Since every B2B company now leads with “AI”, things increasingly look the same to buyers.

It’s a tough job for marketers.

We’ve got to avoid falling into the sea of sameness.

I collected 904 AI product positioning examples and homepage screenshots to help. Find role models, find gaps, scan it like a buyer.

Explore the full gallery here.

If you find any stand-outs, let me know in the comments here.

Here are my takeaways (and favorite examples) from analyzing the whole set...

Common Patterns

Generally speaking, I see 10 positioning approaches. Here’s what they mean and how they breakdown amongst AI-Native companies.

“AI for [X]” is the default. I saw that in 37% of AI-native companies and a whopping 67% of AI-integrated SaaS companies.

The agentic wave (26%) has three flavors worth noting:

• “Agentic [Category]” — 37 companies staple “agentic” onto their category. Agentic CRM. Agentic lakehouse. Agentic procurement. This is “AI for [X]” with a trendier word.

• “Meet your AI [Role]” — Bolder. 11x says “Digital workers, Human results.” Lindy says “Meet your first AI employee.” These companies sell teammates, not tools.

• Agent Platform — Positioning as the platform for building agents. Writer, Forethought.

What Stands Out

My role models. The homepages that stopped my scroll all had clarity and confidence.

Lovable — “Build something Lovable.” Your company name IS the call to action. Perfect for the Vibe Coding vibes.

Fal.ai — “Generative media platform for developers. Built with fal, loved by all.” They tell you what it is and who it’s for in one line, no “AI” in h1.

Clay — “Go to market with unique data — and the ability to act on it.” Doesn’t lead with AI. Leads with the outcome. The AI is how, not what.

Databricks ($19B) — “AI agents trained on your business data.” The biggest pivot in the dataset. THE data lakehouse company, now leads w AI agents. A masterclass in repositioning at scale. CIOs know “your business data” = context = King.

Typeform — “Build forms at the drop of a prompt.” Implies AI without saying it. In 2022 it was “There’s a better way to ask.” They’re still great at six-word headlines.

Harvey ($1B+) — “Practice Made Perfect.” Three words. No mention of AI. Lawyers speak. This is a $1B+ AI company that knows their audience.

Intercom — “Intercom is the AI customer service company.” Not “offers AI.” Not “uses AI.” They ARE the AI company. Driving a identify shift with Fin (and their numbers back it up).

Domino Data Lab — “AI outcomes, not AI hype.” The anti-hype positioning. Calling out the noise directly.

Sierra — “Better customer experiences. Built on Sierra.” No AI in the headline. Confidence. The product is the platform, not the buzzword.

1mind — “We are Super Humans building Superhumans.” The most aggressive human-replacement framing I found. You won’t forget it.

Descript — “AI editing for every kind of video.” Crystal clear, opens the imagination for a horizontal product.

What to Avoid

I won’t name names. But you’ll spot these patterns fast in the gallery:

• 100% buzzword headlines — “The AI Super Cloud” or “AI Platform for Agentic Intelligence.” When every word is a buzzword, none of them land.

• “Agentic” as wallpaper — 37 companies staple “agentic” onto their existing category. It’s becoming the “cloud-based” of 2026.

• Superintelligence for everything — When “revenue superintelligence” coexists with actual AGI research labs using the same word, the term is already diluting.

• The committee headline — Safe, forgettable, could belong to any of 50 competitors. “AI-powered platform for X” describes countless plays.

The weak homepages try to hedge and cover everything. The strong ones pick a lane.

What’s Next

I’m planning to refresh this gallery monthly — tracking how AI positioning evolves as the market matures. Some things I’m watching:

• Does “agentic” follow the same path as “cloud” and “big data,” going from a hot buzzword to commodity label?

• Will more AI-native companies drop AI from their headlines entirely, like Harvey and Lovable?

• How many AI-integrated SaaS companies break out of the “AI for [X]” default?

If you think a company belongs in the gallery, reply. I’m especially looking for strong examples I’ve missed.

I’ve been whining about the lack of AI GTM progress.

But I think a wave is coming.

This week I went to the positive side, highlighting the most promising companies working to fill the gap.

This is how I see the gap:

This led to creating the The AI GTM 100.

I’ll be quickly expanding on this resource and tracking changes weekly. You can add a comment here with any AI GTM tech you’re using or following.

Why does this matter?

The gap is real

Categories like support, dev tools, and legal have multiple domain-specific AI companies breaking through. Sierra hit $100M ARR in under two years. Harvey grew 8x in 2025 to an $8B valuation. Cursor is worth $29B.

If you care about GTM keeping up with AI transformation, you want a rich ecosystem.

Clay is the only recent break out winner. Harvey (legal) alone is worth more than all AI-native GTM companies combined.

The wave is building

That said, there are a lot of interesting things happening in AI GTM. So I put together a tracker of the companies worth watching.

Get the AI GTM 100 →

Note: There aren’t 100 in there yet, but I’m going to track the space and keep the resource fresh.

Who stands out (in my opinion)

Clay — Most dramatic growth story so far. $1M to $100M ARR in 2 years. 6x growth in 2024. $3.1B valuation. Dominant brand. The RevOps → GTM Engineering movement is real. They’re flattening the whole data vendor space.

AirOps — Most interesting in the AEO and AI content workflow space. $40M Series B at $225M valuation. Logos like Ramp, Carta, Wiz, Brex. Well-positioned as AI search changes how content gets discovered.

Inflection.io — The only one delivering Marketing Agents on top of a modern MAP. Full Marketo replacement with embedded CDP so the Agents get all the context. Early-ish ($15M raised), but landing $100K+ deals. Customers like Vercel, Twilio, Postman.

Unify — After the AI SDR wave, they brought a new take on AI prospecting. $40M Series B at $260M valuation with 700% YoY growth. Marquee AI logos using it (Cursor, Perplexity, Together AI). Very complete product vision connecting intent signals to AI agents to multi-channel outreach.

Endgame, Actively, Rox — All well-funded, smart teams, interesting approaches to “super intelligence for sales.” I think of these kinda like ChatGPT for sales, but with deep context and knowledge. Gong’s got something to say about this space too.

Agency — Huge vision, unstoppable founder. Elias raised $32M from Sequoia and Menlo to define the new AI-native customer success model. Strategic backing from Snowflake and Databricks Ventures. I’d bet he eventually tries to replace SFDC and become the customer OS (or something like that).

What I’m watching

I think the wave is building and this space is going to change fast in 2026.

• The real, winning “agentic” use cases.

• New startups entering the space.

• How the early AI players evolve and expand.

• How incumbents like Gong, and ZoomInfo make the transition.

The tracker has dozens of interesting AI GTM tech players with research on each. But I know I’m missing some.

Who else should I track?

Recommend a company here or leave a comment on LinkedIn

Back in 2022, SDRs topped every “jobs AI will replace” list.

Now the hottest AI-native companies are building SDR functions. OpenAI, Anthropic, Clay, LangChain, ElevenLabs are all hiring SDRs or SDR leaders right now.

I’m seeing this clearly in my AI Index (200 top AI companies). 39% of those with open GTM roles are hiring SDRs or SDR leaders. The vast majority are outbound and enterprise-focused.

If anyone is going to replace people with AI, I’d think it’s these companies.

The new playbook is being written

What’s most interesting: many $100M+ AI companies are forming their SDR function for the first time in 2025.

• Clay just hired Rob Cook as Head of BDR. Now they have open roles for “ClayDRs.”

• Anthropic posted a BDR Manager role to “build, lead, and scale a team of 8-12 BDRs.”

• CoreWeave is hiring an SDR Director to “lead and scale a high-performing SDR organization, managing a team of SDR managers.” They’re building the leadership layer before the IC layer.

• LangChain has IC sales development roles open, explicitly saying “Hiring our first Sales Development Representatives.” A company with massive open source adoption, now building an upmarket GTM motion.

• OpenAI is hiring a Head of Sales Development to build their first enterprise Sales Development organization.

These aren’t backfills. They’re building leadership layers to create the function.

These companies have an advantage. They get to work from a blank page. They aren’t trying to reduce headcount or reconfigure an existing motion. They’re figuring out the new playbook from scratch.

I know Jason Lemkin replaced his sales team with 20 agents. That works for event sponsors (straightforward, transactional deals). But if you want to win enterprise? Complex sales, multi-threading, multi-product, executive engagement? You probably need humans involved in creating those 6-7 figure opps.

The Enterprise talent war is on

This is part of a broader move upmarket. Enterprise-first companies like Sierra are doubling down. PLG-first companies like Cursor and LangChain are building their enterprise motions.

We can see them all making a big push for Enterprise GTM talent.

~1/3 of all open GTM roles at top AI companies are Enterprise-focused. 50% of companies with open GTM roles have some Enterprise roles open. Not a surprise given the size of the prize and the pace of enterprise demand.

The biggest demand is enterprise AEs who can check three boxes:

1. 7+ years experience.

2. Self-sourcing skills.

3. Tech chops.

Listed OTE averages $300-320K.

Here are some examples of what they’re looking for:

They’re building full orgs, not just hiring AEs

Same story across the broader Enterprise motion—new programs, rapid expansion.

