No surprise there, because AppSec is a multi-billion-dollar market and still growing fast, with projections putting it north of $50 billion in the coming years as software footprints expand and risk surfaces keep multiplying (in AI era especially).

This kind of TAM attracts new players eager to challenge how the industry works, with incumbents trying to defend their ground. And this is where things start to blur, because very different forces get grouped together and discussed as if they were the same shift.

On one side, AppSec vendors are adding AI to scanners, workflows, and dashboards in an effort to reduce noise, speed up triage, and make existing programs easier to operate at scale. This effort matters, but it mostly optimizes what already exists.

On the other side, AI-native IDEs are challenging the basics - changing how code gets written in the first place, shaping decisions long before a pull request is opened or a CI pipeline has a chance to run. They are not trying to “do security”, they are quietly altering what reaches security at all.

As a marketing leader who has spent years working with security and developer-focused products, I see this confusion surface repeatedly in conversations with founders and security leaders, especially when we talk about positioning, buyer expectations, and why otherwise strong products struggle to explain their value once AI-native tooling enters the picture.

Both evolutions touch the same lifecycle, but at very different points, with different incentives and very different buyers. Treating them as one trend blurs the real shift and leads to product strategies and go-to-market narratives that sound right, but do not land.

So rather than adding another hot take about AI, I want to map out what’s actually shifting in AppSec, why AI-native IDEs change the game earlier than most security teams realize, and where go-to-market narratives start to break if you miss that split.

Two Parallel Evolutions, not One

When people say “AI is changing code security,” they are often talking past each other, because they are pointing at different changes happening at different points in the lifecycle.

What’s actually unfolding is not one unified shift, but two distinct evolutions happening in parallel.

The first evolution is downstream. AppSec platforms are evolving from pure detection engines into execution systems. The focus is moving away from finding more issues and toward prioritization, remediation guidance, workflow automation, and proof of impact. This evolution is driven by organizational needs: auditability, policy enforcement, cross-team coordination, and the ability to demonstrate control at scale. Adoption remains deliberate, top-down, and justified at the company level.

The second evolution is upstream. AI-native IDEs are changing how code gets written in the first place. Instead of reacting to issues later in CI or security scans, developers receive real-time feedback while they work: suggestions, refactors, preventive guidance. Value is immediate and personal. Adoption is bottom-up, usage spreads because it helps developers move faster, and security outcomes emerge as a side effect rather than an explicit goal.

“We’re seeing PRs get bigger and more frequent with AI-assisted coding, and our security process isn’t designed for that scale yet.”

— VP Engineering, public Reddit AMA discussion

Both evolutions influence security outcomes, but they operate at different moments, optimize for different incentives, and follow very different adoption paths.

Most go-to-market confusion in this space comes from treating these as a single market motion. They are not. And collapsing them into one story is where positioning, messaging, and buyer expectations quietly drift out of alignment.

Inside AppSec: From Scanners to Execution Systems

Detection is no Longer the Nottleneck

What was the traditional role of AppSec tools supposed to be?

For years, it was straightforward: scan code, surface vulnerabilities, and rely on downstream teams to decide what mattered and what to fix.

That model no longer holds. Most AppSec tools already find far more issues than teams can realistically deal with. Coverage is rarely the limiting factor anymore, while throughput is.

Security teams are buried under findings, engineering teams are overwhelmed by false positives and noisy results that are hard to prioritize or trust, and the coordination required to assign ownership and push fixes through the system often costs more time than the fix itself.

AppSec’s Bottleneck Has Moved to Execution

This shift defines where AppSec product investment has moved over the past few years: from better scanners into improved triage automation, prioritization and reachability analysis, autofix PRs, backlog reduction campaigns, and increasingly agentic workflows that try to move issues through the system with less human coordination.

Of course, the scanners themselves are not yet obsolete, but the operating model around them is moving in that direction. In 2026 more alerts are no longer a sign of software maturity. Instead, they erode trust with engineering teams, especially when security could not help teams decide what mattered or how to move faster.

That is why the winning AppSec narrative is no longer about finding more issues. It is about closing the loop reliably, reducing friction between teams, and proving that security can keep up with delivery instead of slowing it down.

And that raises the next question - how does AI change buyer expectations?

What AI Is Actually Doing to AppSec Right Now

From a market perspective, it’s easy to see why vendors are bullish on AI in AppSec. This is a large, competitive market with slowing differentiation at the scanning layer, rising buyer fatigue, and increasing pressure to prove real outcomes. AI is not just a product feature here, it’s a strategic lever to defend market share, increase attachment rates, and reset value narratives for the incumbents.

“AI is helpful, but it also creates a lot of cleanup work. Security teams still have to deal with the mess.”

— Security leader, LinkedIn discussion

If you look at what the established players are actually doing, the pattern is hard to miss. Checkmarx acquiring Tromzois a bet on workflow ownership - automating triage, prioritization, and remediation to move AppSec up the value chain from alerting to execution. GitHub follows the same logic from a platform angle, pushing Copilot Autofix deeper into CodeQL and tracking its impact in Security Overview metrics, because fix throughput is a business metric that reinforces GitHub’s default position and drives spend consolidation.

On the developer-first side, Snyk is openly bullish on AI as a strategic advantage, with its AI Trust Platform and agentic positioning around Snyk Evo focused less on discovering new vulnerabilities and more on protecting its core ICP: developers who want security to move at the speed of delivery. Even Semgrep is reinforcing the same shift, using AI in Semgrep Assistant to drive triage, confidence scoring, and decision support, deliberately moving the value conversation from “how much we found” to “how much we helped you decide.”

From a marketer’s perspective, the signal is clear: scanning has commoditized. AI is now being used to reclaim differentiation at the execution layer - reducing noise, accelerating remediation, and embedding security decisions closer to where work actually happens.

The vendors most likely to win are not the ones shouting “AI-first,” but the ones using AI to own workflows, improve fix velocity, and align tightly with how their ICP actually measures value.

AI-native IDEs: the Shift Happening Hefore AppSec Even Shows Up

So far, we’ve been talking about incumbents and how AppSec vendors are trying to fix execution downstream. But there’s a parallel shift happening upstream, driven by a new breed of tools that don’t present themselves as security products at all: AI-native IDEs.

AppSec is a large, established market with predictable growth, while AI-native IDEs represent a smaller but rapidly expanding category, growing fast because they reshape how developers work.

Tools like Cursor, Windsurf, and AI-first experiences inside editors are no longer just about faster autocomplete. They increasingly understand entire codebases, reason about developer intent, propose refactors across files, and act as conversational reviewers while code is being written. That fundamentally changes when feedback happens.

What’s changing: instead of writing code, opening a pull request, and seeing warnings later in CI, developers now get inline critique, suggested rewrites, and preventive guidance as they type. Feedback moves earlier, before mistakes are baked into the codebase or handed off to security tooling.

From fewer alerts downstream we’re getting fewer mistakes created upstream. But as nice as it sounds, that prevention story is still uneven and incomplete.

