spend significant time on tasks AI could handle. 

But they're stuck in specialist mode. Can't see the forest for the trees.

The AI Generalist Advantage: 

You're not the best coder. 
You're not the best writer. 
You're not the best marketer. 

But you orchestrate AI systems that are.

Real Example: 

I'm not the best at any single thing. 

But I orchestrate AI systems that handle: Lead research, Content creation, Image generation, Content repurposing, Distribution

The Portfolio Career Model:

Operator role (stable base income)

+ Content revenue (immediate cash flow)
+ Community revenue (recurring)
+ Course revenue (passive)
+ Coaching revenue (high-margin)
= 5 income streams vs 1

Why It Compounds: 

Each stream feeds the others. 

Content attracts consulting clients. 
Coaching creates course material. 
Courses build community. 
Community generates content ideas.

The Specialist Trap: 

Maxes out at hourly rate × available hours. 
One income stream = high risk. 

Can't take time off without revenue hit.

The Portfolio Advantage: 

Income streams compound. One stream down? Four others running. Time off doesn't kill revenue.

The Meta-Shift:

Industrial Age: Specialize deeply, climb ladder
AI Age: Generalize widely, build portfolio

Your Move: Audit your income sources. If you only have one, you're at risk. Start building stream #2 this quarter.

I've watched dozens of companies implement AI agents.
The difference between winners and losers? 
How they start.

LOSERS START WITH TOOLS
We need AI agents for our business.
Then they scramble to find use cases.
Result: Solutions searching for problems. Expensive pilots that lead nowhere. Frustrated teams. Wasted budgets.

WINNERS START WITH PROBLEMS
We have this manual process eating 20+ hours per week.
Then they build AI to solve that specific problem.
Result: Problem-first, tool-second. Measurable ROI from day one. Team buy-in. Sustainable adoption.

THE LOSER IMPLEMENTATION PATTERN
See impressive AI demo at conference
Get excited about possibilities
Buy enterprise AI license
Tell team: Start using AI to be more productive
Three months later: Minimal adoption, unclear value

Wonder why the pilot failed
They skipped the most critical step: Building conviction before buying tools.

THE WINNER IMPLEMENTATION PATTERN
- Identify specific manual bottleneck (e.g., lead research)
- Document the current manual process in detail
- Build AI agent for that ONE specific task
- Measure performance before and after
- Scale what demonstrates clear value
Conviction first. Tools second. Always.

REAL-WORLD EXAMPLE
Loser Approach
Let's implement AI across our entire GTM stack!
• Pilot 10 different AI tools simultaneously
• Team overwhelmed and confused
• No clear ownership or success metrics
• Nothing sticks after 90 days

Winner Approach
"Automate lead research using intent data"
• Build ONE AI agent for this specific task
• Clear scope and measurable outcome
• Saves 15+ hours per week immediately
• Team sees value, asks for more

THE QUESTIONS WINNERS ASK
Before buying any AI tool
• What manual work costs us the most time?
• What tasks are repetitive and high-volume?
• What's our success criteria for automation?
• Who owns the outcome and adoption?

Losers ask: "What's the coolest new AI tool everyone's talking about?"

WHY THIS MATTERS IN 2026
Most companies will waste their AI budgets this year.
Not because the technology doesn't work.
Because they're buying tools without building conviction.

They're implementing Layer 3 (outputs) without Layer 1 (identity) or Layer 2 (framework).
Winners? They solve problems. Then automate solutions. Then scale what works.

Tools are commodities. Conviction is competitive advantage.
What manual process is your team solving first?

#AIImplementation #GTMStrategy #AIAgents #ProcessAutomation #BusinessSystems

AI didn't fail. Our operating model did.

We added AI to the funnel thinking it would fix things.

It exposed every broken assumption we had instead.

Here's what actually broke and how we caught it:

Most AI GTM content shows you the wins.

Pipeline numbers. Time saved. Conversion lifts.

Nobody talks about what happens in month 2 when the cracks start showing.

This is that thread.

Working in B2B SaaS GTM, I've seen 4 failure modes show up consistently across teams.

None of them were tool failures.

Every single one was a governance failure.

Duplicate outreach. Routing ambiguity. Measurement drift. False positives on intent.

The AI did exactly what it was told. The operating model just wasn't ready.

Duplicate outreach.

Two AI systems with no shared suppression logic.

The same prospect gets contacted multiple times in the same week.

Different tone. Different offer. Same company.

Fix: One unified suppression list. Checked before every send. No exceptions.

Routing ambiguity.

An AI scores a lead as "high intent."

But high intent for what? The feature they browsed? The tier they can afford? The segment you actually serve?

The signal was real. The routing rules were vague.

Fix: Intent signals need explicit routing destinations. "High intent" is not a destination.

Measurement drift.

AI-assisted pipeline gets measured the same way human-sourced pipeline does.

Different velocity. Different close rates. Same dashboard.

The data looks fine. The decisions are wrong.

Fix: Separate attribution lanes for AI-assisted vs. human-led work. Different benchmarks. Different levers.

False positives on intent.

The AI surfaces accounts that look ready based on behavioral signals.

Some are competitors doing research. Some are non-buyers with curiosity. Some have no purchasing authority.

Fix: Intent scoring needs disqualification logic, not just qualification logic. Build both sides of the filter.

All of this gets caught through a weekly governance review.

Not a dashboard. A conversation.

GTM and RevOps looking at a shared log of anomalies together.