International expansion is aggressive. Mistral AI has 78% of Enterprise roles outside the US across 9 countries. Harvey is in 8 countries. Sierra is pushing into Japan and Singapore.

Vertical bets are forming. Harvey has two distinct tracks: Law Firms vs Corporate In-House. ElevenLabs is making vertical bets on Healthcare (Boston) and Government (DC). Anthropic built a real Federal practice—DOD/IC, Civilian, State & Local.

Solutions investment is heavy. Companies like Cresta have 41% of GTM in FDE/solutions roles. Cursor is building AI Deployment Managers and Field Engineers alongside AEs.

2x more Enterprise AE roles than SDR roles in my data. That suggests more SDR hiring is coming.

What I’m watching

I can’t believe these orgs will run the same Aaron Ross playbook from 2011. The skills, the automation expectations, and the AE/SDR ratios are all likely shifting.

A few things I’m curious about:

• How does output (and comp model) change when AI handles more of the manual work?

• What skills matter for AI-native SDRs? Is it any different than SaaS 2.0?

• How will these orgs take shape more broadly? AE/SDR ratios, FDE investment, Marketing roles?

Worth watching how these AI companies build Enterprise GTM. What’s different, and what stays the same.

300+ episodes. Every transcript. Free to the public.

I went digging.

I was interested in what we can infer from Lenny’s content about the AI wave. His podcast has become a go-to for insights at the intersection of AI and business. The guests are top tier. The conversations go deep.

If anyone’s dataset would reveal where AI is actually working, it’s this one.

Here’s what I found.

Why dig here: Lenny’s pod has become an AI-first conversation

The shift is clear. In 2022, less then 10% of episodes were AI-focused. By the end of 2025, 67% of episodes were AI-focused.

And the audience is responding. AI-focused episodes average 88K YouTube views. Non-AI episodes average 35K. That’s a 2.5x difference.

Lenny goes where the action is. Right now, the action is AI.

But where exactly is the AI action?

Digging deeper, I categorized the AI conversations into three domains. I used this framework to further analyze Lenny’s archive.

1. Building AI-Native Products - LLMs, prompts, evals, safety, security, RAG

2. Engineering & Coding with AI - Vibe coding, Cursor, Lovable, Windsurf, CoPilots

3. GTM & Growth with AI - AEO, pricing, AI agents, lead gen, sales

All three matter. But depth and interest are not evenly distributed.

The gap

Comparing the depth and engagement across these buckets showed a clear gap. Product and engineering are riding the wave. GTM is paddling around.

I’ve expressed versions of this before. But Lenny’s dataset brought it to life in a different way for me.

The first place I saw this difference is in the number of episodes and views across categories. ~87% of the views and episodes are focused on product and engineering, with ~13% focused on GTM.

The word frequency tells the same story.

Across the 75 AI-focused episodes:

• “product” appears 5,070 times

• “build” appears 3,848 times

• “code” appears 1,489 times

• “engineer” appears 1,443 times

• “marketing” appears 444 times

That’s an 11x gap between “product” and “marketing.”

And the top 25 AI episodes are dominated by product and engineering

The first GTM-focused episode is Ethan Smith on AEO at 98K views. Solid. But it’s surrounded by product and engineering content.

Product and engineering have clear winners. Cursor, Lovable, Anthropic, OpenAI. Tactical playbooks on evals, prompts, agents. Real revenue, real growth stories.

GTM is still finding its footing.

The opportunity

This gap makes sense. Lenny follows where the action is. But it also signals where the next wave lives. I think all of us in the GTM space (leaders, practitioners, and builders) should be excited to push this forward.

What’s next

I’ll keep studying this gap and bringing what I learn and see to the conversation.

If you’re working in the GTM AI space, I want to hear from you. What’s working? What’s hype? Reply and let me know.

The GTM AI wave is coming. The question is who’s going to lead it.

Big big picture I’m optimistic about AI (and a bit scared).

Two clicks down, business and information work are changing fast. New AI-Native tools are tearing through many categories and functions. Customer support AI is on fire (eg Fin, Sierra, Cresta). Coding agents of course (Cursor, Lovable, etc). General AI productivity tools (Gamma, ChatGPT, Lindy, decks, docs, etc). Legal work (eg Harvey). Plus many vertical apps taking off.

But in GTM… not yet.

What have we actually got so far?

1.) LLMs gave us all productivity lift.
ChatGPT, Claude, Gemini. All impactful. Video and image gen for content. But these help everyone. Not directly solving for GTM.

2.) We got a wave of "AI SDRs."
Maybe it works eventually. But I don't see success stories yet (except for Jason Lemkin and his 20 AI sellers).

3.) We have Clay and n8n.
Truly amazing leverage here. This is where you can build stuff with LLMs. But they aren't ready-made agents.

4.) We got better chatbots.
This is like Drift + LLM. Better than old chatbots. But doesn't change how you go to market.

5.) Every GTM tool added AI features.
e.g. At Keyplay our agents do enrichment and lookalikes. Helpful but not revolutionary. Every SaaS 1.0 and 2.0 platform sprinkled AI in. Some more than others.

Net net we have:
✅ Lots of RevOps efficiency.
✅ Content production leverage.
✅ GTM engineering with LLMs.

It feels like most “AI in GTM” is efficiency theater. It makes sellers faster inside the same old workflows. And buyers mostly experience it as… more noise, at higher velocity.

Of course, the buyer dynamic is already be shifting because we all have LLM chat + search. But it’s not inside “GTM tech” categories. This is a scary dynamic because it’s completely outside vendor control. I think this will be part of the “new way” that emerges, but unclear how it takes shape.

What I’m waiting for is AI that changes the buyer dynamic AND gives GTM teams new playbooks. Not just efficiency for sellers and ops, but ways to create different/better experiences for buyers. Upleveling both sides of the equation.

That's when it gets exciting IMO.

What did I miss? Do you think AI + GTM breaks out in 2026?

Naturally, I weighed in.

I’ve worked in GTM tech for 15+ years. I’ve never seen a company quite so unique and disruptive.

Every vendor pitch these days gets met with the same question:

“Ok, but can’t we build this in Clay?”

As a user, I see Clay outside of traditional categories. It’s a different verb.

We build in Clay. We use other tools.

As a founder, I’m in awe of their community flywheel, watching it spin faster. They created a movement around “GTM Engineering” that feels real and important.

As Keyplay CEO, I’m navigating the frenemy line. We integrate with them. They could displace us. We must be excellent in our niche.

Their path to $100M is super impressive.

As they sprint toward $500M, here’s what I’m watching:

1. How does ZoomInfo respond?

Clay is disruptive to everyone, but especially ZoomInfo.

It’s a tale of two opposite approaches. ZI owns the data; Clay orchestrates the network. If one field out of 100M is wrong, ZI takes the heat. Clay has the enviable position of giving you data, but not being liable for the quality.

But ZI has control, and control means they can invest to make the reliable and AI-ready (without making users do extra waterfalls and GTM engineering). wrote a great piece about the investments ZI is making to be the best “unified data layer.” If ZI could turn data ownership into an undeniable strength (instead of a liability), that’s a real counterpunch to Clay’s network model.

This is a battle for mindshare. Clay is winning today. But ZI definitely won’t roll-over, especially in the enterprise.

2. Does Clay expand beyond enrichment?

Enrichment made Clay beloved. Now they’re moving into signals, sequencing, audience management.

More “solution-y,” less pure orchestration. Makes sense for Enterprise consolidation, but it’s a high bar. The friendly Brooklyn disruptors suddenly look aggressive — taking shots at Gong, Outreach, 6sense, and probably more.

The question: will customers want Clay as the ultimate consolidator?

3. GTM Engineers or AI Agents?

The GTM Engineering movement is smart right now. AI + GTM data is too complex for most business users. But it’s easy to imagine a world where business users skip GTM Engineers entirely and work with AI Agents.

Clay’s Sculptor is a step in that direction. They are getting to workflows with a prompt. If the UX becomes “talk to an agent,” how do GTMEs evolve? Do business users adopt? And do power users drift to n8n, Claude Code, or whatever comes next?

Exciting times. 🍿

What else are you watching in the Clay vs. GTM landscape?

This story keeps happening in my network.

Today I evolved Keyplay to hit the problem head-on. It’s time for someone to take aim at the legacy ABM problem.

ABM strategies are alive and well. But the blackbox overpromise has run its course.

At first, I thought this was just unbundling. CMOs cherry-picking best-of-breed tools instead of paying for bloated platforms. That’s part of the story.

But it’s sharper than that now. The market is splitting into two camps:

Camp 1: Legacy ABM

Big enterprises “too deep” to switch. Locked into multi-year contracts. They are justifying the sunk cost... for now.

They’ll be 2 years behind.

Camp 2: The New Way

Teams moving fast with AI and modern tools. They’re not just unbundling. They’re rebuilding GTM from first principles. They need:

• Models that adjust in minutes, not months.

• AI that finds new signals instead of recycling CRM data.

• Intent you can explain instead of defend.

• Systems that work for both CMOs and GTM Engineers.