AI-native IDEs are highly dependent on context quality, training data, and developer judgment, and they often optimize for plausibility over correctness. They can prevent obvious errors, but they can also confidently suggest subtle mistakes, insecure patterns, or changes that look right locally and break assumptions elsewhere.

They also lack shared accountability. What gets prevented depends heavily on how individual developers interact with the tool, which models are enabled, and how much trust is placed in suggestions. That makes outcomes inconsistent across teams and hard to reason about at an organizational level. Prevention improves velocity, but it doesn’t guarantee correctness, coverage, or policy alignment.

So while AI-native IDEs increasingly shape where security decisions start, they don’t yet offer a reliable way to govern or verify those decisions at scale. And that tension - between early prevention and downstream accountability - is exactly where the next phase of code security friction is likely to surface.

AI-native IDEs are powerful at the individual developer level, where context is local and feedback is immediate, but they don’t replace large-scale AppSec systems built to operate across many repositories, teams, and environments. IDE-level prevention improves moment-to-moment code quality, while organizational security still depends on cross-repo visibility, policy enforcement, and shared accountability that IDEs don’t yet provide.

How AppSec Buying Decisions are Actually Made

Application security is a multi-billion-dollar market growing at double-digit rates, with incumbents expanding their platforms and challengers pushing hard on speed, developer experience, and AI-driven workflows. On paper, it looks like a healthy, competitive space.

In practice, security buying is far less dynamic than market maps suggest. Most AppSec programs follow a predictable path. Teams start with platform defaults, add point solutions only when pain becomes impossible to ignore (which happens more often on the enterprise level), and then consolidate aggressively once tool sprawl becomes visible. Very few organizations approach AppSec as a clean-slate evaluation.

The real go-to-market consequences? Best-of-breed rarely wins by default: incremental value has to be obvious and immediate, and anything that introduces friction has to justify itself. Messaging that assumes an open comparison of features often misses how decisions are actually made.

In this environment, AppSec buying is less about features and more about justification, consolidation pressure, brand reputation and certifications, and how little resistance a solution introduces into an already overloaded system.

The Hard Parts No One Likes to Talk About

Right now, the market is in an awkward in-between state. Prevention is improving upstream, but governance and accountability are still downstream, and the connective tissue between the two is thin. AI-native IDEs influence decisions that AppSec teams are later asked to own, without always providing the context, traceability, or consistency needed to reason about those decisions at scale.

This creates real operational tension. Security teams are left with fewer issues overall, but the ones that remain are harder to triage, harder to explain, and harder to standardize. At the same time, developers are increasingly guided by tools that optimize for local correctness and speed, not for organizational policy, long-term risk, or cross-repo impact. That gap shows up later as surprises, misalignment, and debates that are harder to resolve than classic false positives ever were.

There’s also a maturity gap. AI behavior varies wildly by team, model configuration, and usage patterns, which makes outcomes inconsistent across an organization. What one team “prevents” upstream may still show up downstream elsewhere. That unpredictability makes it difficult to set expectations, define ownership, or confidently automate decisions without introducing new failure modes.

And from a market perspective, this ambiguity is uncomfortable. Buyers are being asked to trust systems that are still evolving, vendors are racing to claim AI leadership without clear standards, and many GTM narratives promise certainty in a moment where the industry is anything but settled. The next phase of AppSec will be shaped as much by how teams navigate this uncertainty as by how fast the technology improves.

What This Shift Changes for Go-To-Market and Product Strategy

As prevention moves upstream, the traditional AppSec go-to-market playbook starts to misfire. Messaging built around coverage, finding counts, and “we catch what others miss” lands flat, because buyers are experiencing less pain at the point of detection.

That shift also fractures the buying surface. Developers feel value first through fewer interruptions and faster local feedback, while security leaders remain accountable for a smaller but more complex set of residual risks across the organization.

GTM motions that assume a single buyer, a single pitch, or a clean evaluation struggle, because value is now distributed across personas and stages of the lifecycle.

Product strategy is having it’s own moment: AppSec teams are investing more in execution primitives: prioritization logic, fix orchestration, workflow automation, and tighter integration with IDEs and PRs. In practice, that means fewer “net-new findings” announcements and more focus on making security decisions easier to act on and harder to ignore.

The implication is uncomfortable but clear. AppSec vendors are no longer selling a standalone tool, they are selling a system that will coexist with AI-native IDEs, platform defaults, and bottom-up developer workflows.

Who wins in the next phase?

The ones that understand this redistribution of responsibility and design their positioning, product surface, and go-to-market motion around the new trends. Not louder AI claims, but сlearer value, tighter execution, and a sharper read on how decisions are actually made will help GTM strategy stand out.

This is the lens I keep coming back to when working with teams in this space.

I’ve worked with early-stage teams across different pricing motions - some PLG-first, some API-based, some open-core. And here’s the pattern that keeps coming up: the pricing model you pick in the early days doesn’t just shape your revenue—it shapes your runway, your roadmap, and your reputation with buyers.

Especially if you’re playing in the AI or developer tooling space, where copying functionality is trivial and infra costs rack up before your Stripe account does.

More than once, I’ve seen open-source become a trap. A team gives away too much for adoption, and when it’s time to sell into enterprise, they realize those companies are already using the free version—and have zero interest in paying for “upgrades” they’ve already hacked around.

That’s not a GTM problem. That’s a pricing blind spot.

If you’re not thinking about monetization from day one, you’re not building a product—you’re subsidizing someone else’s.

The good news? You’re not stuck.

Pricing isn’t a one-time bet—it’s a lever you get to design, test, and evolve. But it has to be intentional.

You can’t just copy Stripe’s tiers, slap on “Pro” and “Enterprise,” and call it a day. (Unless your business model is confusion.)

What follows isn’t a pricing formula. It’s a stack of real strategies I’ve seen work—especially for teams building in the wild world of PLG, AI, and open-source. Pick what fits. Combine where it clicks. And if all else fails, at least you’ll have a better plan than “We’ll figure it out after Series A.”

10 Pricing Strategies That Don’t Suck (Even If You’re Open Source, PLG, or Shipping AI Wrappers)

Pricing isn’t just about revenue. It’s positioning. It’s funnel design. It’s survivability. And in the AI era, where copycats move faster than your Notion updates, you need pricing that does more than look good in a pitch deck.

Here are strategies that can actually work—especially for founders juggling open-source traction, PLG growth, and high infra costs.

Let’s go deeper into the first three, because they’re often the deciding factor between “dev love” and “burnout at scale.”

Share this post with a founder or dev team still figuring out their pricing strategy.

1. Infra-Tiered Pricing

💡 TL;DR: Price based on what actually costs you money - tokens, GPU time, API calls, minutes of video generated.

It’s not sexy, but it’s sustainable. You don’t need to explain features - you just need to not go broke.

🔍 What this solves:

• Prevents free users from tanking your infra bill

• Aligns pricing with real usage (and value delivered)

• Lets you be generous without being reckless

🧪 Real Example (OpenAI):

• You get a few dollars of free usage.

