Mark Kilens

TACK

TACK ⛵️ is a media and go-to-market firm helping startups and scaleups match how they go-to-market to how people buy. Modernizing how businesses go-to-market to create, capture, and convert demand into revenue. 

Companies like Commsor, ZoomInfo, Swoogo, Coursera, Aiven, CloudBees, Audette, Terminus, and more partner with TACK to evolve how they go-to-market using a People-first GTM approach. 

Its mission is to match how companies go-to-market to how people buy by making every experience and interaction with a business more human.

TACK Network is a GTM media network of content and resources all about B2B go-to-market. We produce original shows and authentic stories giving some of the brightest people in B2B go-to-market the support they need to share stories and ideas.

TACK Insider is for marketers and entrepreneurs looking to learn how to go-to-market to match how people buy today. Members learn how to use seven different growth strategies that are all part of the People-first GTM Model.  The membership and community comes with hundreds of resources, weekly masterclasses, and personalized coaching.

TACK GTM is Uber for GTM Leaders. An on demand go-to-market services firm specializing in People-first GTM strategies and services that grow pipeline and revenue. We partner with CEOs, Founders, and Marketing Leaders to design and deploy People-first GTM strategies.

Chart your go-to-market with TACK.

CEO and Co-Founder

Stage 2 Capital

The Stage 2 Capital Catalyst supports early stage B2B software companies in scaling their businesses by combining Venture Capital and Go-To-Market, empowering founders with hands-on coaching from an unrivaled network of go-to-market leaders.

Limited Partner and Advisor

Airmeet

Airmeet helps marketers book more meetings and create pipeline faster using premium webinars and online events. 

Airmeet's Event Experience Cloud is an all-in-one premium webinar and event platform helping B2B businesses generate more pipeline and revenue.

I lead Airmeet’s global marketing team and are working to advance the event-led growth movement.

Chief Marketing Officer

Drift

Content and community are key ingredients to accelerating a business's revenue, delivering more customer value, and building an enduring brand.

My team's partner with Drift's marketing, sales, and customers success teams to develop the right content, programs, and experiences to accelerate revenue, deliver more value to our customers, and grow Drift’s brand.

Get your 100% free Drift Insider account. https://www.drift.com/insider/

Check out a couple of Drift's newest digital books. https://www.drift.com/books/

The Conversational Marketing Blueprint: https://www.drift.com/books/conversational-marketing-blueprint/

The Guide to Conversational ABM: https://www.drift.com/books/conversational-abm/

Learn something new on Drift's blog: https://www.drift.com/blog/

Vice President of Content and Community

HubSpot

I was the VP of Marketing and founder of HubSpot Academy.

HubSpot Academy is a global learning community and platform with millions of people in more than 200 countries.

It’s a tremendous growth driver of demand and interest - signing up and certifying tens of thousands of new HubSot users and community members every month.

We focused on converting free users into software revenue and helped retain and grow that revenue by building onboarding, expansion, and community programs.

I had the honor and privilege to lead a team of extremely passionate, motivated, and intelligent people. 

HubSpot Academy's purpose is to educate and inspire people so that we, together, transform the way the world does business.

We created content and education to teach people the inbound methodology and how to use the HubSpot platform to grow their business.

Vice President of HubSpot Academy

HubSpot Academy was launched in May 2012 after running it as an experiment for 18 months. 

I was responsible for creating the Inbound Methodology with Brain Halligan and Dharmesh Shah. 

I updated the original methodology in early 2018 to HubSpot's new Inbound flywheel model. 

We created content and education to teach people the inbound methodology and how to use the HubSpot platform to grow their business.

HubSpot Academy's purpose is to educate and inspire people so that we, together, transform the way the world does business.

Director of HubSpot Academy

I was a Senior Inbound Marketing Consultant at HubSpot. I worked with B2B and B2C businesses helping them create, execute, and measure their inbound marketing goals and strategies.

Senior Inbound Marketing Consultant

I joined HubSpot as an Inbound Marketing Consultant. I trained businesses how to use HubSpot to get found, convert leads, close leads into customers, and analyze their results.

Inbound Marketing Consultant

SalesQuest

I was responsible for email marketing campaigns, building prospect and customer databases, social media, inbound marketing, lead generation, the website, and helped create new products and services.

Director of Marketing

I managed a team of market research analysts that created and maintained a library of more than 850 Fortune 1000 and Global 500 company profiles. 

I was responsible for all inbound marketing activities, SEO, social media, and marketing campaigns.

Manager, Marketing and Operations

I performed research on Fortune 1000 companies which included investigating their technology implementations, org charts, budgets, financials, and operating models.

Research Analyst

CEO and Co-founder of TACK ⛵️ & ClubPF ⚓️ | People-first GTM Champion

Mark Kilens is the CEO and Co-founder of TACK, a media and go-to-market firm helping startups and scaleups match how they go-to-market to how people buy. TACK modernizes how businesses go-to-market to create, capture, and convert demand into revenue.