This isn’t about swapping vendors. It’s about a fundamentally different approach to how GTM works.

The CMOs and RevOps leaders in Camp 2 are making moves.

Why Legacy ABM Falls Short

We’ve seen three core problems with the old model:

1. Blackbox models you can’t trust. ML trained on stale CRM data. No way to explain outputs. Sales loses confidence fast.

2. Speed mismatch. Annual model refreshes while your market moves weekly. By the time you adjust, the opportunity is gone.

3. Intent without Fit is just noise. High intent + Low fit = WTF-Intent-QLs. The classic trap. Account selection is 2D.

Fit is foundational. Intent is the accelerant. Get this order wrong and you waste sales time on accounts that will never close.

What’s Coming Next

We’re in the third generation of B2B targeting, accelerated by AI.

AI Agents are a fundamental shift in how companies identify, segment, and prioritize accounts. Instead of relying on legacy firmographic filters or black box ML, you can now answer any account research question about every account in your market.

These new AI “columns” (research, enrichment, signals) are key ingredients in the shift from legacy ABM to modern ICP marketing.

But AI alone doesn’t get you there. It’s also requires a return to fundamentals. The new model:

1. Start with backtested ICP models (not guesses)

2. Use AI agents to research any signal for every account

3. Layer whitebox intent (you see why accounts surface)

4. Adjust in minutes, not months

The market is ready for an alternative.

If you’re onboard with this future, check out Keyplay’s take here.

Grab the spreadsheet template here. This newsletter offers a framework, process, and principles for an effective market map.

Most GTM leaders say they know their market. But when annual planning questions start, the cracks show.

Which segments should get more SDRs? How much more “room” do we have in mid-market? Should we keep selling to core Tech or expand to Financial Services? Which new geographies are actually worth investing in?

These “where do we play?” and “how do we win?” questions are the heart of strategy.

A clear market map can help provide answers.

What Market Mapping Actually Means

Market mapping isn’t top-down TAM sizing for your board deck.

It’s bottom-up insight about where opportunity concentrates within your serviceable market.

A proper market map answers:

• Where is the opportunity for growth?

• Where are we winning most today?

• Where are our buyer’s in their journey?

• Where should we focus going forward?

Framework

Market mapping works in two levels.

Level 1: Markets

First, break your business into top-level markets that you can serve.

Common market definitions:

• Business units (Enterprise, Commercial, SMB)

• Geographic regions (NAM, EMEA, APAC)

• Product lines (Core Product, New Product)

Most companies use a combination.

Pick the market structure that maps to how you actually run GTM.

Level 2: Segments & tiers within each market

Within each market, you need segmentation and account tiering. Your Enterprise market looks different than your SMB market and your New Product market might also be distinct.

Segment by the dimensions that matter to you, not just NAICS and SIC codes:

• Categories - Your own vertical or industry definitions.

• Themes - Group by business situation or priorities.

Then tier accounts within each segment by ICP fit. A/B/C/D tiers based on your scoring model. This helps you move from “who can we serve?” to “who will we serve best?”

This two-level approach lets you understand hot spots at both levels:

• Which markets get resources? (Enterprise US vs Commercial EMEA).

• Which segments within each market? (Financial Services vs Tech within the Commercial business).

Use Your Map to Allocate Resources

Once you have your market map, you can act like an asset allocator.

You have finite resources: budget, headcount, executive attention. Your market map shows expected return by segment.

For each segment, evaluate:

• Opportunity size. How many A/B-tier accounts? What’s the revenue potential?

• Current traction & success. Where do we actually close deals? Where do we win fast? Where do customers stick?

• Investment required. What does it cost to acquire here? How much coverage do we need?

This gives you a simple ROI framework for every segment. Some segments are obvious wins. High opportunity, proven win rates, efficient GTM. Fund those fully.

Some are obvious passes. Small opportunity, low win rates, expensive to serve. Starve those.

The interesting decisions happen in the middle. Your market map makes these tradeoffs explicit.

The Output: Your 2026 Market Map

This becomes your strategic foundation.

Tyler Calder at PartnerStack showed what this looks like when executed well. They mapped their entire market, scored every account across 40+ signals, and tiered them A/B/C/D. Then they aligned their full GTM engine around those tiers.

The result: 34% drop in cost per dollar of pipeline created. 58% increase in pipeline value.

That’s the leverage of a good market map. It drives everything from board reporting to daily sales priorities.

Territory planning: Assign your best reps to your best segments. Stop wasting $300K OTE sellers on C-tier accounts.

Marketing budget: Allocate spend by segment priority. Create segment-specific campaigns.

Expansion decisions: That new vertical opportunity? Check the map. If Financial Services doesn’t include enough ICP accounts, it might not be the best bet.

Three Principles for Better Maps

1. Don’t confuse TAM with ICP

Your TAM slide shows investors scale. Your ICP defines who you serve best. Your market map shows where those ideal customers concentrate. All three matter. Don’t mix them up.

TAM is great for strategy. Market maps drive operations.

2. Definitions before data

Before you can map your market, you need a clear ICP definition. Most companies have fuzzy, off-base, or stale ICP definitions. That makes market mapping impossible.

Start with one of three proven frameworks:

• Attribute-Based ICP (breaks down fit into firmographics, situation, organization, technology)

• Theme-Based ICP (identifies business themes that indicate fit)

• ICP Paragraph (distills your ICP into a concise narrative)

3 proven ICP definition templates here

Once you have a clear definition, translating it into a market map becomes much easier.

3. Program, not a project

Markets shift. Your product evolves. Treat market mapping as an annual program, not a one-time project. Build the map in Q4. Test it in Q1. Adjust throughout the year. Rebuild next Q4.

Why This Matters Now

2026 planning is happening right now across B2B. Most companies will set targets based on “grow 30% everywhere.”

The winners will pick their spots.

They’ll map their market. Score the segments. Focus resources on the highest-potential opportunities.

Market mapping isn’t sexy.

But it’s the difference between spray and pray and strategic focus.

Want help mapping your market for 2026? Request a demo of Keyplay and we’ll help you get the data.

He doesn’t just pull reports. He avoids the vanity metrics trap. He understands that focus beats volume.

Doug Botchman is just that.

He’s the Director of Revenue Operations at Ceros, where over the last year they’ve been able to drive 2.5x higher conversion on top tier accounts with ICP Marketing, Keyplay, and specialized AI Agents.

Doug walked me through his strategy. In today’s newsletter, we’ll break it down.

ICP Focus Unlocked The Next Phase of Growth

Ceros knew their next phase of growth would be about focus.

Their previous volume-focused machine was reaching it’s limits. Lots of ‘qualified’ leads that weren’t ending up converting. Too many poor-fit accounts were slipping into their targeting and onto sales calendars.

They needed a better way to ensure that they were focusing their limited GTM resources on their best-fit accounts (and no one else).

Doug setup a comprehensive ICP Model in Keyplay to:

• Focus on the right accounts. No broad account lists or native LinkedIn ad targeting. A comprehensive list of scored accounts that everyone agreed were qualified.

• Measure the right metrics. No more fuzzy SQL definitions that incentivized volume. Specific account-level scoring that only rewards breaking into the right accounts.

• Align and improve over time. No more arguments about what their ICP really is. It’s documented in a model that they can validate, backtest, and improve.

AI Agents to Triple Down on ICP Quality

Once he had a model that was working – scoring their high ARR customers high, and DQd leads low – he wanted to go a step further.

Doug asked: “How can we use AI highlight at characteristics that we could never get at before?” With Keyplay, he could go beyond basic firmographics and avoid the pitfalls of blackbox “predictive” models.

He worked with the team at Keyplay to build a family of AI Agents that would research and enrich his accounts in his sleep. These agents didn’t just spit out long-winded answers, they delivered insights that fed Doug’s scoring and segmentation.

Some of the unique Agents that he’s running across his TAM:

• Marketing Use Case Intelligence

  • What it finds: What content are they building? Landing pages? Microsites? Interactive experiences?

  • Why it matters: "Our reps were spending time researching to try and find this. Now we have it at our fingertips and can craft a quicker, more personalized story."

• Content Strategy Analysis

  • What it finds: Are they focused on thought leadership? Lead generation? Event marketing?

  • Why it matters: Helps sales understand the buyer's content priorities and business focus.

• Interactive Content Detection

  • What it finds: Are they already creating interactive content? How sophisticated is it?

  • Why it matters: Competitive intelligence and expansion opportunity identification.

These are just a couple of the agents that Doug was able to build with Keyplay. All of these insights that would normally take a team of humans to find, he can now find instantly.

After applying these agents to their market, they were able to surface hundreds of great-fit accounts that their traditional data providers were missing.

From Data to Action: Coordinated GTM Plays

Using their Keyplay ICP Model, which looks at 40+ data points, including the AI Agents, they’re able to prioritize and segment accounts much more intelligently.

Each account gets scored and tiered:

• Tier A: 70%+ score. High-fit, high-readiness. Their best NRR performers, proven over 24 months.

• Tier B: 50–69%. Strong potential, often missing one or two key signals.

• Tier C/D: Below 50%. Lower intent or maturity, but still on their radar.