• After that? You pay by token, per model tier.

• No confusion. No surprises. Just: use more → pay more.

[OpenAI’s pricing page with GPT-3.5 vs GPT-4 token tiers]

🧠 Pro Tip:

Don’t charge for features. Charge for the output. If your user sends 2M tokens through your orchestrator, that is the moment of value.

2.Feature vs Control Split

💡 TL;DR: Open-source the engine. Monetize the dashboard, monitoring, role controls, alerts—anything a team needs to stay sane.

🔍 What this solves:

• Devs get to explore and use your tool freely

• Teams, companies, and enterprise buyers still have reasons to upgrade

• You don’t have to cripple your OSS to monetize

🧪 Real Example (Langfuse):

• OSS covers core LLM observability logic

• Hosted version adds role-based access, dashboards, team workflows

• Paid tiers kick in for collaboration and scale

Check out Langfuse pricing page showing open-core vs hosted features

🧠 Pro Tip:

Don’t put login buttons behind the paywall—put control behind the paywall. Give usage. Charge for governance.

3. Dev Team vs Org-Based Plans

💡 TL;DR: Not every user is worth the same. Solo devs ≠ startup teams ≠ enterprise security buyers. Don’t price them like they are.

🔍 What this solves:

• Reduces friction for early adoption

• Encourages small team upgrades organically

• Creates logical upgrade moments without sales calls

🧪 Real Example (Replit):

• Solo devs build for free

• Add team features? Need a Pro or Team plan

• Seamless growth from individual → team → org

Screenshot: Replit’s pricing table

🧠 Pro Tip:

Set limits by project, collaborators, or integrations—not just usage. Most users don’t care about tokens, but they do notice when they hit “Add Member” and can’t.

Should I start with a free plan or go premium from day one?

In my experience: if you’re building a technical product—especially in devtools, infra, or AI - you do need to give some stuff away. But not because freemium is trendy. Because awareness is expensive, and distribution is brutal. Free earns you usage. Usage earns you feedback. And feedback earns you credibility.

But here’s the catch: free can’t be forever. And it definitely shouldn’t be everything.

The smartest early-stage teams I’ve worked with treat “free” like a channel, not a pricing model. They make sure what’s free is:

• ✂️ Trimmable later (no core infra costs or entitlements you’ll regret)

• 🎯 Targeted at the right persona (not random tire-kickers)

• 🚪 Directional toward conversion (with natural upgrade moments)

For example: you might give away self-hosted access or a basic agent config builder—but gate the hosted control plane, team collaboration, premium model access, or enterprise integrations.

Think of your free tier as top-of-funnel UX, not a gift. It should earn its keep by qualifying users, teaching them your value, and pointing them toward the plan that actually pays your GPU bill.

Each strategy includes:

🎯 Who it works for

🧪 Detailed real-life use case

📸 Visual example with screenshot prompt

🧠 Includes: Notion pricing planner, AI calculator sheet, OSS monetization matrix, and more.

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Let’s be honest: the hardest part of early-stage marketing isn’t “what channel should I use?”

It’s:

• What the hell do I say?

• Who actually cares?

• And how do I test that yesterday, without sounding like a try-hard on LinkedIn?

I’ve been there — on both sides. As a fractional CMO and growth partner, I’ve worked with early-stage teams where the funnel looked more like a fog machine. Endless noise, no direction. And when I say “growth plan,” I don’t mean spending two weeks renaming Notion folders.

If you’re building something new and searching for traction, you don’t need another 45-tab Miro board.

You need signal. You need structure. You need speed with a spine.

That’s what these frameworks are for.

What these frameworks actually help with

I’ve spent years helping technical founders turn “we built a thing” into “we’re getting users.”

These five frameworks are my response to all the fuzzy funnel moments I’ve had to fix — too much content, wrong message, great product, zero pull.

Each one is designed to help you:

• Find early traction (not just “create content”)

• Say sharper things in smarter places

• Use speed strategically, not chaotically

If you’re trying to get your first 100 users, build narrative clarity, or stop reinventing your positioning every time you write an email — you’ll want to bookmark this.

Let’s break them down.

TL;DR: You don’t need a massive team — you need the right systems and someone to help you run them

I work with early-stage founders to cut through the noise, sharpen messaging, and launch fast GTM experiments that don’t die in Notion.

In this post, I’m sharing five AI-powered frameworks I use to help you find early traction without hiring a full marketing team or spinning in strategy loops.

1. SCOPE – Quickly turn market signals into testable campaigns with messaging, offers, and channel focus

2. The Alignment Flywheel – Keep your story tight across all touchpoints, even as you move fast

3. Message Engine – Test value props and sharpen narrative before committing to a full relaunch

4. Content Barbell – Balance SEO and social with one idea → many outputs (without burnout)

5. The Traction Stack – Run structured growth tests that actually scale instead of random tactics

You don’t have to do this alone. I bring the structure, the systems, and the speed — so you can focus on building.

1. SCOPE – AI-Powered Campaigns That Don’t Waste 3 Months

Let’s say you’re pre-traction.

You’re staring at a blank doc titled “launch campaign.” You’ve got vague buyer personas, a gut feeling about Reddit, and 20 half-baked content ideas in a Notion graveyard.

SCOPE is the framework I use to build actual campaigns that ship fast, sound sharp, and aren’t pulled out of thin air.

It stands for:

Signals · Content · Offers · Placement · Execution — and AI plugs into every part.

What problem it solves:

You’re not sure what message to lead with, what offer to pair it with, or where to test it.

SCOPE gives you a way to structure all of that in one repeatable loop.

How AI helps:

• Finds early signals (complaints, questions, trends) across Reddit, Hacker News, YouTube comments, forums

  → Tools: Octolens, GummySearch, Perplexity, Glasp, Hacker News search

• Distills market language from transcripts, tweets, or reviews

  → Tools: Claude, ChatGPT, Fireflies.ai, Fathom

• Drafts fast variations of hooks, explainer copy, ad headlines

  → Tools: Custom GPTs, Copy.ai, Jasper

• Suggests offer ideas from trends (e.g. “everyone’s asking for [X], give them a checklist”)

  → Tools: ChatGPT, Airtable for clustering

• Rewrites content per channel — so your cold email doesn’t sound like a blog post

  → Tools: ChatGPT + Typefully / Taplio

• Automates campaign setup

  → Tools: Zapier, Notion, Airtable templates

Use case: A 3-months brand campaign now takes a week — with more creative variation.

If you want the full step-by-step behind how I run SCOPE in real founder workflows — I share the deep dives, templates, and system breakdowns inside the paid newsletter. Worth it if you’re building for speed and signal.

2. The Alignment Flywheel – Keep Your Messaging From Falling Apart

The faster you build, the messier your messaging gets.

One week you’re promising speed. Next week, your product page leans into security. Your emails sound like a SaaS founder, your ads like a self-help coach. No one’s on the same page — because there is no page.

The Alignment Flywheel keeps your message consistent across channels as you scale.