TACK helps B2B organizations transform how they grow using its proprietary People-first GTM model and approach. TACK'S mission is to make every experience and interaction with a business more human. Companies like Commsor, Swoogo, ZoomInfo, Casted, and UserGems partner with TACK to unite their go-to-market teams to grow more efficiently and effectively.

He previously was CMO of Airmeet, a leading virtual and hybrid event platform. He oversaw Airmeet’s global marketing team responsible for brand, demand, lifecycle, and product marketing.

Before that he was the VP of Content and Community at Drift where he led the brand, content, creative, and events teams. He oversaw Drift’s community, Drift Insider, which has 65,000+ members, and Drift’s FLASH, RevGrowth, and HYPERGROWTH events.

Prior to joining Drift, he served as VP of Marketing and founder of HubSpot Academy. As a former customer, Mark joined HubSpot in 2010 where he spent two years working with thousands of HubSpot customers to scale their inbound strategy. 

From there, he went on to build HubSpot Academy from the ground up, and in doing so, educated and certified millions of people and helped grow HubSpot to more than $600 million in revenue.

When he’s not building TACK, Mark enjoys plenty of steak and lobster, a round of golf or two, and loves being on snow or in the ocean.

Go fast, take chances!

Leading Airmeet’s global marketing team and working to advance the event-led growth movement across B2B.

Airmeet is the most customizable and immersive virtual and hybrid event platform for growing businesses. 

Here's how I almost failed scaling HubSpot Academy.

At the time, HubSpot was becoming a global company serving several different languages outside of just English. 

It was tricky. Do we create native content in each language? Do we translate some of the content but not all? Do we use automated translation solutions?

I can tell you this. The automated translation solutions we tried and used at the time were not very good. 

Globalization and localization. It’s hard. 

There were thousands of hours of video, transcripts, and website content. It was an immense undertaking. 

Here’s how we solved this complex problem. We implemented a tiered system for the localization and translation of all content assets. For some content we localized and translated the piece. For other assets we just translated the content. 

That tiered approach saved us a ton of time and money over the long run. 

Since then I’ve advised many companies on how to think about content localization and translation. In the last few years a lot has changed thanks to generative AI. 

Recently I came across a new solution to help solve this problem. Something I wish I had back in 2015. Weglot.

What’s Weglot? 

It’s a solution that simplifies website and content translation. Some of the things I faced when doing this ten years ago were manual translation, page duplication, complex SEO setup, and technical integrations. Painful and time consuming. 

The number one thing you should be doing today is speaking the language of ALL your customers. Did you know that more than 70% of people prefer to buy in their native language

It’s now easy to expand your international presence in just a few clicks. You can do it quickly thanks to automated translation that’s accurate and crisp. 

Increase your SEO visibility in every market that matters. More markets = more reach = more potential customers. 

And thanks to solutions like Weglot, it's easy to integrate with WordPress, Shopify, Webflow, Wix, and more. In just a few minutes. No need to move to a different CMS. 

I recently decided to partner with Weglot to help businesses of any size create a more global footprint and make sure their content is speaking the language of their customers. 

It’s more important than ever to make sure your content is in the right language both for people and the bots. 

#WeglotPartner

p.s. still rocking my favorite HubSpot Academy custom Patagonia.

VP of Marketing at EasyLlama | People-first GTM Evangelist

Don’t fall into the status quo.

Here’s my marketing playbook for 2026.

1) Major investments in brand. Which is combo of content, events, community, and customer programs. 

I was so fortunate to bring on a rock star for all those things Katrine Reddin 🦕. She of course will have support and we’re going to be playing offense next year.

Expect to see the 🦙 everywhere. With some major surprises. 

2) Intent and network signals.

We’re an inbound heavy operation which means we can use signals to prioritize leads, follow up with other leads, and nurture a lot better.

Plus we can use network signals like if someone in our network knows someone that could unlock an introduction for us or give us some intel. 

I’m betting big on network signals next year. Both from our customers and our broader network.

3) Holistic revenue attribution.

We’re going to be tracking three types of revenue attribution.

Software attribution, first and last touch.

Self reported attribution in our demo forms.

Discovery call question attribution asked by each AE.

We have two of the three already in play and the data is fascinating. 

I’m also going to add incremental testing to the mix. Do something and then remove it or isolate the removal of the activity like not doing a billboard in one city but having one in another city, and using lead and pipeline data to determine impact. 

This data will help us understand number one and two a hiwnlot better next year. 

4) Monthly marketable moments. 

We already ship a lot of new products and tools in the reg. But next year we’re going to package them better and create one big wave each month. Beat the drum!

5) New offers.

We are cooking up come very compelling free new offers to launch in the first half of next year. I can’t give you any sneak peeks yet, but you should be thinking and investing in offers that will help you turn brand attention into business revenue.

Offers that are differentiated and compelling are key to your demand strategy today.

Don’t forget that your offers should treated as products built by the marketing team. Positioning, promotion, and distribution all matter.