Like Tyler Calder at PartnerStack, Doug and the team at Ceros use these tiers as a strategic anchor in account selection, territory planning, and demand generation:

• Different segments get different bidding & creative in LinkedIn Ads.

• Automatic fair territory assignment to reps.

• Google Ads conversion events are weighted differently by account score.

• Custom nurture sequences and content tailored to their specific use cases.

• Pre-call research packets with agent-sourced marketing use case intelligence, content strategy analysis, and competitive positioning

They treat ICP as a program, not a one-time project. They revisit this quarterly, not annually. Markets shift fast, so they use these scores to catch Tier C accounts on their way to becoming Tier Bs. That lets us get in early, and grow with them.

Measuring What Really Matters

And finally, they use this to refine and improve all of their programs against the metrics that actually matter.

Instead of marketing celebrating “MQLs” while sales is missing, the whole GTM team at Ceros is laser-focused on if they’re getting into the right accounts.

Looking for a measurement framework? Kyle Poyar has a great one here.

Wrap-Up

Doug's approach proves a simple truth: precision beats volume every time.

While most companies chase more leads with legacy ABM platforms, Ceros focused on better leads with ICP Marketing. They built systems that identify their best-fit accounts and ignore everything else.

The result? 2.5x higher conversion on their top tier accounts.

The playbook is clear:

Build a comprehensive ICP model that goes beyond basic firmographics. Use AI agents to surface insights you'd never find manually. Tier your accounts and align your entire GTM motion around those tiers.

Most importantly, measure what matters. Account penetration in your ICP, not MQL volume.

Your growth is waiting in the accounts you're not targeting yet. But first, you need to know exactly who they are.

Reporting. Optimizing. Automating. Emailing. Campaigning. MQL-ing.

Tricia Gellman thinks this is backwards.

She's led marketing at startups, big tech (Adobe, Salesforce), VCs, and is now the CMO at Box.

Her mindset: Be the "Chief Market Officer" instead of Chief Marketing Officer.

She walked through what that means on my podcast.

Takeaways:

1️⃣ Rep quota attainment is the CMO's real metric.

"The number one place where companies grow is when reps are confident and they like pick up the phone and they have a great conversation. Cause they're like, 'I won yesterday, I'm winning tomorrow.'"

Great CMOs focus on making it happen.

2️⃣ Intent without fit is just expensive noise.

"There's a lot of people that are just researching a category, researching a solution out there in the world, and they have zero intent to buy." Obsessing about intent without ICP understanding will drive your metrics off a cliff.

3️⃣ Focus on market dynamics, not marketing metrics.

The CMO should be able to see the bigger picture.

"What are we doing for whom and why? Not email open and close rates and website conversion numbers"

4️⃣ Pipeline councils can actually work

"Don't just show red/yellow/green dashboards. Make it about insights and action." Getting tactical and practical here pays off.

The best marketing leaders don't just report and optimize pipeline metrics. They understand the market, the business dynamics, and how to build sales confidence.

This conversation changed how I think about the CMO role.

Full episode here: https://www.peersignal.org/podcast

If you’re one of the 99.7% of marketers who are trying to use AI more in your day to day, this is for you.

We collected 49 AI Marketing examples from real marketers 🤓

This is our attempt to find some receipts.

What marketers are *actually doing* with AI. Real use cases & workflows you can copy.

Some of our favorite examples

When we went over these examples, a theme stood out: the best marketers aren’t using AI as a shortcut; they’re using it as a multiplier. As Kieran put it, “to create more value for a more targeted group of people.”

Some examples…

Tom Wentworth’s iOS game

Tom vibe-coded a pager-tron style game for one target account. Something that would have taken weeks to do, he did in an afternoon.

Kaylee got 10x more specific with her LinkedIn Ads.

When Kaylee was working with Thoropass as a fractional VP of Marketing she wasn’t running standard verticalized ABM ads. She’s ran use-case-focused messaging to segments she used AI to find. Such an intelligent way to laser-focus their messaging. Not to anyone who visited their website, but to accounts with a compelling reason to solve this problem right now (*real* intent).

Mark's original research actually got consumed.

48k+ views, 1,200+ submissions, and kept driving leads 9mo after launch. These research projects used to take 5 figures, and months. Now you can get a great one done in two weeks.

PJ Acceturo made a Super Bowl-quality ad in 3 days.

It got millions of impressions. PJ thinks that these high-dopamine AI videos will be the ad trend of 2025. Maybe this doesn't fit your B2B (boring) brand, but there is definitely something to learn from this new medium.

I created one for Keyplay in 4 hours. Full process documented (prompts included).

Dave and Adam cloned themselves.

Adam’s replaced their broken sales assist motion, handling 850 inbound requests/week. It's not perfect, he mentioned how 10% of the time it's "just bad". But this is a very interesting play.

Special thanks to Kyle Poyar, Kyle Lacy, and Tom Orbach for original research & surveys of real marketers that we also pulled in.

Get the whole database here.

-Adam & Eric

ps.

Have anything interesting that you're doing with AI? If people like this, we'll do a V2 with many more examples. Reply to this email if you want to be included.

It's scary good.

He came on my podcast to talk about how he's actually using AI in marketing, and:

• Why he moved from 'big company CMO' to a scrappy team of 5.

• How they're using AI to drive pipeline right now.

• How to build a culture of AI adoption.

• His formula: Marketing Output = AI × Taste²

• Where the AI puck is going.

• And of course, how he's replacing himself with his Claude Code AI CMO.

Listen Now

My takeaways from our talk:

1️⃣ Make AI a team sport

Tom is leading from the front. In the tools daily testing, learning, shipping. He's aproaching AI as a team sport. No one is solely responsilbe for 'AI Adoption'. Everyone is experimenting, and everyone is benefiting.

2️⃣ Marketing Output = AI × Taste²

"Taste is an exponential contributor to marketing output and it can't be easily automated". The place most people fail isn't in the ability to write or build *something* it's to write or build the right thing, that they're be proud to ship.

3️⃣ AI Marketing can be wonderfully compounding

Claude Code will keep getting better --> his 'context database' will keep getting better --> his agents will work better --> they'll invest even more. His AI machine is compounding. Everything it makes, makes the next thing easier to do, faster, and better.

4️⃣ It's still early

Tom talked about how he'll talk to some marketers who work at companies who aren't explicitly pro-AI usage. His advice to marketers at these companies: leave. It was surprising to me that some companies are still not bought in, but it makes sense. We're still early.

Full episode here: https://www.peersignal.org/podcast

Want more examples of real AI use cases in marketing, subscribe!

Subscribe now

We replaced our whole marketing team with 47 AI Agents who created $50m in pipeline in 6 months!! 🎉

Just kidding LOL.

There’s a lot of hype on LinkedIn about AI Agents. Rightly so. They’re giving marketers who understand them massive leverage.

This newsletter isn’t about replacing your team with agents or driving huge pipeline numbers.

It’s about my real journey building one agent that we actually use. And how you can do the same (without hitting the dead ends I had to go down to get to something that actually works).

Step-by-step walkthrough below. Prompts included.

Automating Call Prep: Our Pre-Call Lead Enrichment Agent

Our AE, Katie, was spending ~15-30 minutes/call manually researching to prep for demos. At our scale, that meant she was spending dozens of hours a month doing call research.

With this agent, she gets all the information she needs (and information she may have missed before) in a couple of clicks.

Building this agent boiled down to three parts: data, analysis, and delivery. I’ll walk through each piece in the following sections, or you can watch this quick video walkthrough I recorded.

Data: Company Information & Scoring

I used Keyplay to get clean account-level data, a fit score, and to answer more qualitative fit questions with their AI Agents.

*Obviously, we already have our ICP Model set up in Keyplay. If you want to get yours set up, start here: How to Build an ICP Model and AI Agents for Account Research.*

I built account research agents in Keyplay to get us:

• Target market type. What type of market does this company sell into? (broader, more complex markets is better for us).

• ABM Maturity. Based on the tech this company uses, their team dynamics, and other details we can find online, how mature is this organizations ABM function?

• RevOps & AE team size. Signals of RevOps maturity, and how sales-led the company is.

Once you’re set up with Keyplay, you can use Zapier to get all of this data on command.

Analysis: AI + Data = Insights

Keyplay is an ICP Modeling & Targeting company. We help companies stop wasting spend on non-ICP accounts.

So, the more deeply Katie can understand a company’s ICP before the meeting, the more valuable the call will be for everyone involved.

So, I layered on some AI to help Katie better grok their ICP more quickly. We had chatGPT get us:

• Simple company description. Explain what this company does to me like I’m a 5th grader.

• Use Cases & ICP. Katie needs to understand a company’s ICP before the call. ChatGPT gives her a starting point.

• Case Studies. Case studies are a super helpful way to understand what the company actually does and who their best customers (who they’ll replicate with Keyplay) are.

• AI Agent Ideas. We build account research agents for companies. I had ChatGPT suggest some ideas. While these are not perfect, they are a nice starting point.

Delivery: Wiring it all Together

After some iterations, we landed on using Zapier for all of this (simple, flexible, and reliable).