It helps you turn raw insight into reusable message blocks, push those across touchpoints, and evolve your story without rewriting your positioning doc every two weeks.

How AI helps:

Extracts repeated themes from calls, support convos, Slack threads, and demos

→ Tools: Claude, ChatGPT, Fathom, Fireflies.ai

Rewrites your core narrative for different personas, channels, or tones

→ Tools: Custom GPT, Jasper, Notion AI

Audits message drift by comparing what’s working vs. what you’re actually saying across your funnel

→ Tools: Claude, ChatGPT, Loom transcripts, landing page crawlers

Remixes messaging across assets like outbound, onboarding, and paid campaigns

→ Tools: ChatGPT, Taplio, Typefully, Copy.ai

Organizes message blocks into a single source of truth

→ Tools: Notion, Airtable, Coda, Supernormal

What problem it solves:

You’ve got traction, but the story isn’t scaling with you. This framework helps you stay clear, consistent, and aligned — without slowing down.

Want to see how I run this with early-stage teams? The paid newsletter includes a breakdown of how I structure messaging hubs, run async audits, and keep everyone saying the same sharp thing.

3. Message Engine – Sharpen Fast, Test Faster

You’ve got five versions of your value prop, and your team can’t agree on which one actually makes sense. Meanwhile, your website says one thing, your cold emails say another, and your latest deck starts with “We’re revolutionizing…”

Read more

Most are just… calendars in disguise.

The startup default is to build a funnel that looks clean on a slide, tracks “awareness → revenue,” and gets updated every Tuesday at 10 a.m.

But ask anyone what it’s actually telling them, and the answers get foggy. Fast.

Because most funnels don’t generate signal.

They just create motion. Meetings. Reports. Activity theater.

Let’s fix that.

Funnels Shouldn’t Be Passive

If your funnel isn’t showing you where trust breaks, where your story fails, or where users stop caring — it’s not doing its job.

The goal isn’t just to convert.

The goal is to expose friction so you can act on it.

A signal-rich funnel gives you:

• A map of drop-off with context

• A way to tie behavior to narrative

• A faster path to alignment between teams (and reality)

Let’s look at what this actually looks like in practice.

Example 1: The Free Trial That Was Lying to You

DevTool startup. Free trials are flowing in.

30% activate. “Not bad.”

Until we start treating the funnel as a signal engine:

• 70% of trials never connect a repo

• 55% open the onboarding email, but don’t click anything

• 80% of successful activations come from just one segment: AI-focused teams

Suddenly the funnel isn’t a static stat — it’s a story:

• We’re overpromising ease-of-use in ads

• Our onboarding email talks about features, not friction

• Our ICP isn’t “developers” — it’s a very specific subset

Same numbers. New lens. Now we’re not reporting — we’re redirecting.

Example 2: The Demo Funnel That Blames the Wrong Thing

Vertical SaaS with an outbound-heavy funnel.

100 demos/month → 30% show → 10% close.

Sounds predictable. But once you peel back the layers:

• 40% of no-shows come from one SDR

• Highest-performing bookings use one specific email template

• SQLs who played with the ROI calculator convert 5x faster

So no — it’s not a “sales problem.”

It’s a misaligned message → asset → handoff problem.

This isn’t about building a better dashboard.

It’s about building a funnel that reflects how people actually buy — and why they bounce when they don’t.

What to Do With This

If you want your funnel to do more than eat budget and spit out KPIs:

• One funnel = one narrative. If it’s not consistent, it’s not useful.

• Label the friction. Don’t just measure drop-off — diagnose the break.

• Kill the vanity stages. No one cares about “MQL to SAL” if nothing actionable lives there.

• Use it to drive conversations. If marketing, product, and sales aren’t seeing the same gaps — the funnel’s a mirror, not a map.

Book your free funnel teardown now

TL;DR

A funnel that doesn’t generate insight isn’t a funnel.

It’s a burn rate with nice formatting.

Coming up next:

Positioning isn’t a tagline.

It’s a decision filter.

See you soon.

“The future is there... looking back at us. Trying to make sense of the fiction we will have become.” — William Gibson

Everyone’s got their hot take on AI.

Most of them are noise.

On one end, there’s the productivity porn: “I built an AI empire in 72 hours using ChatGPT and 14 Chrome extensions.”

On the other, the doomers whispering about jobless marketers and prompt-replacing overlords.

Meanwhile, the real shift is quieter — and smarter.

AI isn’t replacing marketers.

It’s redefining what great looks like.

Founders aren’t asking, “Should we use AI?”

They’re asking, “Why is our messaging still slow?”

“Why are we guessing what works?”

“Why does it take a team of five to run a funnel?”

Here’s the unlock:

They reach for it instinctively.

To test. To think. To prototype. To scale.

The AI-First marketer isn’t the person who knows the most tools.

It’s the person who knows how to think in systems, build for momentum, and ship faster - without losing the thread.

This is the inflection point:

Not AI vs. humans.

But AI as leverage — and the marketers who know how to wield it win.

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What Is an AI-First Marketer?

First off - no, it’s not the person sending ChatGPT “write me viral LinkedIn post” and calling it a growth strategy.

And no, it’s not your colleague who downloaded 12 AI tools and built absolutely nothing.

Being AI-First isn’t about flexing your stack. It’s about defaulting to leverage.

When a new challenge shows up, your instinct is:

“Cool. How do I loop this, automate it, scale it, or make it smarter with AI?”

An AI-First marketer is not:

• A prompt monkey cranking out SEO sludge

• A growth hacker rebranding spreadsheets as “LLM automations”

• A startup mascot who spends more time screenshotting GPT responses than shipping work

Here’s what they are:

• A systems builder with creative taste

• A marketer who can write and wire things together

• Someone who actually understands how ideas turn into motion

They can:

• Turn a messy founder rant into a narrative and a Notion workflow

• Spin up 15 ad angles using customer pain, not “brand adjectives”

• Catch GPT when it’s hallucinating — because they’ve actually read their own outputs

• Stack tools like Clay, Zapier, Notion, and GPT to build growth loops that don’t suck

They don’t worship the tools — they orchestrate them.

They don’t say “let’s test this” and then wait three weeks.

They say “brb, shipping a working draft in 10 minutes and a meme while it renders.”

AI-First marketers move fast.

Not because they’re hustling. But because their systems are.

Voices from the Fields

“AI marketing isn’t just a thing, it’s basically the future. The biggest shift I’ve seen is AI taking the guesswork out of ad performance. We’re testing AI that not only optimizes ads in real-time but also predicts which creatives will perform before we even launch them. It’s making campaign management way more efficient.”

“AI marketing tools are a full-time job to keep up with … but optimising campaigns using reinforcement learning is next-level.”

“AI will not take over digital marketing… [it] will likely redefine their roles. …Digital marketers of the future will focus more on creativity, strategy, and ethical considerations while leveraging AI for data analysis, personalization, and automation.”