I got a coupe of other ideas brewing. Going to wait to share them until I have more conviction behind each idea. 

If you think these topics are interesting, then you should join my marketing leadership mastermind group. We meet every week for an hour. 

Today we’re chatting about the homepage redesign process and the dashboards a marketing leader should be using to run their business. 

Drop ‘mastermind’ in the comments for more info.

Data is the lifeblood of a business today. Yet most businesses are missing two things. 

Clean data and actionable data. 

This problem costs businesses thousands, if not millions, of dollars in revenue.

What every leader should have are insights into revenue generation, customer health, competitors, and a whole lot more. 

Trust me, I have felt this pain at all of the businesses I’ve worked at. We're actually working on solving this problem at EasyLlama right now. Through a combination of hiring the right people, tech, and process changes. 

One solution that I’ve been trying out is Rows.com  

I’ve been able to upload CSVs and PDFs, connect GA4, and our paid media accounts.  Then I simply ask it questions in plain English.  Way easier than me trying to fumble around with Google Sheets or Excel. 

It organizes and understands the data without me needing to understand SQL or anything else. 

Things like, ‘tell me how many opportunities find us through paid search and also mention Google as how they heard about us.”

Simple prompts that can give me quick and impactful insights into how different channels, campaigns, offers, or messaging are driving results. 

It extracts, connects, analyzes, and keeps all the data fresh. 

Which means I get automated reporting and get insights and actionable recommendations on the regular. Exactly what a busy marketing leader needs. 


My point of view is every company should have at least one business analyst. But that’s not always possible due to the cost involved or other factors. So why not give that power to your founders, and other executives, heck your individual contributors too!?

It’s now possible for you to create your own business analyst copilot/agent helping you analyze and sift through data to find the golden nuggets. I’ve built my own business analyst at a fraction of the cost.

Rows is helping me flex my critical thinking skills and become an even more data empowered marketing leader. 

Helping me see around the next few corners and better plan and predict for the coming quarters. 

You can try it for free yourself. Rows dot com

Product launches are always exhilarating ... and stressful ... but so rewarding.  

Today, I'm proud to announce the third product pillar in EasyLlama's Compliance and Learning Platform. 

Security Awareness Training 🥳

Lots of small and medium businesses should be protecting their business and brand from cyber threats. 

Most don't becuase of two reasons, time and the cost.

We eliminate both reasons and give you a solution that's less than $2/month per employee. For a 150 person business, you can provide an important layer of protection from bad actors and cyber criminals for only $250/month.

It includes a powerful Phishing Simulator powered by AI.  

Giving you the ability to use pre-built phishing campaigns that can run automatically year-round.

You also get detailed ongoing reports so you can have a complete view of your organizational risk and how to better prepare employees to not be fooled.

It's time to: finally...

Prepare. Replace passive, one-off cybersecurity courses with interactive, scenario-based training that builds employee confidence and real-world readiness.

Produce. Demonstrate measurable results with dynamic risk reports and benchmarks that connect safer behaviors to stronger business outcomes.

Protect. Reinforce safe habits through continuous phishing simulations, instant feedback, and ongoing awareness campaigns that reduce risk all year long.

We now offer SMBs ONE platform to manage all of their compliance, security training, employee onboarding, and team training needs. 

All-in-one for the win. Finally one tool that employees need to use and one solution you need to manage and pay for. Nirvana, right?

A massive thank you to the team members behind this big launch. ❤️

From the folks on the product and engineering side, Lawrence Valenti, Michaël Devyver, and Merry Chin. To the people on the GTM teams, Alicia Falcon, PMMC®, Tan Tran, Katrine Reddin 🦕, Carmen Choi, Hana N., and Laura B..

A great product launch take an immense amount of coordination, planning, and execution. We use Asana, Google Doc/Sheets, and Slack to make it all happen. Of course Figma, Canva, and our CMS have something to say too.

Here's how to create the best possible webinar. 

I call it the webinar triple threat play. 

Yes, I did steal that from Bobby Flay's Triple Threat show. 🥓

Here's how to cook it up. 

By the way, I shared this playbook in my weekly marketing leadership mastermind group yesterday. Let me know if you are interested in joining. 

Start first by creating a webinar outline and agenda that has THREE distinct segments. 

1) A 20 or so minute conversation with a thought leader or subject matter expert focused around the webinar's topic. 

2) A ten minute product demo that ties what the thought leader spoke about with the use case of the product. Make sure at the end of this segment you ask the audience, using a poll in the webinar, if they would like to get a full demo of the product they just saw. I usually see about 5% to 20% of the attendees asking for a demo. 

3) A customer speaks for about ten minutes and shares their perspective on how they are using the product and the benefits value it's creating for them and their business. 

Make sure you have a DYNAMIC host. Their job is to host the entire webinar and to facilitate each segment. Getting the audience fired up, engaged, and keeping the webinar on track and managing the overall webinar experience. 