We found that Katie would be doing two jobs with this data:

• A) quickly reading the fit score/company details to determine quality.

• B) doing deeper research to understand the company’s ICP.

For A, piping this data into our existing #demo-requests Slack channel was perfect. For B, we found that a project actually made more sense because she could ask follow-on questions and go deeper.

I built her a simple Claude project that she can chat with. All she has to do is say, “tell me about [company domain].”

My wishlist of agents

Naturally, this sparked a lot of ideas. I figured I’d share a few. Let me know if you want me to build any of these (or other ideas!).

Takeaways & Learnings

A couple of my favorite takeaways from diving in and actually building something:

• So many dead ends. It takes more work than you think to get something, even something simple like this, to work. Lots of edge-case management & prompt tweaking.

• AI automation should be used as a starting point. Automations are rigid. So, it’s only worth automating processes when they’ve proven repeatable. Anywhere there’s variance, or more human judgment, a simple project in Claude might make the most sense.

• Data = alpha. Trash in, trash out. We need better inputs if we want great outputs.

It was fun building my first agent! Let me know what AI in Marketing use case you want me to explore next.

- Eric

Our podcast used to die after one post.

All that work finding guests, booking, recording, and publishing… and we’d get one great LinkedIn post out of it. That’s it.

So, Adam and I decided to run an experiment. How much more value could we get out of each episode if we used AI to do most of the work?

The result: the system I built gives us ~5x the output from each podcast with only 1-2 more hours/week of work.

In this newsletter, I’ll share the complete system, prompts included.

NOT a quick fix

If you’re like me, you’re probably seeing a lot of content about “how I’m replacing my team with AI Agents, or how I built this content repurposing workflow with AI and it’s working perfectly.”

My experience was very different.

1. AI still isn’t good enough to one-shot good creative. It’s a great copilot and can drastically speed up the process, but my taste is 100% necessary to get to something I’d actually ship.

2. This process took a looong time. Probably 3x longer than I would have guessed when I started (if you copy my steps, you’ll save a lot of time!).

3. The end output is helpful, not magical. Very, very helpful. We can use this process to ~3-5x our content output, with about an extra hour a week of work. But not magical. This still takes *real work* to get a truly valuable output that I’m proud to ship.

If that still sounds interesting, let’s get into it:

Our AI-Powered Podcast Machine: 1 Episode → 3-5X Output

Up to this point, we'd post one episode, the post would do well, and we'd move on. Now, we get wayyy more leverage. 6 posts, 6 clips, and a compounding machine—every time we create a podcast, our repurposing job gets easier.

I landed on three core workflows:

• Podcast → Promo Post

• Podcast → Future LinkedIn Posts

• Podcast → Video Clips

I'll break down each, with details and the exact prompts I'm using.

Podcast → Promo Post

After some iteration, we landed on a winning promo post format. It promotes the podcast while still delivering ‘zero-click’ value to someone scrolling in the feed.

The post has 4 main sections: hook, lead-in, ‘what you’ll learn’, and takeaways.

AI is horrible at one-shotting this output. Instead, we use it as a co-pilot. It gives us:

• 15 different options for the hook

• 20 different options for the "what we covered"

• 15 different takeaways

Key to getting this to work was providing specific context. Only other posts that follow this same format.

System prompt here

Podcast → Future LinkedIn Posts

We wanted to create content out of each episode. Kipp Bodnar shared an interesting idea about how AI is letting marketers do things that are faster, better, AND cheaper. Elissa shared interesting learnings on how to get buy-in from your board.

These are ideas that we’d love to share with our audience, but as a team of one, I just never made the time to review episodes, extract takeaways, and write post ideas for Adam.

So, we built two Claude projects. One that extracts takeaways (Golden Nuggets) and one that helps Adam turn those takeaways into LinkedIn posts.

Claude (Sonnet & Opus) does an amazing job of extracting takeaways. Here’s an example from our podcast with Kipp Bodnar, CMO of Hubspot.

System prompt for ‘Golden Nugget Extractor’ here.

Then I built another project to help Adam create better LinkedIn content. This one took some iteration:

• It can’t one-shot good content.

• I tried having it give multiple hook/angle/body options, but this turned into more work for Adam (having to scroll through all of that is a pain).

We landed on using a Claude project as an AI-Assistant. He can open it up, share the golden nugget he wants to flesh out into a post, and it will help him by suggesting angles, sharing role models, getting stats & data to back up claims, and sharing feedback.

I used PhantomBuster to scrape all of Adam’s LinkedIn posts, along with posts from other creators whose styles we like. Grabbed the best-performing ones, and uploaded as project files.

Prompt here.

Podcast → Clips

After analyzing other creators, it was clear that clips themselves aren’t doing well on LinkedIn. But clips are a great way to make an existing post more compelling— to ‘punch up’ a post.

So, our goal was to create clips for all of our ‘Golden Nuggets’ easily and quickly.

This is where I tried the most different tools and hit the most dead ends.

🚨Avoid these dead ends & save yourself headaches! 🚨

1. Trying to automatically create clips from your golden nuggets. All the clip creation tools give you 2 options, A) manually find & create clips B) “magic clips” (i.e upload an episode and get clips). The magic clips aren’t good enough to ship, and they don’t let you upload timestamps and get clips out.

2. Trying all the tools. I tried Submagic, Descript, Riverside, and SwellAI. Descript fits this workflow the best.

3. Automating too much. These tools are just not good enough to create shippable clips without a human touch. Trying was a waste of time. The better way, use AI to get 80% of the way there, and apply my taste as the final 20%.

The clip creation workflow I landed on was simple. First, I created clip templates for vertical (single speaker) and horizontal (two speakers) in Descrit. Then I just pull the Golden Nuggets from Claude, use Descript’s manual editor, and create clips for each takeaway. I save these to Drive via Descript for easy retrieval.

Descript’s AI Editing is great. I can get 6 clips in ~30mins (much better than paying an agency $3k/mo!).

Special Treat: Image-gen

My next iteration is to see if I can get awesome comic book-style carousels out of each episode. Reply to this email if you want me to go deeper on this and share my learnings :).

Wrap-Up

My biggest takeaway: AI won't magically solve your content problems overnight. It's a powerful copilot, but your taste and strategic thinking are still 100% necessary.

Understanding this earlier would have helped me save a lot of time automating what is just plain impossible (right now).

We're planning to open-source more of our prompts and share updates as we refine this system. If you build something similar, I'd love to hear what works (or doesn't work) for you.

-Eric

ps. what did you think of this newsletter?

He's a legendary CMO.

Not only did he help build one of the most impressive businesses in SaaS, he built one of the most impressive teams.

People like Russel Banzon (now CMO @ Cresta), Chris Orlob, Devin Reed, Sheena Badani came out of his talent factory.

He came on my podcast to talk about:

• Best practices marketing vs. corageous marketing.

• How to keep boldness in the culture as your team grows.

• Why "different is better than better".

• Why "story-audience fit" matters more than storytelling skills and how to test it properly.

• How AI and modern tools can amplify courageous marketing strategy (but never replace it).

And more.

My favorite takeaways:

1️⃣ "Different is better than better" (and better than boring 'best practices')

By the time something becomes a "best practice," everyone is doing it. If you want extraordinary results, you have to break away. It's risky, but not as risky as getting lost is the sea of sameness.

2️⃣ Simple marketing measurement trumps fancy attribution.

You don't need complex attribution to prove if something works. If it really works, you'll feel it. And if something flops, you'll feel that too. Focusing on clear signals rather than trying to track every micro-conversion frees marketers up to take bigger swings.

3️⃣ Subject matter experts can be your best marketers

Legendary marketers like Chris Orlob or Devin Reed weren't legendary marketers when Udi hired them. They just knew their persona cold. I think this will continue to prove a competitive advantage as AI enables anyone to create AI slop content.

4️⃣ Every touchpoint is a brand moment

From privacy policy emails to webinar invitations, every customer interaction shapes perception. Treat the smallest details as opportunities to reinforce your differentiated brand experience.

5️⃣ ICP Discipline is a competitive advantage

Amit, their CEO would consistently say no to short-term revenue in the early days because he knew it would distract, drain, and diffuse focus. Easy to say, hard to do, but so valuable if you can maintain that focus.

This was one of my favorite episodes.

If you want to learn more from Udi, I can't recommend his book enough. It's a must-read for any marketer looking to level up.

Or at least, I was bad at AI.

It would never actually work for me. I'd see all these LinkedIn posts about people replacing their teams with agents, Kipp Bodnar (Hubspot’s CMO) told Adam he’s 10x as productive because of it, I see people like Brendan Short as excited as they’ve ever been about GTM because of AI.

And my boss is blowing up my Slack with threads of other marketers doing cool stuff with AI.

But if I’m being completely honest, I never really got it to work. I obviously used it for content. I tried some other tools, but it never really delivered something better or faster than what I could do myself.

I’d be somewhere between ‘Unacceptable’ and ‘Capable’ on Wade Foster (Zapier CEO)’s AI fluency framework.

Until now.