“Honestly, if you’re still writing every line from scratch in 2025, you’re either doing it for the love of pain or seriously underestimating your time. […] The real skill now isn’t avoiding AI—it’s knowing when to use it and when to step in so your copy doesn’t sound like it came out of a corporate blender.”

Turns out, the AI-First marketer isn’t a mythical job title — it’s already a Reddit regular with three browser tabs open, one broken Zap, and a strong opinion about reinforcement learning. Some are automating ad campaigns like it’s NBD, others are drowning in tool fatigue, and a few are just trying to stop their copy from sounding like it was written by a well-meaning robot with zero social life. The vibe? AI isn’t replacing anyone. It’s just making it painfully obvious who’s thinking in systems… and who’s still manually updating the Monday board.

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• A single roll-up banner

• A jar of branded mints

• And three people standing around like they’re waiting for their mom to pick them up

You didn’t pay $15K for “brand presence.” You paid to build pipeline and start conversations — or at least that’s what your board deck said.

So here’s a better playbook for 2025. One where events don’t suck the soul out of your budget.

1. Go niche or go broke

Big expos are a dopamine trap.

They look impressive, feel busy, and deliver almost nothing.

Instead:

Find focused rooms where 80% of the people are actually your ICP.

Yes, the badge count is smaller. But so is your waste.

London, New York, Boston, SF? Still full of micro-events and founder dinners where real deals happen — usually over pizza and too much espresso.

2. Speak, don’t just stand there

Here’s a wild idea: say something useful.

Don’t just rent space — own it. A good talk travels.

Especially if it’s not a thinly veiled demo with 45 slides and a chart no one asked for.

Give people a story, a lesson, or a point of view they can disagree with.

Bonus: clip it for LinkedIn and watch your reach do something your booth never could.

3. Swag that slaps (and doesn’t end up in hotel bins)

Stop handing out stress balls.

If you’re giving stuff away, make it:

• Useful

• Shareable

• Slightly unhinged

Think:

→ A sarcastic “Ask me about my ROI” tote

→ Founder meme tees

→ Mini zines about “why your funnel is broken”

If it makes people laugh and remember you? You win.

4. Don’t send a team. Send a squad with a game plan.

You brought 5 people and 0 outcomes. Congrats on the company field trip.

Here’s the fix:

• Pre-event playbook: Who’s your target? What’s the offer?

• Clear KPIs: Convos, demos booked, deals sourced

• Real follow-up plan: No leads left in the abyss

Because wandering the floor and handing out stickers is not a growth strategy.

5. Booth? Make it weird enough to remember.

You spent money — now earn attention.

• Run a coffee bar with painfully hip beans.

• Offer tarot card pipeline readings.

• Host “founder therapy” in a fake therapist chair.

Whatever it is, make them stop, talk, and take a photo.

A booth is a stage. Use it.

6. Bring in local voices who already have trust

Influencers don’t have to mean 100k followers and TikTok dances.

Micro-creators, community hosts, or newsletter authors with your audience? Gold.

Invite them to your booth. Give them a mic. Let them be the draw.

Just don’t make them say “industry-leading synergy.”

7. Send people who turn handshakes into CRM entries

Your content manager is lovely. But they don’t close deals.

Bring sales. Partnerships. Biz dev.

People who know how to listen, qualify, and log.

Otherwise, you’re paying $800 a night for vibes.

8. Yes, events are brand plays — but also… please measure something

Not everything needs a UTM. But if you walk away with no tagged leads, no sourced opps, and no clear follow-up, you didn’t go to an event — you went to summer camp.

Tag every convo. Track the funnel. See what closed.

Brand ≠ blind.

TL;DR — The 2025 Event Survival Guide

Offline events aren’t dead. They’re just being used wrong.

Here’s the checklist to get it right 👇

Want the Full Event Playbook?

I put together a tactical bonus for this post:

✅ The 2025 Event Playbook — a 5-page PDF with:

• A “Should we even go?” decision tree

• Budget-to-pipeline calculator

• Messaging checklist for talks and swag

• Booth & speaker prep cheat sheet

📊 Airtable Event Tracker Template — built to:

• Log convos, leads, follow-ups

• Track by team member

• Attribute pipeline, not just vibes

Plug, customize, and make every event count.

said “Every marketing channel sucks right now.”

And yeah — he’s not wrong. Everyone’s in the same inboxes, fighting over the same tired feeds, remixing the same templates from 2019. Somehow still thinking it’ll break through? That’s delulu, babe.

So the obvious question becomes: What’s left?

And if you’re like me, this is the part where your brain politely suggests “carrier pigeons” or “fax campaigns” - because surely we’ve run out of options, right?

But actually, no.

There’s still leverage hiding in plain sight.

Because while the channels might be loud, formats — the how behind the message — still give you room to play.

Same tools. Same audience. Different outcomes.

• An email doesn’t have to be ignored — it could be a Notion walkthrough someone actually forwards.

• A LinkedIn post doesn’t have to disappear into the scroll — it could be a teardown that gets shared in Slack and sparks demo requests.

• Even a product demo doesn’t have to feel like homework — it could be an instant, no-login tour that does the selling before sales even shows up.

The difference? Not the channel. The format.

And that’s what we’re getting into next:

Real formats that are actually working — and how teams are using them to stand out without chasing whatever platform’s trending this week.

Why Formats Matter More Than Channels

Channels used to be the edge. Being early to LinkedIn, paid social, or even SEO gave you leverage. But now? Everyone’s everywhere. The playing field is flat - and crowded. What used to be a competitive advantage is now just the minimum.

That’s why format matters more. Because how you show up is the only part that’s still uniquely yours. Same post, same inbox, same feed — but a teardown, a behind-the-scenes walkthrough, or a sharp carousel lands very differently than yet another “quick thought” thread.

As puts it, “Products are built to fit with channels. Channels do not mold to products.”

It’s the same with formats. You can’t force attention — you have to earn it with delivery that fits how people actually consume.

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Format-First Plays That Still Win in 2025

Let’s get honest: most marketing formats age like milk — fast, sour, and with a weird aftertaste if you let them sit too long.

But a few? They evolve.

When executed with clarity, speed, and specificity — they cut through the noise and build momentum.

Here’s my take on what’s still working, what’s just getting started, and what’s criminally underused (especially if you’re an AI startup trying to stand out).

Live Founder Teardown Series

Nothing says “trust us with your GTM strategy” like watching someone sweat through a homepage audit on a Tuesday morning Zoom. But somehow, it works. Maybe that’s the point.

Real-time breakdowns of funnels or prompt flows are the new keynotes — punchier, raw, and way more clickable. Bonus points if you fix the mess live, using your own tool. Yes, it’s terrifying. That’s why it lands.

Strategic Partnerships That Actually Multiply

Some of the highest-leverage plays I’ve pulled off didn’t come from new channels — they came from the right partnerships. A well-matched tool, a smartly bundled offer, and a co-hosted session with actual value?

It can feel like a growth cheat code. I’ve seen prompt swaps, AI workflows, and demo bundles outperform entire outbound sequences. But it only works if the alignment is real. That means clear positioning, shared audience logic, and someone (ideally not me) keeping the Notion doc updated.