Use something like Wistia's webinar platform to make the webinar feel like a well produced show so what people are seeing is changing every couple of minutes. The key is making it not boring. 

Once the webinar is done, you now have three amazing pieces of content to repackage and reuse. 

One that's thought leadership centric, one that's very product focused, and one that is your customer proof and voice of customer segment. Wisita makes it super easy to create clips from each segment and for you to turn those clips into a format that's right for the right channel. 

So there you have it. My tasty Webinar Triple Threat Play™ 

We're run this playbook many times at EasyLlama and it never fails. 

Drop a note if you want me to share an example of one of our latest webinars using this format, or if you want to join the mastermind group. 

Happy Webinaring!

Don’t start your strategy by thinking of a tool first.

It never works. 

Always think about your STRATEGY first. 

For me it's, 'What is the best way to win the game I’m playing?'

For people in marketing and sales the answer is not AI. 

The answer is how can I best match how I’m marketing and selling to how they like to buy what I’m offering. 

For some businesses, it’s more a PLG motion. For others it’s PLG + Sales-assisted. And others use an enterprise-grade consultative GTM motion. 

But no matter what you’re trying to sell, using buying signals is a smart part of your strategy. 

Buyer signals are key to helping you understand what a buyer is doing so that you can match your approach to them. 

You can start really simple by first identifying what are your low, medium, and high intent buying signals. 

Low intent would be downloading a piece of content, going to an event that you hosted, or reading your newsletter. 

Medium intent would be checking out your pricing page, going to a webinar that includes a product demo, or researching competitors. 

High intent would be signing up for a fee trial or account, requesting a demo, or raising their hand for more info during a webinar. 

Signals can be at the contact or account level and include activity data from any first-, second-, or third-party data source.

Once you know your signals and where to find them. Don’t forget that part. :) You can then determine how to act on each signal. 

The easiest way to think about this is what signals are marketing-led, sales-led or a hybrid approach.  

Alright so we now let’s talk about tools. One of my favorite tools I’ve used to run a signal-based GTM strategy is Common Room.  They make it easy with their RoomieAI suite to find, organize, and act on all the buying signals. 

The key, IMO, is to try and keep the signal architecture in one centralized place. Much easier to manage and maintain. Helps streamline the prospecting motion of creating deals and using signals to close deals.

One of the most important things for your signal-based strategy to work is making sure the salespeople know where to see and how to use the signals. Sure, automation can help, but when a salesperson is given a power that they didn’t have before and knows how to use the power responsibility, great things can happen. Take a look at the image to see what a salesperson could get.

Things like faster lead follow up, meeting the buyer where they are, sending the right message at the right time, and reaching out to the right stakeholders to increase the likelihood a deal closes. 

The big takeaway is to please make sure you design your GTM to match how people buy today.

My new community and resource hub is open for business. 

It's called The GTM Library.

A hangout for founders, fractional folks, and marketers who wants to sharpen their go-to-market skills and execution.  

I'm an entrepreneur at heart which means I'm obsessed with all things go-to-market.  I've built a pretty big private collection of my favorite go-to-market resources, including a 100+ masterclasses from people in my network.

When you sign up you'll get access to the community and my regular masterclasses. I'm hosting a new one in a couple of weeks about my latest positioning and messaging framework that I've built. 

For a small one-time payment you can get your library card and access to my private collection of resources and masterclasses, including a private call with me. 

Hope you find it valuable. 

https://lnkd.in/eK27VGsG 

p.s. that's me with one of my favorite books about entrepreneurship and leading a business.

Oren Greenberg

B2B Marketing Led Revenue | Marketing Advisor Scaling Tech Companies

Smart companies aren't scaling their GTM teams. They're scaling systems powered by AI.

I help B2B tech revenue leaders (CEO/CRO/CMOs) build AI-first GTM systems that generate pipeline. Typically, technology mid-market.

GTM engineering has shifted from optimising what exists to building entirely new revenue engines, where one AI-powered marketer produces output equivalent to that of 5 traditional team members.

Efficiency is the route to increased effectiveness.

My toolkit includes Python, Google Colab & Cursor. Also Clay.com

I leverage data science & statistics to shift from anecdotal debates to scientific conclusions.

Starting with foundations by looking at patterns in your customer data e.g.:
- Who your ideal customer profile is
- Why they buy from you specifically
- How to find similar accounts to target

Using these insights, I help the sales team know which prospects & accounts to prioritise.

This results in:
- Increased sales velocity
- Less churn
- Lower cost of customer acquisition
- Higher lifetime values

Companies using AI-native GTM systems achieve:
* 5-25% revenue increases
* 30-70% higher conversion rates
* 5-15 hours/week recovered

What does that look like in practice?

Strategic direction:
* Ideal customer profile segmentation & prioritisation
* Automated competitive intelligence monitoring

Insights:
* AI-driven account research
* Signal-based triggers
* Predictive lead scoring

Automation:
* Content engines producing assets at scale
* AI lead routing & qualification
* Multi-channel sequences 

Pipeline acceleration systems:
* AI-augmented outbound
* Predictive lead scoring

I help you leverage the intersection of GTM with data science to drive growth.