This week was my first big unlock. It’s nothing sexy. Honestly, I just spent 3 days heads down researching the fundamentals to get up to speed (and get AI to actually work).

In this newsletter, I’ll share my learnings, condensed into a simple guide you can share with a marketer on your team who isn’t using AI as much as they should and needs a simple place to start.

It’s not sexy, and I’m not even close to an expert. This is obviously not a quick fix. But I thought if this helped me finally get consumed by the AI bug, maybe it'll help you too.

Btw, I started this process by reading dozens of pieces from people who really get it. 9 of my favorite resources at the bottom of this email.

The TL;DR: Prompts + Context + Projects = 🙂

The takeaway from my deep dive is simple: AI wasn’t working for me because I wasn’t good at asking (prompts), I wasn’t good at tuning (context), and my disorganization made everything take longer (projects).

Once I figured these pieces out, it all clicked.

If you want to get value out of AI, here’s where I would start (and what I’ll cover in this email):

• Prompt engineering: Learning the basics of prompting so you can ask the AI for what you want clearly.

• Context: Why AI is more like an “intern with a PhD in everything” than an assistant. Understanding how, when, and where to share context.

• Projects: The simplest way to actually bring this all into practice.

Prompt Engineering 101

I started where most would, the prompts. I:

• Listened to this podcast literally titled The Ultimate Guide to Prompt Engineering

• Downloaded Marketing Against the Grain’s prompt library and started deconstructing and testing them

• Had ChatGPT run a deep research project for me on Prompt Engineering 101

I would say improving my prompting gave me a ~30% improvement in effectiveness. These are all resources I’d recommend to go deeper. If you’re just getting started, here are my key takeaways:

1. Structure. My prompts were just blobs of text before. This is more challenging for LLMs to comprehend and is often unclear regarding the desired outcome. A simple structure that works much better:

   1. Prompt Blocks: Context → Task → Constraints/Criteria → Output Format → (Examples) → Self-check.

   2. Output Format: Demand tables, bullets, JSON, etc. Show a schema or tiny example.

   3. Delimiter Your Data: Triple quotes or tags around source text to avoid confusion.

2. Specifics. Be incredibly specific about what deliverable and output you’re looking for.

3. Context. Show the model 1–3 examples of the task/output (if you can’t, share as much context as possible).

4. Decomposition. Break big asks into ordered steps. Tell it to reason before answering. This helps a lot with any more complex asks.

5. Self-Critique / Refinement. Always prompt it to check and improve its own answer.

Context Engineering

This is what finally got ‘how to work with AI’ to click. From Dharmesh’s simple.ai newsletter:

“Think of AI like hiring an intern with a PhD in everything who shows up on their first day knowing absolutely nothing about your specific business. They're incredibly well-read and can discuss philosophy, history, and quantum physics, but they don't know how your internal systems work, who your customers are, or where the coffee machine is located.”

AI is like an intern with a PhD in everything. It doesn’t lack intelligence (AI can’t do that yet) it just lacks context (AI can’t do that for you yet). Just like an intern with a PhD in everything, it needs to be hired and onboarded, given real context before it can deliver true leverage.

Onboarding my AI with context: Projects

I found that the simplest place to give AI the context it needs is projects. You can give each project specific instructions (a role), and arm them with specific context that they remember across chats.

My first project was a general co-pilot (following this article). I created the project, adding the following prompt under instructions:

“I am a marketing lead at Keyplay (keyplay.io), and you are my expert coach and advisor, assisting and proactively coaching me in my role to reach my maximum potential.

I will provide you with detailed information about our company, such as our strategy, target customer, market insights, products, internal stakeholders and team dynamics, past performance reviews, and retrospective results.

In each conversation, I will provide you with information about a particular initiative so you can help me navigate it.

I expect you to: ask me questions when warranted to gain more context, fill in important missing information, and challenge my assumptions. Ask me questions that will let you most effectively coach and assist me in my role.

Encourage me to:

- Be BOLD! I want to take big swings that can deliver big, multiplicative outcomes. Iteration is fine, but I want you to encourage me to push the limits of this role.

- Be excellent. I always want to strive for real excellence in everything I do. No checking boxes, but thoughtful, sharp, and intelligent execution.

- Be AI-native in everything. One of my core goals is to become one of the most AI-native marketers in my space. I want to stay on top of the newest improvements and learnings of other smart people and always be using AI as much as possible in anything I do. I want to use AI properly too, the execution details matter.

These values are also very important to me.

- Tell the truth and do honest work. Munger said this well: “You want to deliver to the world what you would buy if you were on the other end.”

- It’s clear that I work for the customer. According to Peter Drucker: “The purpose of business is to create and keep a customer.” In my role, this means everything I do SHOULD BE ANCHORED IN WHAT VALUE IT PROVIDES TO MY TARGET PERSONA: B2B Marketing Leaders.

- Results matter, but learning & improving in the pursuit matters just as much. Here’s Josh Waitzkin: “The key to pursuing excellence is to embrace an organic, long-term learning process, and not to live in a shell of static, safe mediocrity.”

- I care deeply, but makes space to smile or laugh. Larry King ftw: “What you are doing is nice, and it can be important, and it can have meaning, and it can move people, but it ain't the end of the world.”

I want you to find the balance of: pursuing excellence, but also not taking the work too seriously. What matters is the output, what value I deliver to the customer. What matters much less is process, systems, operational excellence in delivering that outcome. Less SOPs and more "shipping dope shit to the customer".

Use the corpus files (01–06) as primary context. Follow these rules:

1. Ask > Assume: If required info is missing or obviously stale, stop and ask Eric. Default ask-threshold: <70% confidence.

2. Conflict handling: Prefer the most recent statement in these files. If unclear, ask.

3. Format: Start with a blunt TL;DR (≤3 bullets). Then detail. No fluff.

4. Evidence > vibes: Don’t invent metrics, quotes, or policies. Flag uncertainty.

5. Confidentiality: Treat everything here as internal unless Eric explicitly says otherwise.

6. Voice: Match 06_brand_style.md.

7. Alignment check: Before recommending actions, ensure they align with Goals (05), ICP (02), and GTM strategy (03). Call out misalignments.

8. Use learnings (04) to avoid repeating failed ideas and double down on what works.

9. Autonomy: Draft assets (emails, posts, briefs) without asking IF the relevant section(s) are filled. Otherwise, get clarification first.

10. When citing facts back to Eric, reference file + section so he can verify quickly.

If you need new durable info, propose which file it belongs in and ask Eric for it.

I added context files covering: company context, ICP & personas, overall GTM strategy, experiments and general learnings, team structure, roles, and success metrics, and brand style/voice. I also added source docs covering Keyplay’s positioning, strategy, and other helpful context.

This took me some time finding source documents, rambling to chatGPT, and having it summarize. Use this prompt to build your own context files in a couple of clicks here.

A new world: my AI hammer

• I’m now using my copilot in GPT to give me new (actually good) campaign ideas.

• I built a project in Claude and trained it on our best historical PeerSignal content. It did a fantastic job at a first draft of this email.

• I built another project that takes a podcast transcript and cuts my time-to-finished-post by at least an hour. *the key here was to decompose my prompt. Instead of “give me one post” I ask for 15 variations of the hook, then 15 of the lead-in, then the takeaways, etc.*

I’m finally excited enough to take this seriously, to dive in. I feel like I have this new AI hammer, and everything looks like a nail!

I hope my learnings help someone on your team do the same.

Wrap-Up

If you’re a marketer reading this and you know you should be diving into AI, but you just don’t know where to start, here’s how to get the basics down in less than 2 hours.

1. Improve your prompts with structure, specifics, context, decomposition, and self-critique. Try this on a couple of your existing prompts and look at the impact.

2. Build a project with clear instructions and deep business context (use the prompt I shared above).

3. Start experimenting. Ask your project for feedback, ideas, outputs.

And then, reach out and share what you’re learning with me! Would love to swap notes.

I’ll be continuing to share AI Marketing learnings and experiments here. Please respond if you liked this and have other areas or ideas you want to explore!

Links to Resources

Some of my favorite resources I came across in my learning sprint:

1. Lenny’s Podcast – Ultimate Guide to Prompt Engineering
📺 Watch here

Great baseline for prompting strategy and mental models. A great place to start if you want to go deeper on prompt engineering.

2. Dharmesh Shah – Context Engineering > Prompting
📄 Read here

Dharmesh’s newsletter is amazing. So helpful in understanding the fundamental principles and getting truly excited about AI. This is a great one to start with.

3. Hubspot – AI Prompt Library
📚 Browse here

Some really interesting marketing prompts to play around with. Helpful to see examples of great prompts to put theory into action.

4. Lenny’s Newsletter – Building an AI Co-Pilot
🧠 Read here

Where I started in actually implementing this stuff.

5. Marketing Against the Grain YouTube Channel
📺 Watch here

This video specifically helped me understand the power of A) good prompts and B) working with AI once you get it tuned correctly. This channel is a great place to get inspired, and to stay up to date with what’s happening.

6. Wild Bare Thoughts - Taste is the New Intelligence
🧠 Read here

Such a good newsletter on taste (helped me understand just how important taste will be, and some tips on how to cultivate).