When it clicks, it scales. Just be ready to juggle landing pages, CTAs, post-webinar follow-ups, and at least one brand team’s “tiny feedback.”

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Interactive Embedded Demos

“Book a demo” is the new “check out our whitepaper.” Static, slow, and completely ignorable. Cutting edge startups let you touch the product before you talk to sales. Click-through, no login, straight into the magic.

If you’re not building this, you’re asking people to imagine value. Spoiler: they won’t.

AI-Powered Demo Toolkits

If your product is built on AI and your demo still looks like a 2016 screencast… why? Tools like Arcade are making it embarrassingly easy to storyboard and narrate flows.

If you’re building smart tools, the way you show them should reflect that — or at the very least, show that someone on your team knows how to use them with a bit of imagination.

Reddit Community Formats

Everyone says they’re “community-led” until it’s time to actually show up in the community. Reddit isn’t just for gaming threads and chaos anymore — it’s becoming a high-trust space for bottom-funnel buyers, and it’s indexed by Google andLLMs. If you can’t be helpful there, you probably won’t be helpful in the inbox either.

Creator-Led Micro-Explainers

Influencer marketing for SaaS used to mean 20-minute tech reviews on YouTube with zero emotion. That’s over. Now it’s a 2-minute raw screen-record with commentary like “honestly I didn’t expect this to work.” And somehow, that sells better.

Why? Because creators have what you don’t: trust and reach with a specific community. Borrow both.

Vertical-Niche Content

Here’s a wild idea: talk directly to the exact people you want to sell to. “AI for HR teams with no time to prompt-engineer” beats “AI automation for modern teams” every single time. The deeper you go, the more creative freedom you unlock. Specificity isn’t limiting - it’s liberating.

AI Repurposed Content Kits

I love this one because it saves your team from content burnout and proves you understand distribution. Start with one good piece of thinking, then let AI stretch it across formats: a blog, a carousel, a 45-second clip, a CTA for outbound. That’s not “repurposing.” That’s building leverage.

Prompt Libraries & Mini-Challenges

People don’t believe your AI works until it solves their problem. Prompt libraries give them a head start. Mini-challenges? Even better.

Make it a game: “Can our AI write a better onboarding email than your intern?” Spoiler: yes. But let them discover that on their own.

Build-in-Public Founder Logs

Transparency isn’t a virtue signal. It’s a strategy. Especially in AI, where trust is still earned. I’ve seen founders win loyal early users just by sharing bugs they fixed and prompts that failed. No polish, just presence. Build in public — and people will root for you.

AI-Repurposed Content Kits

This one’s pure leverage. You start with one solid idea — a product insight, a teardown, a hot take — and let AI spin it into multiple formats: a short video, a carousel, a blog draft, maybe even a few cold outbound hooks. I’ve used this to test tone, find traction, and avoid the “one-and-done” trap. It’s not about flooding channels. It’s about giving the same idea more ways to land.

Channels are distribution. Formats are differentiation.

The 5-Part Format Check We Use Before Hitting Publish

(Because vibes aren’t a strategy)

Before anything goes out — a post, a demo, a live session — me and the team give the format a quick gut check. Not in a big process-heavy way, just five fast filters we’ve come to trust.

It helps us catch the difference between “this looks fine” and “this could actually land.”

The goal isn’t to be perfect — it’s to avoid publishing something that relies entirely on the channel doing the heavy lifting. Because channels are noisy. But if the format has a hook, gives people something to do, earns some trust, and delivers a clear payoff? It tends to work harder, longer, and better than we expect.

This check has saved me from approving forgettable work more than once.

And when everything lines up — it’s usually the format, not the channel, that made it win.

⚙️ Format Flywheel Framework

Vibes aren’t a strategy. Before you publish anything — a teardown, a demo, a carousel - you need to know if the format can actually carry the weight.

That’s where this framework comes in.

The Format Flywheel helps you gut-check your creative before it hits send: Is there a hook? Will anyone interact? Does it earn trust? Can it spread? And most importantly — what’s in it for the audience?

When these five layers click, the format pulls its weight. When they don’t, no channel can save you.

Why Format Still Works — Even If Everything Else Is on Fire

I didn’t get into marketing just to run A/B tests and optimize conversion paths. I got into it because I liked the idea that one message — said well, delivered right — could shift how people think, act, or decide. From a small town in Europe, that felt like a kind of superpower.

Years later, not much has changed. Except now, we’re all flooded with content. Everyone’s running the same plays. Every channel feels maxed out.

But format — how we show up — is still where the difference happens.

It’s not just about reach. It’s about resonance.

That’s why format still works. Even now. Especially now.

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Eventually, I started asking what many of us are …

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But - beneath the noise and fake guru hype there’s a sliver of something real. When used right, AI doesn’t replace your brain. It frees it. It doesn’t generate genius. It accelerates judgment.

Most people are using AI to do mediocre work faster. The smart ones are using it to do better work differently.

Why AI sucks in marketing

The current state of AI in marketing is like watching someone try to microwave a steak and pretend it’s fine dining. Everyone’s automating “creative work” and somehow ending up with less creativity than ever. Teams are feeding prompts into LLMs, getting beige soup in return, and calling it innovation.

Marketers keep trying to use AI to sound “human,” which is adorable in a deeply concerning way. The output? Content that reads like it was generated by a tired intern who just discovered synonyms. And let’s not forget the design teams slapping AI into brand campaigns like it’s glitter glue. Just because the visuals are technically generated doesn’t mean they make sense - or match your brand, or look good, or do literally anything except exist.

We’ve also entered the era of AI goop. The internet is drowning in slop: AI-written articles, AI-voiced videos, AI-generated shorts, AI-created podcasts. It’s mass production without direction. Recruiters are hiring for “prompt engineering,” which is code for “please make the robot slightly less embarrassing.”

The robot isn’t your strategist. Stop pretending it is.

And in case you were wondering, yes - Google hates it too. Because when your AI blog post has all the depth of a puddle and none of the soul, Google doesn’t want to rank it. Shocking, I know.

The TL;DR? We’re using AI to fake value at scale. And it shows.

AI helped me save time, so I could ignore worse content faster?

Also AI gives me more time. Cool. But that doesn’t magically make my time valuable.

Here’s a genuine question I’ve been asking myself:

Who’s out here willingly listening to AI-generated podcasts? Like… unironically?

Because unless you’ve completely given up on brain activity for the day, I can’t see the appeal. These things aren’t conversations. They’re just verbal white noise. Background static with a slightly smarter accent.

It’s not even podcasting at this point. It’s audio doomscrolling. You’re not listening to learn anything (and I mainly spend time in this department to do that). You’re not engaged. You’re just passively letting synthetic voices spoon-feed you trivia until your will to live quietly exits the chat.

And yeah, I get it - sometimes you want something mindless. But maybe, just maybe, we don’t need to industrialize content emptiness.