Background:
* 20+ years building revenue systems for B2B SaaS (HandsHQ, Redbrain, Peakon, SeedLegals + enterprises such as BT, Lenovo, Canon, Investec Bank)

* Managed millions in marketing spend

* Scaled bootstrapped agency to 7-figures, now I focus on consulting

I help teams become category leaders through system building. 

Find out more + get your AI marketing capabilities score → orengreenberg.com

Founder & Fractional chief marketing officer

Kurve is a boutique digital marketing agency scaling tech businesses into hypergrowth.

I’ve been hospitalised.

Got hit with the flu that’s developed into a respiratory complication.

Fighting an unpleasant fever; I asked the doctor what the best remedy is and they said posting on LinkedIn.
I don’t argue with experts.

For all the flaws of the NHS it’s pretty remarkable how efficient hospitals are. 

Doctor was amused we were chatting to Claude about medical history as that’s all living in a project I setup (Covid messed me up with a nasty dust mite allergy so the past five years have not been easy).

Gutted I won’t make it to TuringXSystm
fest on Thurs. Brilliant speakers and lovely humans - recommend you check it out.

Scaling B2B SaaS & AI Native Companies using GTM Engineering.

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And people say cold email outbound is dying. Can’t for the life of me figure out why.

A leaked OpenAI file reveals interesting insight from user interaction tracking.

Specifically just how how few clicks chatGPT sends.

For one top-performing page, the stats:

- 600k impressions
- 4k clicks

That’s a 0.69% click through rate.
Not great. On most paid search & social platforms you’re looking at 2-7x more on average.

More stats revealed:
Best individual page CTR: 1.68%
That’s the max you’ll be getting (at an unlikely stretch).

Most other pages: 0.01%, 0.1%, 0%.

0% means you could be putting in a lot of effort to optimise for rankings in gen AI without viable ROI.

Across my clients & network gen AI is generating good quality enquiries & customers. So even trace amounts of (relevant) traffic can make a difference.

Keep in mind that although the tech is sweeping it is new which introduces ambiguity & volatility. This doesn’t make planning any easier for revenue leaders.

This is a symptom of the overall shift we’re seeing with moving from quantity to quality in GTM.

graphical user interface, text, application

Here's my view (backed by research) on the marketing skills most at risk due to AI disruption.

Beyond language-related skills (basic writing, email marketing) the tasks next to be on the chopping block are:
• Repetitive and procedural
• Include limited decision-making authority
• Lacking in specialist know-how

Yes, entry-level roles are at risk.
The shift will result in students & entry-level functions spending longer academically developing specialised skills before entering the job market.

The traditional generalist approach to hands-on experience across multiple domains is in decline. The shift is now primarily technical & data-led + system design & orchestration.

This doesn't mean hiring AI-generalists who'll manage numerous specialist agents. This POV underestimates the importance of ecosystem context in knowledge-based roles. Companies need specialists with AI chops, not AI-first generalists.

Understanding which categories are growing, which players in each category are hot, which channels present growth opportunities. The best GTM experts I know live this daily - accessing specialist knowledge only available in dark social & private relationships. Generalists aren't in those circles.

Marketing will evolve to become less 'soft' and 'hazy' to more predictive & systemised. Just as marketing automation, performance marketing, product-led growth and data-analysis continue to grow in importance.

The data I've presented before supports this - generalist marketing roles have shrunk 5-7% over the past 5 years.

This is already happening in more advanced companies. With specialised terms like GTM engineering, AI ops and variants of growth marketing evolving.

So what skills are needed now and in the near future?

1. Context engineering (previously known as prompt engineering). Understanding and managing the right information to feed in and manage. With basic prompt-structure know-how.

2. Vibe-marketing/coding. Being able to use Loveable / Bolt and similar tools to create assets. e.g. ROI calculators, dynamic quizzes. Lite software tools for buyer enablement.

3. Deploying AI agents. These serve as always-on LLMs to take on specific tasks. Mostly recurring mundane ones like enriching inbound leads with info from website.

I cleaned up 25k unread emails in 20min using AI. You can do it too.

Context: I’ve managed to accumulate many newsletters over the years. In a desperate attempt to consume as much as I can to become a super-brain.

At one point the majority ended up on the promotions tab in Gmail. Because my super-plan became an overly ambitious super-nightmare (common sense for most people but I like learning things the hard way evidently).

Step 1. Ask Claude what the process is to do this.
Step 2. Follow the steps outlined.

Here’s what it looked like for me:
- Admin credentials in Google admin to setup API access
- Python script run locally from Terminal

This sounds technical but it isn’t, more like learning new tools. No coding required.

Gmail can identify newsletters via those that have unsubscribe option.
Then mass delete all emails older than 30 days.

I’ve now setup multiple forwarding rules & labels. The way I do that:
- 3 priority buckets
- Newsletters labelled under each priority bucket

For examples emails from software subscriptions I pay for are all nested in own labels under priority1.