7. Brendan Short’s Newsletter – His AI Journey

🤯 Read here

HIGHLY recommend Brendan’s newsletter. He’s one of the people who A) really gets it and can explain his understanding clearly and B) is actually doing stuff. This article was probably my favorite.

8. SaaS + AI: It’s Time to Move Much Faster with SaaStr CEO and Founder Jason Lemkin

📺 Watch here

Great watch to give yourself a little kick in the ass.

9. Looped In – Bridging the AI Skill-Gap

📄 Read here

Kaylee’s stuff is great. This very helpful in understanding how to win as a marketer during this wave. Systems thinking (not some fad, like prompt engineering) will be the real edge for us that want to keep our jobs.

In this newsletter I'm sharing my exact process, all the prompts, and what I learned spending an afternoon with Google's Veo 3.

My video features 7 different AI grandparents reading Gen Z slang while roasting spray-and-pray marketing. Of course.

I have ZERO video skills. But after seeing what people are creating with AI video, I had to try it myself.

My goal: Create a 60-second "commercial" for Keyplay that *might* make people stop scrolling and would definitely help me learn about AI video.

Here's how I got there and what I learned.

The Setup (15 minutes)

First, I created a Claude project and loaded it with two types of knowledge:

• Veo 3 examples and prompts I found online (links at the end of this post)

• Some of my past LinkedIn posts about B2B marketing and Keyplay positioning

This saved me from repeating context with each iteration. Highly recommend this simple step.

The Concept & Script (45 minutes)

I started with a rough idea: What if grandparents tried to explain modern B2B marketing problems using Gen Z slang?

The contrast felt right. We get lost in buzzwords, but grandma gets ICP Marketing.

I used PJ Accetturo's approach from his Puppramin (spoof) and Kalshi (aired on TV) commercials as a role model. Here's my exact starter prompt to Claude:

Act as a viral video producer.

Give me 4 different scripts for a Keyplay video commercial that I'm gonna produce with AI and share on social media.

I am going to make the commercial using Veo 3. Research Veo 3 best practices so that the script can be easily translated into prompts.

The commercial should be no more than 6-8 total shots. Ideally ~30 seconds total. It needs a killer hook.

I want each shot to be a different person in a different situation so it's easy to produce each piece in Veo 3.

The ending bit should be a tag line where it's like "ICP solved" and then cuts to the Keyplay logo.

Approach to consider: Old people talking about B2B marketing. Nobody has 90-year old influencers. Maybe they use a bunch of Gen Z language like bro and W's in the chat and "cook" etc. Little statements about ICP and Keyplay. Then it could end with something like 9 out of 10 grandparents recommend Keyplay. Not sure on this idea just riffing.

The first script was mid.

I spent 30 minutes iterating with Claude. My team jumped in with feedback. Lauren had the key insight: make the grandparents read their lines deadpan, like they're just reading from cards. That's what made it funny.

The Final Script

After iterations, here's what we landed on:

Shot 1 - Grandma Reading Deadpan: Selfie video of 85-year-old grandma with thick glasses, holding camera at arm's length. She's clearly reading from something off-screen in a flat, confused voice: "Spray and pray marketing is... mid. These companies be spending millions targeting everyone. That's giving... broke energy?" She squints at the paper. "No cap."

Shot 2 - Grandpa Struggling: Selfie video of 90-year-old grandpa with oxygen tank, holding index cards. Reading slowly: "Sheesh... bro? Thirty percent of marketing budget wasted on non... I-C-P accounts? That's an... L." Looks at camera confused. "Martha, what's an L?"

Shot 3 - Street Interview: Street interview with elderly woman. Reporter asks: "What's your take on Account-Based Marketing?" She pulls out reading glasses and a crumpled paper: "A-B-M platforms? That's... chew-gy... A-F. Black box scoring has no... rizz?" She looks at reporter: "Is this English?"

Shot 4 - Distinguished Man, Zero Context: Selfie video of 80-year-old man in suit, reading from phone with arm extended: "Intent signals are... sus. These companies are... delulu... if they think predictive platforms... slap." Long pause. "What does slap mean? I thought that was bad."

Shot 5 - Chess Player's Take: Selfie video of elderly Black man at chess table. He holds a brochure that says "Modern GTM Strategy." Reading slowly: "They said... just trust the AI scoring model. That's like playing chess with no pieces." He looks at camera. Long pause. "You need a strategy. Not vibes."

Shot 6 - Eastern European Grandma: Sharp-tongued elderly woman at kitchen table covered in spreadsheets. She deadpans: "You target 10,000 accounts." Raises eyebrow. "You book four meetings." Beat. "My grandson could do better with a phone book and a pencil." Leans back: "Get your ICP together."

Shot 7 - The Resolution: Elderly man at computer desk. Screen glows on his face. He leans toward webcam and says softly: "ICP solved." Nods once. "Grandparent approved." Fade to black.

Shot 8: Keyplay logo + "ICP marketing. Grandparent approved." Note: Added this at the end using iMovie. Real editors use something like CapCut or Adobe Premiere, but you can ship something with consumer grade tools for sure.

Creating the Shots (2.5 hours)

This was the meat of the work toggling between Google Flow (Veo 3 model) and Claude. For each shot:

1. Ask Claude to write a detailed Veo 3 prompt for each shot, based on the script.

2. Paste into Google Flow and generate.

3. If it was off, back to Claude for tweaks.

4. If it was close, just regenerate (Veo 3 can be random).

5. When stuck, I asked ChatGPT for a second opinion.

The key was being specific about style, setting, and audio. Here are three examples with the full prompt. Each took iteration.

Shot 1 Prompt (Deadpan Grandma):

A selfie video of an elderly white woman in her mid-80s with thick tortoiseshell glasses and curly silver hair. She wears a lavender cardigan over a floral blouse. She holds the camera at arm's length, and her thin arm is clearly visible in the frame. The style is YouTube Vlog like. The character squints at a piece of paper in her other hand, occasionally glancing at the camera with confusion. Behind her is a cozy living room with doilies and family photos. She says in a flat monotone voice: Spray and pray marketing is mid. These companies be spending millions targeting everyone. That's giving broke energy. No cap. Sounds: The quiet ticking of a grandfather clock, the distant hum of a television in another room. No subtitles.

Shot 3 Prompt (The Street Interview):

A street interview style video. Over-the-shoulder shot of a young attractive asian female News reporter holding a microphone with a news logo interviewing an elderly jewish woman. An impeccably dressed elderly woman in her late 80s with perfectly coiffed silver hair faces the camera. She wears a Chanel-style bouclé jacket, massive diamond earrings, and designer sunglasses pushed up on her head. Background: A busy day in New York's Times Square. Reporter speaks first: What's your take on Account-Based Marketing. Elderly woman responds by reading from a small card in a refined accent: A-B-M platforms are NOT the answer. Elderly woman continues: Black box scoring has no rizz. Elderly woman stops reading and lowers the card. She looks at the camera, frustrated now. Sounds: Bustling city traffic, taxi horns, tourist chatter. No subtitles

Shot 4 (Distinguished Man, Zero Context)

A vlog show video of an elderly Indian man in his early 90s with wrinkled dark skin and thin white hair. He wears a crisp white button-down shirt with a navy cardigan and wire-rimmed glasses. The style is YouTube Vlog like. Behind him is an elegant study with leather-bound books and framed university degrees. He holds a smartphone in his other hand and reads from it with clear confusion: "Intent signals are sus. These companies are delulu if they think predictive platforms slap." He puts the phone down completely. Long pause. He looks directly at camera and asks in a refined voice: "What does slap mean. I thought that was bad." Sounds: A grandfather clock ticking, pages turning somewhere nearby. No subtitles.

The pattern: specific visual details, clear audio instructions, environmental sounds, explicit "no subtitles" directive.

→ Get all 7 prompts and see my iterations in this Google Doc 📃

What I Learned

This isn't one-shot magic. You work with the AI, not just prompt it. My first two shots took forever. By shot 7, I had a feel for what works.

Veo 3 is a slot machine. Same prompt, different results. Audio sometimes works perfectly, sometimes doesn't. Subtitles appear randomly. Just regenerate.

Simple beats complex. Single speakers work better than multiple. Clear, short dialogue beats long monologues.

Details matter. "YouTube Vlog like" got me the selfie angle I wanted. Specifying "no subtitles" usually worked. Environmental sounds added production value.

There are levels you can explore:

• Level 1: Type one prompt, get an 8-second video. Smile, walk away. 90% of people stop here.

• Level 2: What I did. String 6-10 shots into a story you can use. Now I have the muscle and can repeat when the right idea hits me.

• Level 3: Push quality. Add ElevenLabs audio, better narrative structure, iterate more upfront, test with audiences. I might try this next if I get a good idea.

• Level 4: Professional work like the Kalshi ad (hundreds of attempts + pro editing). If you’re already a great script writer and editor with taste, then this can be a force multiplier.

Cost: ~300 Veo credits. About $25 + the monthly cost of a pro plan.

Time: ~4 hours total. I set a hard deadline (picking up my son at 3:30pm) which forced me to ship v1 instead of endless tweaking.