If nobody is reviewing AI outputs with fresh eyes on a weekly cadence, you are not catching drift. You are accumulating it.

Adding AI to your funnel is easy.

Designing an operating model that can hold AI accountable is hard.

The failures are not in the model. They are in your assumptions, your routing rules, and your measurement logic.

Audit those first. Then automate.

Working in B2B SaaS GTM, I have been documenting patterns like these for years.

If this was useful, follow me. I publish practical operator-level AI content weekly.

Drop a comment: what is breaking in your AI-assisted funnel right now?

Graphic contrasts traditional operational models with an advanced AI system, highlighting workflow efficiency and clarity improvements.

93% of venture-backed startups now screen for AI skills in GTM.

Most operators hear that and think: tools.

Investors mean something completely different.

Here's what they're actually evaluating and how to position yourself:

The HubSpot AI GTM Report dropped this stat in January 2026.

But the conversation in most operator Slack groups missed the point entirely.

Everyone scrambled to add "AI" to their resume.

Nobody asked what investors were actually looking for when they wrote that requirement.

Working in B2B SaaS GTM, I've noticed a pattern across the companies that are winning right now.

It's not the ones with the most AI tools.

It's the ones where a single operator understands how to connect tools into a motion that compounds.

That's the skill being screened for.

What investors mean by "AI-first GTM":

Not "uses ChatGPT to write emails."

They mean an operator who can design a revenue motion where AI handles volume and humans handle judgment.

Two different skill sets. Only one of them is hard to hire for.

The old GTM model: hire more reps to generate more pipeline.

The AI-first GTM model: design the system once, then scale the system, not the headcount.

Investors are now asking: who on your team knows how to build that system?

If nobody raises their hand, that's a red flag.

What "AI-first" looks like in practice:

Intent signals trigger outreach automatically. AI scores and routes. Reps only touch accounts that meet a qualification threshold.

The operator who built that motion is not replaceable.

The rep who only runs it? Increasingly optional.

Three things investors are actually screening for:

1. Can you design a GTM motion with AI in the workflow, not bolted on at the end?
2. Can you measure AI-assisted pipeline separately from human-led pipeline?
3. Can you build governance so the AI doesn't drift?

Most operators can do none of these.

The fastest way to become the person investors want on their team:

Stop learning tools. Start learning systems.

Tools change every 6 months. The ability to architect a connected revenue motion that scales without headcount compounds forever.

The operators who will win the next 5 years are not the ones who know the most AI tools.

They're the ones who can look at a broken funnel and say:

"I know exactly where AI fits, where humans need to stay, and how to measure whether it's working."

That's GTM engineering.

The good news: most of your competition is still in the tool phase.

They're collecting subscriptions.

You can be building conviction stacks, documented processes that show exactly how you connect AI to revenue outcomes.

That's the portfolio that gets you in the room.

What investors are screening for in 2026:

Not "do you use AI" because everyone does.

They want operators who design systems where AI handles volume and humans handle judgment.

Build your proof of that capability now. The interview happens later.

If this reframed how you think about AI in GTM, follow me.

If you can't describe your QA gates in 4 lines, you're not ready for agents.

Most teams skip this.
Then a bad output reaches a customer.
Then trust collapses.

Here's the 4-check model to use before anything AI-generated goes external:

The "just automate everything" crowd has never dealt with a compliance flag at 2 AM.

AI throughput is real.
AI errors are also real.

The teams winning with agents are not the ones moving fastest. They are the ones who built quality control before they scaled.

I call it the DEVA Gate:

Drift Check. Escalation Trigger. Voice Alignment. Accuracy Validation.

Run these before any AI output touches a customer, a prospect, or a stakeholder.

Here is what each one means in practice:

D is for Drift Check.

Is the AI still doing what it was designed to do?

Outputs drift when prompts age, data changes, or edge cases accumulate.

Weekly: Pull 10 random outputs and read them. Ask "Would I send this?"

If the answer is ever no, your prompt needs updating. Not your workflow.

E is for Escalation Trigger.

Not every output should auto-send. Some need human review.

Define your escalation criteria before launch, not after something goes wrong.

Examples: a new account with no prior engagement, legal language in the output, or an anomalous confidence score.

If it hits a trigger, it goes to the queue. Not the customer.

V is for Voice Alignment.

Does this sound like a human from your team wrote it?

AI is a throughput engine. Your brand voice is the constraint.

A practical model: AI drafts, a designated reviewer validates tone, and a final approver handles anything novel.

Most content never needs escalation. The exception-handling is what matters.

A is for Accuracy Validation.

Especially critical for personalization.

Name right. Company right. Industry right. Offer appropriate for their stage.

One wrong field in a personalized message does not just look bad.

It signals that your "personalization" is automation wearing a mask.

Here is the practical division of labor that works:

AI drafts at volume.
A QA layer validates against the checklist.
The operator reviews exceptions and sets new templates only.

This is not about distrust. It is about throughput with guardrails.

The QA layer is not slowing the system down. It is the reason it does not fall apart.

Agents without QA gates are just fast mistakes.

Build the gates before you build the automation.

Define what "good" looks like. Codify it. Train your reviewer on it. Then scale.

The goal is not autonomous AI. The goal is trustworthy AI.

I publish practical AI workforce content for operators every week.

Follow if you want frameworks, not theory.

Reply with the one QA step your team currently skips. I am building a pattern library.

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