So now I have all new newsletters neatly categorised into their own labels with only important emails hitting my inbox.

Yes I could just pay $15/mo for email management software. But that isn’t as much fun.

Some of the cooler uses of Nano Banana Pro I've seen.

Implications for marketers with the rate of advancement:
- Less reliance on designers for infographics
- Less reliance on designers for creating ads

Meaning more creative variants to find best performers (within constraints of distribution budgets).

But also more noise making it harder to push out remarkable ads.

I predict that in time there will be a strong counter-culture shift to having to show the authenticity and human-touch behind creatives produced. It'll happen in B2C first but will eventually hit in B2B (500-600 years later or so like most things).

This will result in a cull of low-level designers. Where the top ones shift focus to creating new creative concepts & design-led innovation.

There's still areas top designers won't be affected:
- Logo design (for now)
- UX/UI components (for the near future)

Tried nano banana to create visual from a PDF. Total fail. 

But, then asked it to summarise and extract key points.
Then create image.

See attached output.

Input file was Semrush's recent report on AI visibility (131 pages so not surprising AI couldn't ingest and make a clear image out of it).

Point is: break down your tasks when you make an ask for better output.

Don't think folks in traditional GTM/SaaS are quite aware of how hard they are going to get hit due to AI. Mostly due to vibe-coding.

All interactions related to:
- Learning a specific UX workflow
- Having to use Regex / Javascript / Python to work with data
- Stitching up multiple sources via ETL tools
- Setting up automations (Zapier/Make/N8n) through knowledge of specific nodes/modules/vendors

Put another way - any tool that follows a procedural or linear process related to data that would live in a database or Excel file.

Are all going to be made entirely defunct in the next 12-36 months. AI is already doing a decent job of catering to the above jobs-to-be-done.

MCP integrations will accelerate this due to ease of access but the real transformation will be due to foundational model improvements combined with progressively lower costs.

Lower costs means running thinking-models for longer plus running more agents to gather richer contexts leading to better quality and more accurate outputs. As the semantic-layer (helping the AI agents understand relevance) improves, this will further accelerate adoption.

Cutting-edge tools like Clay and Notion are already implementing chat-first UX.

This doesn't mean GTM-related expertise will shrink and be replaced by generalists. Rather a shift to orchestration & management of multiple agentic workflows.

This includes a deeper understanding of who the best data vendors are for specific use cases (e.g. contact data, social engagement scraping, technographic enrichment) which forms the basis of all effective GTM.

The many pains that block growth, such as poor quality records in CRM, manual and conflicting marketing automations, are being replaced by AI-first mechanisms.

The barrier for entry will drop, resulting in more GTM-engineers/strategists as traditional SaaS sales & marketing roles evolve.

"Wanted your thoughts about gtm these days, how to win attention through all this noise" someone asked me on a DM this week.

My answer:
There's a few ways to win attention with so much noise.

Triple caveat: strategies vary depending if you're: 
1. Small/mid/large
2. Targeting SMB/mid-market or enterprise segments
3. Your target account market size

What I'm seeing:
1. Spend lots of money. On ads. Influencers. Flashy big events and intimate dinners. If you've got the budget.

2. Do the above, but don't spend a fortune. Be selective with which influencer you work with. Which keywords you bid on. Which audiences you target. Run fewer events.

3. Produce unique content.
Smaller companies like Wynter or HockeyStack pack a punch there.

Mid-size ones: 6sense, Semrush, Gong, Mixpanel.

The large ones...well, we all know the multi BN ones.

If you don't have unique data then leverage partnerships or conduct qual/quant research for differentiated content. As long as it's insightful/actionable, it'll work.

4. GTM engineering is working.
Leveraging AI for workflows, automations, enrichment and prioritisation.
- Signal-based prioritisation
- Automated personalisation engines
- Research automation to support SDRs/AEs
- Smart lead routing (based on ICP)
- AI cold email outbound (only for SME, doesn't perform well for mid-market & enterprise)
- Tracking champion movements across businesses

Having clean data (e.g. in your CRM). Having your tech stack integrated and working well.

5. Baking in distribution into content creation rather than leaving it as an after-thought.

6. Leveraging founder brand. Although this is hard. It's fairly slow. It works best when the company is growing quickly. And not many examples of people doing it really well (but the ones who do are seeing great return on effort). Pulling in your team as well to maximise reach & opportunity.

7. Including warm outbound and intent or inbound-led outbound, there are also opportunities for partner marketing. Someone I spoke to the other day said a fair chunk of their pipeline comes from a niche industry-specific CRM partnership.

ABM & social selling work. When done well.

What doesn't work:
1. High volume content production in a world moving towards quality over noise - this is true across most B2B channels.

2. Going broad multichannel beyond your budget means you don't cut through on any specific channel.

3. What's waning in potency: lead gen, while demand gen is getting momentum. SEO while AEO/GEO is growing.

4. Complaining that a few giant platforms monopolise the world and we're all forced to pay taxes to them in one form or another. It's true, but complaining about it won't help you drive growth.