Why This Matters for B2B Marketers

Most AI video examples online are B2C. Consumer brands making funny ads. Fashion shoots. Movie trailers.

But B2B marketing needs video too.

The tools are here. They're only getting better. And there's no playbook yet.

That's the opportunity.

We're all just figuring this out. There are no AI-Native marketing experts because nobody's been doing this long enough to be an expert. The best people are just trying stuff, finding leverage in surprising places, sharing what works.

This video won't win any awards. But it made people stop scrolling. It started conversations. It showed our positioning in a way that a blog post couldn't.

And the best reward: I now have a baseline understanding of AI video that I can build on. I know a little more about how it works, what it can and can't do, and have planted the seed for future ideas.

That's worth an afternoon of exploration.

Resources That Helped

• Greg Isenberg interview with PJ Accetturo - Watch this first

• Kalshi Video Breakdown - See the prompts

• Veo 3 Prompt Advice - Technical tips

• Example Veo 3 Prompts Collection

• Google's Getting Started Guide

→ Get all 7 prompts and see my iterations in this Google Doc 📃

Next time you have a marketing idea that needs video, try it. Set a deadline. Ship something. Learn.

The future of B2B marketing isn't copying someone else's n8n workflow or following a checklist.

It's exploring these tools yourself and finding what works for your story.

Seen any great examples of AI in B2B Marketing? I’d love to feature them here!

Kipp Bodnar, HubSpot's CMO (scaled marketing from startup to $30B leader), told me something uncomfortable for marketers:

"The types of metrics that people cared about a couple years ago are far less relevant today than they have ever been."

Marketing math is getting rewritten with AI. And Kipp says it's the most fun he's had in the last decade. He was my guest for episode 9 of the PeerSignal.org podcast.

What we covered:

• Why he's 10X more productive than pre-AI (with details).

• His "Express, Feedback, Tailor, Amplify" framework for AI campaigns.

• How they increased email engagement 100-400% with AI personalization.

• How he gets his 600-person team obsessed with AI.

• Where smart CMOs are investing or cutting back.

• Why "taste at scale" will separate winners from losers.

And so much more.

Listen Now

My favorite takeaways:

1️⃣ Better, faster, AND cheaper isn't a trade-off anymore.

When someone brings Kipp an idea, he asks: "Is it better, faster, AND cheaper?" If not, he won't do it. Most marketers still think you have to pick two. They're wrong.

2️⃣ AI isn't just automation, it's creative leverage.

AI models are trained on everything that's already been created. If you're still running campaigns the same way you did 2 years ago, you're already behind. He wants humans who can create with AI.

3️⃣ Taste is the new competitive advantage.

"As much as I want you to learn AI, I want you to learn how to have good taste." Most marketers think prompting is the hard part. They're wrong. The hard part is knowing good from garbage.

4️⃣ We're still early in the AI wave.

Search engines were around for years before SEO was born. Hubspot built a movement on top of this years later. ChatGPT launched 2 years ago. We're still very, very early (as easy it is to feel behind).

Here's the uncomfortable truth most marketing leaders won't admit:

The internet is littered with people selling "copy my exact system to become AI-native." Reality: There's no playbook, no experts. This is all being figured out in real time.

People like Kipp are curious learning machines. They try a lot. They find leverage in surprising places. They push and pull constantly. These are the people to study.

What's your take. Does Kipp's thesis on AI + marketing track for you? Or does this sound like hype?

Let me know in the comments.

Let me be clear. I don’t have the answer.

I'm just a 42-year-old trying to adjust to this new world. I'm reading and talking to smart people who say that becoming “AI-Native” is an imperative in all aspect of business and marketing:

• VCs are telling us AI or Die.

• Elena Verna explains how legacy SaaS will struggle to keep up with fast-moving AI Native employees and companies.

• Manny Medina says we need to turn up the volume to 11 on agentic experiences.

• Kipp Bodnar told me he's gotten 10x more productive with AI (I'm probably around 1.5x). He’ll be on my podcast next Thursday.

I feel my blood pressure increase as see this stuff. The FOMO hits hard.

But I also agree 100%.

I know that I need to unlearn all the "best practices" and "playbooks" of the last 15 years. The frameworks that got us here won't get us there.

I'm optimistic overall. I'm glad we finally have something NEW in marketing.

If this plays out in the positive ways, we'll have a lot more creative leverage. Taste and customer understanding will be the only thing that matters. We'll remove barriers and specialized skills. We'll be able to do really creative and interesting work.

But right now? We're all just figuring it out in real time.

The FOMO is real. The opportunity is real.

The Problem: All Vision, No Receipts

Here's what I'm finding: most of the information is very high level.

It strikes me that it’s impossible for anyone to be a true expert. Nothing is battle tested. Everything is experimental.

A bunch of people saying "vibe marketing" or "use AI or die" but not really showing the how to transition.

So it's this painful mixture of FOMO, excitement, and lack of concrete examples.

That's why I'm on a mission to find real examples of AI in B2B Marketing.

The Examples I'm Studying

I really appreciate the people who are sharing actual experiments. I’ve started to collect the examples that pique my interest.

Here’s what’s in my AI + Marketing folder so far:

• Tom Wentworth's “vibe coding for marketers” experiments. He build a game, a lead magnet, and a fully customized 1:1 landing page for Anthropic. He’s posting about these experiments and sharing how to videos.

• PJ Accetturo created an insane AI commercial for Kalshi that aired during the NBA Finals. This was the first “holy crap” moment for me with AI video and how it’s likely to change advertising. All the shots are made with Google’s Veo video generation model. PJ even shares a couple of the prompts used in this X thread.

• Stan Rymkiewicz 1:1 personalized ABM Ads experiment. Stan built a tool to generate personalized ad creative for 100 target accounts and run a $10K experiment on LinkedIn Ads. Stan is honest that the experiment didn’t pencil, but I think it’s a great example of on many levels. Stan built a custom Figma plug-in using AI to power this campaign.

• Kipp Bodnar (HubSpot CMO) shared a super specific approach to creating content for LLM Search. He even gives access to the custom GPT he built. Everyone is talking about LLM Search optimization or AEO, but this actually shows how you can take actions:

• This LA Dentist who is making AI Bigfoot vlogs for social media. You’ve probably seen these. AI video is going to unlock some hilarious, creative marketing like we’ve never seen before. It will even be accessible to small local businesses.

• Amir Ariely created this AI generated ad for Liquid Death (on spec). Another amazing example of what’s possible with AI video. This is already getting very popular in B2C. There must be an application in B2B as well.

• Ant Mancini created this a community-source prompt library for visual design examples at Prompty.me. With MidJourney, ChatGPT, and others getting way better image generation in the last few months, it’s very helpful to see how visual designers are prompting AI. I think lots of these principles can be applied to marketing. Everyone is talking about text generation, but image generation is going to be huge.

• Tim Davidson is clearly have fun with AI video on LinkedIn. I love watching people do this kind of tinkering. Tim has a series of these fun videos where his characters are running around in space suits talking about B2B marketing.

Help Me Find More?

If you're doing something real with AI in marketing, I want to learn from you.

Reply to this email with:

• What you're experimenting with

• What's working (and what isn't)

• Specific tools and workflows

• Results you can share

I'll feature the best examples in future editions.

We need more practitioners sharing the messy reality, not just the vision.

He created the sales engagement category by riding the SaaS wave from 2014 - 2024.

Now he's on the cutting edge of a new wave: The Agent Economy.

While most of us are in the fog of AI war, Manny has inspiring clarity on what this new world will look like, why, and how to win in it.

That’s what we cover in this episode. If you care about winning with AI, it's a must-listen.

Listen Now

What you'll learn in our conversation:

• How to compete when every feature can be copied by AI in minutes

• The "one box" revolution and what it means for AI

• The "rent experience, hire hunger" framework for agent-era teams

• Why agents need to think, suggest, and act (not just automate)

• How the best leaders will adapt to this new paradigm

• How to demo right in the agent economy (not just slapping AI on your homepage)

My take-aways from our talk:

1️⃣ We're in software's 3rd Act, Outcomes.

Act 1: Systems of record. Act 2: Workflows. Act 3: Outcomes. In this new world, you need to deliver "receipts not dashboards". This has ramifications throughout your org structure.

2️⃣ "Love is not a metric".

Love is not a metric. It's a feeling. It's the words they use. It's when an SDR tells you, "you'll have to pry Outreach out of my cold, dead, hands." The best leaders optimize for love and don't stop until they get there.

3️⃣ "If you're worried about TAM, you haven't found your edge yet"

Every leader has a choice: sell to everyone, and be relevant to no one, or take a stand and actually break through. In this 3rd wave, the winners will be the leaders courageous enough to take a stand and be truly different, even if there's no "TAM" to back it up.

4️⃣ Build backwards from customer success, not forward from features

"What does the customer want? They want a meeting in the least amount of time. They don't want 12 steps. They want one step. Matter of fact, they want zero steps." Winners don't retrofit the human to the technology. They start with the outcome, and work backwards.

Full Interview: https://www.peersignal.org/podcast