Maybe we don’t need 10,000 AI podcasts reading Reddit threads and calling it insight.

So I’m asking myself: if I’ve got time, why spend it like that? What am I actually gaining by tuning in to a conversation that no one ever really had?

Spoiler: nothing.

But wait, we still have something good happening!

Where AI actually benefits marketing

Alright, credit where it’s due: AI isn’t all noise.

Occasionally, it does something useful. Like handling the boring stuff marketers pretend to love - spreadsheets, campaign reports, 47-tab competitor audits. Turns out, robots are pretty decent at busywork.

Used right, AI is less “creative partner” and more “intern that doesn’t talk back.” It drafts, analyzes, outlines, and crunches numbers without needing snacks or a motivational Slack channel.

The real marketers - the ones with a brain - use it to speed up grunt work so they can focus on strategy, storytelling, and not hating their jobs.

Some tools are even helping with campaign planning and SEO optimization. Fancy that. Real-time ad testing, content performance prediction, deep market scans… AI can stack up insights faster than your 17-person strategy team ever did. Just don’t expect it to come up with a tagline that doesn’t sound like it belongs on a shampoo bottle.

Bottom line: AI works best when it’s invisible.

It doesn’t replace your job - it just removes the part that made you want to rage-quit.

Where AI still sucks (for me)

Let’s talk about the “intelligence” part of artificial intelligence.

Because half the time it feels like it’s just confidently wrong. You ask it for research, and it throws out links that go nowhere. Literal 404s like it’s time-traveling back to the early internet.

And if you’re hoping it’ll match your tone of voice? Good luck. Unless your tone is “corporate LinkedIn post written by a mildly caffeinated someone,” it’s going to miss by a mile.

Even when it gets the facts mostly right, it’s surface-level fluff unless you’re feeding it prompts that look like a legal brief.

Speed? Sure. Quality? Not unless you micromanage it like it’s an unpaid intern who just lied on their resume. And every now and then it just… makes stuff up.

Full hallucination mode. Like, thank you for the confidence, but no, Shakespeare did not invent TikTok.

Where it actually shines (personal insights)

That said—when AI does work, it’s kind of wild.

As a brainstorming partner, it’s a solid 9 out of 10. Not because it has genius ideas, but because it forces me to think faster and weirder.

But the trick is, you need a framework. Otherwise, it’s like pouring gasoline on a sandbox and calling it fire.

It’s also secretly my English coach. I’m not a native speaker, so having something clean up phrasing or tighten structure without dumbing things down? Huge win.

And when I’m neck-deep in strategic work, AI gives me structure without slowing me down. It’s like having a second brain that’s good at outlining but bad at vibes.

Promo campaign ideas? It spits out 20 in 10 seconds. Most are trash. Some are gold. But that’s the game.

Also? It’s my Martech therapist. When I’m about to throw HubSpot out the window or break up with Zapier for the fifth time, AI helps me troubleshoot before I start crying into a Google Sheet. Not glamorous, but very real.

The “don’t get slop” framework for brainstorming with AI

(aka how to use a robot without letting it waste your time)

AI can help you go deep - but only if you stop treating it like a magic 8-ball and start treating it like a mildly competent intern with memory issues.

Here’s how I actually use it for meaningful, structured research and creative ideation.

1. Split your brain dump into small, painful chunks

Bad: “Help me build a go-to-market strategy.”

Better: “Let’s start by mapping target audiences. Then we’ll look at positioning. Then channels. Then funnel stages.”

Why it works: One giant prompt = one giant mess. Break big questions into tiny steps. More back-and-forth, yes. But also more signal, less sludge. Think of it like a slow roast - not microwave content.

2. Start with context, not a question

Bad: “What are the top marketing trends?”

Better: “You’re a growth strategist helping a B2B SaaS founder go from 0 to 500k ARR. What specific trends in paid acquisition are worth testing in 2025?”

Why it works: AI isn’t smart. It’s predictive. Give it a job, a role, and a scene to work in - or you’ll get advice that sounds like it came from a business horoscope.

3. Always ask for categories before answers

Bad: “Give me 10 campaign ideas”

Better: “Before we get ideas, give me 4 angles we can explore: audience pain, timing, incentives, and channels. Then ideate under each.”

Why it works: You want range and focus. Asking for buckets first stops the rinse-and-repeat answers and forces more thought per output.

4. Use competitors to anchor reality

Before asking for ideas do some deep-dive:

“Based on [competitor A, B, C], what are the common patterns in their campaigns? What are they missing that we could punch through?”

Why it works: AI works better with reference points. Giving it live market context keeps your ideas grounded - and sometimes even helps you zig where others are zagging.

5. Double tap with: “What would a contrarian say?”

After any list:

“Now challenge that - what’s missing, outdated, or plain wrong?”

Why it works: AI is a people-pleaser. Make it debate itself. You’ll uncover blind spots and sharper insights that feel like they came from a real strategist, not a content mill.

6. Ask for source logic, not just output

“Explain how you got this. What’s it based on - behavioral trends, case studies, assumptions?” (AKA give me proofs!)

Why it works: If it can’t justify the take, it probably hallucinated it into existence. And if that’s your idea of ‘research,’ you’re gonna have a bad time.

7. Summarize, then remix

Wrap it with:

“Summarize the top 3 insights. Then give me a weirder, riskier version of each.”

Why it works: Forces clarity, then creativity. You get one version you can ship and one that might actually be interesting.

final boss: use your brain

No matter how smart the prompt looks—don’t copy-paste your way into irrelevance. AI still hallucinates, misses nuance, and occasionally makes stuff up with the confidence of a guy who’s never been punched in a meeting.

Cross-check. Think critically. Own the take.

Otherwise, you’re just dressing up noise and calling it strategy.

Your next marketing hire will not be a prompt

“Your next marketing hire will be a prompt.” Cute line. Great for pitch decks. Still absolute nonsense.

It sounds bold until you realize it’s just tech guys cosplaying as marketers. A prompt isn’t a hire. It’s a sentence. It won’t catch a weak insight, question a bad brief, or tell you your funnel’s a mess.

A good marketer doesn’t just ask the tool for ideas. They shape the input, question the output, and turn advise into execution. That’s not a prompt. That’s judgment. And no matter how good AI gets, judgment doesn’t scale.

So no - your next hire shouldn’t be a prompt. It should be a human who knows when to hit backspace.

Here are the five lessons I learned while working in hyper-growth B2B SaaS startups and managing a marketing function.

Don’t look at demand gen marketing in isolation

Growing companies focus on demand generation, but building brand affinity can make your strategy even more effective. Some leaders think brand isn't as important in early stages, but that's not true.

In other words, marketing channels don't work in isolation - every touch of demand is a touch of brand, and every touch of brand is a touch of demand. The overall success of the marketing strategy and demand strategy will depend on the development of a holistic plan and the allocation of basic resources to brand activities.

Utilize both brand and demand optimization early on to build traction for growth.