I didn't highlight specific channels. That's because they are often dependent on a hyper-audience-specific. Broadly speaking the lower the account value the more digital marketing plays a driving force. The higher the account value the more face-time you need.

Pretty nuts if you haven't seen this, Elevenlabs cloned my voice in under 15min.

I've been deep-diving into their platform recently. If you haven't heard of them, they are one of the most interesting rocketship unicorn businesses to come out of Europe in recent years.

Because I'm obsessed with cutting-edge AI tools, I'll be sharing more about their platform features & capabilities - with a focus on agentic use cases.

I've already started building workflows and free tools for your pleasure, which I'll share soon (make sure to sign up for the newsletter so you don't miss future updates).

Excited to be one of their new partners with a focus on agentic for enterprise.

#ElevenlabsPartner

Looks like AEO ("Answer Engine Optimization") as a term might be winning the race.

I put SEO in for contrast.

You *almost* always want to say 'research this' for any response from a large language model.

The models are trained on data that's 6-12 months old.

Example:
Ask chatGPT: "how to intergrate phantom buster in clay"

Response: "Clay doesn’t have a native PhantomBuster integration".

Ask it to run research, the response then is:
"What the native integration supports:"

It isn't stupid, it's just operating from a limited or outdated knowledge base.

Why almost?
Because if you're looking up the capital of France that isn't something that's changing often. But for almost anything else GTM related you want to make sure you've got fresh context.

From what I've seen more problems happen due to not inputting the right quality information into AI-systems (user error) than AI systems going haywire (system error).

Common mistakes:
- Context overload
- Irrelevant context
- Not breaking tasks into granular steps
- Using the wrong systems
- Expecting the AI to do the thinking for you

I'm running an analysis on the entire AI landscape - 28,945 websites.

I'll post tidbits as I uncover them, but here's one that just struck me and I wanted to share with you.

Big AI brands get all the media attention. But we often forget there's an entire ecosystem of companies built on top of the giant foundational models.

40% of all AI companies (being AI powered, or AI-native etc') have fewer than 10 employees.

There's a massive long tail of solopreneurs and small businesses.

Some of the biggest power users of chatGPT API are companies with fewer than 15 people.

Instagram had just 13 employees when Facebook acquired it for $1 billion in 2012.

This breaks the traditional approach to B2B where employee count was historically a critical variable when defining target prospects.

Bigger doesn't always mean better.

For all the hype with go to market engineering and in particular signals there are two specific trusted & reliable events that consistently perform across clients.

The first is tracking champions/users who move to a new business.

Companies spend an inordinate fortune on building brand, educating the market and creating relationships with new people & accounts.

Aka cold prospecting. Which is becoming less efficient across all channels across all industries in B2B - not just SaaS.
The increase in automation is met by the rise in privacy constraints. From cold email filters, LinkedIn cracking down to Apple caller id - uninvited guests are facing increased barriers.

Past customers - the ones who’ve experienced direct benefit are of disproportionate value as a result.

When a person who’s used your product migrates to a new company that moment opens up a hot opportunity in an entirely new account.

Making it a foundational signal.

The second signal is when someone starts a new role. When your target ideal buyer persona starts a new challenge they are in deep evaluation mode and more open to new solutions.

Across my client base these 2 carry the most consistent predictive power for generating growth than any other signal.

As the years progress I end up viewing most things in GTM through a lens or jobs-to-be-done. Timing is a critical dimension to nailing traction.

Shift to focus more resource on tapping into the moment where there’s increased probability a potential buyer is open to your solution - you’ll get higher ROI.

One of the more prolific use cases for Gen AI I'm seeing lately is personalised web experiences - landing pages, demos, emails.

This tech existed previously but was quite pricey and required manual inputs. Now it's proliferating and will soon become a commodity (given the number of players I know of doing this).

It's still a critical piece of kit for marketing/sales to increase conversion rates by offering a personalised journey - the tech will only improve in time.

There's no such thing as 'too personalised'. As noise levels increase, it becomes critical.

The most important thing to know is that all personalisation efforts depend on the quality & depth of the data you have about said person/account in your CRM/CDP.

Which means the infrastructure layer for gathering real-time data about said person/account is becoming critically important. Fetching info from website, but also job ads, social posts, reviews on third-party sites, etc, for each contact record.

Having clean data that can be used was a differentiator before the advent of Gen AI - but that advantage is having a multiplier effect now as a result of how it can be leveraged.

Invest in clean data. Invest in enriching your data.
Only then invest in leveraging your data.

You don't have a GTM challenge.

You likely have a GTM prioritisation challenge.

You're not limited by resources. 
You lack the clarity re where to allocate your resources more effectively.

Which motion to commit to - and go all in.
Which channels to double down on.
Which ICP to define as tier 1.

When you spread your GTM team too thinly, you end up with ambiguity over how to build a scalable & repeatable growth engine.