How to Build Predictable Growth for Early Stage B2B Startups

Join my new cohort-based course to learn how to build predictable customer acquisition process for early stage startups. I'll provide frameworks to get your demand gen team up and running without you having to reinvent the wheel, including templates for things like Ideal Customer Profiles, KPIs for demand gen team and a list of essential tools for companies in the same stage of growth as yours.

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Overlooking the GTM motion while building your marketing strategy can be costly

Marketing in B2B can be different based on your GTM (go to market) strategy: self-service motion is totally different from a sales-led motion. This means to have relevant business impact, you need to align your marketing strategy, tactics, and team structure with your GTM strategy. 

The following factors can define your GTM strategy:

• You product plans/packages, 

• Average deal size, time to close, sales cycle, adoption, etc.,

• How your audience will use your product. 

By following your chosen GTM motion (sales-led, hybrid or product-led), marketing team will have different KPIs, lead scoring processes, chanells that impact business results, different types of content strategy and website CTAs.

Center marketing KPIs around right targets

Marketing planning and execution can be hampered when you don't identify the right goals. Setting clear marketing goals makes it easier to keep track of milestones, timelines, and objectives as well as achieve the desired business results.

Getting back to the GTM motion, KPIs differ for self-serve and sales-led motions and we should set them right.

In a product-led model, KPIs are normally centered around driving product-qualified leads, while in a sales-led model, they will be about handing qualified leads off to sales.

Product-led motion also means that besides firmographics and interactions with marketing assets, lead qualification and scoring will take into account product data. 

As long as the KPIs reflect the GTM strategy, the marketing function will be able to provide more value to the business and keep the team motivated, which will boost productivity and creativity.

Create a consistent CTA plan for your website

Website is typically the most important tool in your lead generation process, so making sure you have the right CTAs across key pages is crucial for marketing strategy. 

Based on the GTM focus - product-lead, hybrid or sales-led motions, your CTA mix should reflect the goals. For example, the sign up process for product-led motions should be extremely low friction with easy access to your free trial as a main CTA, while most selling will be done via product and onboarding process.

For sales-led motions though, booking a 1:1 meeting through “Request demo” or similar CTA is essential, but also you'll need smaller or "easier" conversion events sprinkled throughout your website - among them gated content such as events, ebooks, reports, etc. 

If you’re using a hybrid model - make sure your leads have straightforward access to the most crucial CTAs of both types on your website. 

Create different types of content based on your sales funnel 

Product focused B2B SaaS startups can leverage the product itself as a content distribution channel, and the product becomes a "sales enablement" tool, once your users or companies are in self-serve mode. 

Sales-led motions though don't have this luxury, so you'll have to rely more on email to share content. You'll also need more gated "lead magnet" content to accelerate your pipeline. And here comes a crucial topic of creating the right content strategy.

First and foremost, make sure you have a well-documented content strategy covering different content types with the sales funnel stages in mind. 

Put quality over quantity and understand well what part of sales funnel your content will influence. E.g. an ebook is good content for top of funnel leads and technical whitepaper is good for leads that are closer to the button of the funnel.

Also, connecting KPIs to your content strategy can leverage it to the new level. Is it to drive traffic? Is it to drive sales? Is it to build brand authority? Make sure your content strategy is documented together with KPIs.

Not investing time into detailed persona research

One area where some young companies fail is getting a detailed understanding of the buyer persona. It is essential to understand or at least build several hypotheses around who your target audience is. 

A persona profile is crucial for effective demand generation in SaaS. The sooner you develop it, the better. Your demand gen strategy can be significantly improved by providing the right content at the right stage of the customer journey to the right persona.

Building persona profiles together with your sales team can be one of the most beneficial exercises in the early growth stages, as sales are your best guide to understanding how customers solve their biggest pain points, and how they look demographically. 

How to Build Predictable Growth for Early Stage B2B Startups

Join my new cohort-based course

Joint my cohort based course to learn how to build predictable customer acquisition process for early stage startups. I'll provide frameworks to get your demand gen team up and running without you having to reinvent the wheel, including templates for things like Ideal Customer Profiles, KPIs for demand gen team and a list of essential tools for companies in the same stage of growth as yours.

Apply to my course

Not aligning efforts between marketing and sales

While some startups tend to run sales and marketing independently, the strongest ones foster cross functional collaboration between these functions from day one. 

Among just a few benefits of getting your marketing and sales aligned is improved pipeline conversion rates, better understanding of customer pain points that reflects into improved sales collateral and content strategy.

Sales and marketing need to work together closely to build a strong demand generation program.

Not using automation and processes

Effective demand gen is all about the right tech stack and lead processes. Creating, capturing and nurturing demand requires a lot of effort from the whole team, so make sure they are supported by the right autonation tools and lead processes are in place. 

Developing healthy, reliable demand requires a comprehensive lead nurturing strategy from the point of first contact through to the point of decision and purchase.

Of course marketing automation comes at cost, but there are tools for every stage of company growth - from inexpensive CRMs for small companies to complex solutions for enterprises.

By implementing the necessary tech stack early on, you will make sure that demand gen effort will not be wasted, leads will be captured and processed according to your sales process. 

Not having the right process for content distribution

One of the most common pieces of advice that early stage founders get is “start building a content engine for your startup”. 

Obviously, building a strong content engine requires time, investment and team effort, so how can you increase the ROI from your content creation activities?

Build a content distribution plan as early as possible. To build authority for your company, you need to be seen by the target market, and so your content has to be in the right place at the right time - to increase the demand gen effectiveness. 

Content promotion involves targeting decision-makers, promotion with paid ads, and leveraging distribution platforms to reach a wider audience. 

Not measuring results (KPIs) regularly

Measuring results is essential if you want to make sure your effort is going in the right direction. Even young companies can do it using a variety of data. 

Here are some following metrics to get you started:

• Close rate indicates how many sales have been made versus the number of leads generated.

• Cost per acquisition will help figure out the cost effectiveness of your demand generation strategy.

• Cost per lead is an essential metric that shows how much your business is spending to acquire a lead.

• Conversion rate will help track the important conversion points in the buyer’s journey. You can measure it for your landing pages, forms, and calls to action in lead forms.

By analyzing these metrics, you will be able to tell whether you are targeting the right personas and which businesses you are addressing are a good match for your product. You can also identify any gaps in your process by closely monitoring these KPIs.

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Not building a brand from the beginning

Developing a brand requires time and a decent budget that is not available in young companies, but your demand gen activities can help start the process. 

Why is brand so important? Winning customer attention in today's competitive world of SaaS is not possible without brand awareness. Build your demand gen strategy with a focus on activities that will position your company as an industry expert. 

Industry expertise means that your brand is seen as an authority in its field from the very first interaction with a potential customer. 

This can be leveraged via a solid content marketing strategy that offers timely, helpful insights at each step of the purchase journey, participating or creating own events, thoughtfully crafting your SEO strategy, leveraging SMM to spread the word about your product, building personal brand for the founders.

This kind of expertise helps build customer trust, and lays the groundwork for important conversations about customer pain points and the solutions your business can offer.

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