Here’s a good alternative:

• Get the leanest tech stack, not the fanciest
• Take your time with org design, can't afford to mess it up
• Use data to define your tier 1 ICP only (see my post pinned on profile if you missed it)

Focus is how you win.

I've recently shifted to recommending clients use Clay's new reverse IP (what they call: deanonymization) upgrade.

The reason is due to the waterfall cascade.
Instead of 1 vendor, Clay aggregates them to give you more coverage.

Some businesses I support with sales teams of 20-70 heads rely on this signal as part of their GTM strategy.

(Thanks to Yaniv Mazor for the tip).

Interesting new research that breaks up a task into over a million sequential steps, each handled by a focused agent call.

Three core components:
Break tasks into the smallest possible steps – each agent focuses on a single action
Get multiple agents to vote – different agents independently solve the same step. An answer is only accepted once it leads by enough votes.
Throw away dodgy outputs – if a response is too long or badly formatted, discard it and try again
The system solved a puzzle requiring over one million steps with zero errors.

The individual error rate wasn't eliminated; it sat around 0.2%. But the voting mechanism caught mistakes before they could cascade.

One counterintuitive finding: don't try to fix malformed outputs. When an agent produces a messy answer, it's more likely to have reasoned poorly. Bin it and resample.

Another surprise: smaller, cheaper models won even though they have higher error rates on average. This works because the task is broken into tiny steps. 
Then each step gets multiple independent attempts. 
An answer is only accepted once it leads by enough votes so errors get outvoted.

Before running a million-step task, you can estimate which model will be cheapest by testing error rates on a small sample. They ran 10,000 random steps first, measured error rates, then picked the most cost-effective model.

Another interesting point: smaller models focused on tiny tasks are easier to audit, control, and sandbox than one powerful model doing everything. They argue this approach reduces the risk of uncontrollable AI behaviour.

Longer responses = more errors: once responses crossed about 700 tokens, error rates jumped from ~0.1% to ~10%. When an agent overthinks a simple step, it's probably confused.

Takeaway: chunk your tasks into smaller steps + add a validation layer. Bigger models doesn’t mean better if your prompt is poorly defined or your context layer is thin.

New open source Kimi2 outperforms ChatGPT 5.
Or does it?

GPT-5 beats K2 on multimodal. GPT-5 is also a better writer (which isn't saying much as Claude does a better job there than either model).

K2 Thinking, however, is a better planner. Can execute 200-300 sequential tool calls autonomously.

You can inspect reasoning traces & fine-tune for your domain so for regulated industries, K2 is a better option.

K2 Thinking trained for mere $4.6M.

Open-source models commoditise margins so the only way to justify the huge cost in infrastructure is if investing in larger models means it delivers better outcomes.

Research suggests that smaller models tend to outperform larger models. So it's possible that the giant infrastructure projects from OpenAI will be one of the largest financial black holes in history.

GPT-5 must remain proprietary because the math only works if developers stay locked in.​

K2 costs 1/10th what GPT-5 does per token.

Eventually, the question isn't 'which is better' but it's 'can you justify the 10x markup?'

That's historically the moment proprietary dominance starts to crack. e.g. last year ANZ Bank switched from OpenAI to fine-tuned open source Llama because of quality-cost consideration.

Don't ask 'which model is the best?'
Ask:
- Which model is the best for this project
- What's the business case

Here's I think about prioritising smart signals for GTM motions. And why most companies are still working with an outdated playbook when assembling their target account list.

Many folks are still using generic firmographics to build their target list. Using industry + employee count. 
But they're missing out on establishing relevant context.

I've been working with the Cargo 🧱 team to set this up for a client recently.

Layer 1:
Identify new hire in closed lost accounts. 

When a new hire begins in closed lost accounts, you get notified and can reach out. You do this for the ideal buyer persona who meets your criteria.

Can also do this for new target accounts you're focusing on - when a new hire starts in that role you add them to your ABM-mix.

Why does it work?
Typically a higher propensity to purchase in first 90 days of starting a new role.

Layer 2:
Identifying champions moving to new company. 
If a previous user has had positive experience and then leaves to new company they have context of your solution. 

I'm seeing this one work across multiple clients at the moment.

Layer 3:
Finding target accounts using customer Lookalikes.

There's a few ways to build out lookalikes. I prefer to build a custom formula using multiple factors (https://lnkd.in/eUBWfxRv).

Sometimes that isn't viable if public characteristics are weak indicators, statistically speaking.

Layer 4:
New users' past employers
Someone who's worked in a target account previously, but is now a user who can give you an intro into their old company.

Layer 5:
Reach ex-employees of your new customer who've moved to prospect companies.

Every time you win a new logo, automatically surface all qualified personas who used to work at that company and are now somewhere else.

Essentially running an alumni playbook in reverse.

--
People don't believe what you say, but what other people say about you. These tactics milk that to the max.

Thanks Maxence de Villepion  🧱 for input on this post.

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