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, SaaS & Fintech B2B in the 10-100mil ARR range.

I've coached or built growth engines for global companies such as BT, Lenovo, Canon, Investec Bank and scale-ups, e.g. Spendesk, HandsHQ, Peakon, SeedLegals.

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.

My toolkit includes Python, Google Colab & Cursor. 
AI GTM tools e.g. Clay.com, Cargo, OctaveHQ. 
For agentic workflows I'm either using N8n or a custom framework like Crew AI or Langchain.

I leverage data science & statistics to shift from anecdotal debates to scientific conclusions.
Companies using AI-native GTM systems achieve:
* 5-25% revenue increases
* 30-70% higher conversion rates
* 5-15 hours/week recovered

I help you leverage the intersection of GTM with data science to drive growth, fusing strategy & tactical execution.

Here's what people say about me (UK invite-only CMO community):
- “Fountain of knowledge”
-  “Leads the pack in all things B2B SaaS, GTM, AI, ICP — the lot”
-  “Generous with his knowledge and time”
-  “Half the time I don’t understand a word he’s saying, the other half is genius”

I'm often either speaking, running trainings, workshops or shipping GTM engines.

Brands I collaborate with include: Elevenlabs, Semrush, Hubspot, Miro

Background:
* 20+ years building revenue systems for B2B SaaS, a digital marketing native
* Managed millions in marketing spend
* Scaled bootstrapped agency to 7-figures

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.

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

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

If you've not heard of Google's new Antigravity tool, and you can't keep up on top of all the recent updates - I got you.

Here's my quick overview comparison of Antigravity to Cursor. Engineering AI tools (called IDEs).

I personally use Cursor for hours and hours a day (and weekends) for client work & projects, often with Claude code. This is part of my toolkit, amidst using tools like Cargo, Clay, Mixpanel & N8n (which is like Make or Zapier but more AI-centric).

I use these tools to:

- Run Python scripts for data analysis & running statistical models

- Integrate into CRMs, marketing automation tools etc' to clean data, port it, enrich it and build growth engines by connecting them together. 

- Support sales & customer success teams with lead generation, inbound lead enrichment & qualification and running agents to automate manual tasks.

- Help marketers with demand gen & performance marketing primarily.

Mostly all result in supporting revenue leaders in reducing the cost of revenue growth.

This is a cursory view of capabilities, so this isn't meant for data scientists or engineers but folks in go-to-market who keep hearing: 
- 'AI is disrupting everything'
-  'Enterprises are cutting the workforce'
But you can't quite reconcile why that's happening when you're using ChatGPT or Gemini in your day-to-day.

In the video I cover the similarities and the differences.

The most notable difference I spotted for those of you who don't have 2.5 minutes on 2X speed:
- Cursor has far more Gen AI large language models available

For now, Cursor has the lead, but given Google's fierce focus on catching up to players across multiple domains (ChatGPT on multi-modal, image & video generation) and now AI coding assistants as well as having the most advanced large language model to date, it's a space worth keeping a close eye on.

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.

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.

OpenAI's new 2025 Enterprise AI report (adapted more visual version) attached.

One number jumped out at me: 320x

That's the increase in reasoning token consumption per organization over the past 12 months.

Not 320%. 320x.
That's 32,000%.

There's a clear gap emerging between frontier firms (those on top of it) and everyone else.

Frontier workers use AI 6x more than the median worker. Their firms send 2x more messages per seat.

Gap is widest with: 17x in coding, 11x in writing, 10x in analysis.

BCG found AI leaders are seeing 1.7x revenue growth and 3.6x greater shareholder returns than the rest.

Meanwhile, 25% of enterprises still haven't connected their internal data to AI tools.

Nearly 1 in 5 active users have never touched data analysis features. This is surprising to me - even basic capabilities such as thinking mode.

Essentially folks aren't using the areas the AI is strongest in.

The report's conclusion rings true to me: "The constraint is no longer technology, but organisational readiness."

Translation: The tools exist. The ROI is proven. 

The bottleneck is whether your team knows how to actually use them, and do so well.

Report adapted

"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.

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 ran a test using 5 large language models for a go-to-market task. An analysis of 1.6million marketer profiles. 

Context: I'm classifying job titles to build a trend tool.

We often hear about Gen AI benchmarks on maths & coding tasks but what about marketing, sales and customer-success challenges? 

So I've decided to test more specialised GTM use-cases and share some of my projects openly.

Why do this?
I want to see:
- What job titles are growing/shrinking in popularity
- Which skills are growing/shrinking in popularity
But not based on conjecture or opinion, rather on the actual shifts in the landscape.

This matters to:
- Job seekers looking to invest in developing popular skills
- Hiring managers looking to understand what the next big thing is
- As AI disrupts marketing roles, some specialists will need to pivot, and they'd like to catch the next wave that's adjacent to their current capabilities/interests
- Some trend hoppers want to grab an opportunity before it goes mainstream

And also big picture. The marketing landscape is large and fragmented.

Many folks look to leaders for answers they don't have. I see many mistakes on job descriptions, wrong interview questions, mixing up the wrong responsibilities for the wrong job title.

Even with access to ChatGPT or Gemini (those models make mistakes. If you don't have specialist knowledge, you wouldn't be able to tell).

My challenge:
A lot of text. Roughly 326 million words.
Which means expensive to process.

So I wanted to see if the open models (which cost a fraction) can achieve comparable quality.

Now, to the fun stuff - data!

The models I tested:
- DeepSeek V3.1
- GPT-OSS (120B)
- Qwen3
- Mistral Large 3
- ChatGPT 5.1

Explanation of the image in the table attached:
1. Agreement: How often the AI got the right answer.

2. F1 Score: A single number that shows how good the AI is overall at finding correct answers without making mistakes.

3. Precision: When the AI says "yes," how often is it actually right?

4: Recall: Out of all the correct answers that exist, how many did the AI find?

Conclusions:
1. I was delighted to see open source model achieving such a high-quality output

2. Using the right open source model can be far more efficient than the big known models (chatGPT, Gemini etc')

3. I was disappointed to see an open-source Chinese model (Deepseek) beat a European one (Mistral).

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.

Your company has both a GTM and a prioritisation problem.

Most companies think it's either/or.

It's not.

When I review a company's go-to-market engine, I'm not looking for one broken part. I'm looking at the whole system.

Think of GTM like an engine with lots of different parts.
The question isn't "is it broken?" 

The question is: which parts are broken, which are weak, and which need to be replaced entirely?

Common pattern:

Poor clarity over ideal customer profile
↓
Misaligned budget allocation in GTM activities (marketing VS sales)
↓
Wrong target accounts getting resources
↓
Sales & marketing strategies pulling in different directions

It's not just a GTM problem.
It's not just a prioritisation problem.

It's both - simultaneously.

Treating it as one or the other is why the engine keeps sputtering.

During scope discussions, this becomes evident fast.
Ask a CRO/CMO who they're targeting, how they're allocating budgets across channels & accounts, what variables they are optimising for using statistics rather than conjecture?

The answers (or lack thereof) tell you a lot about the maturity stage.

This becomes really evident when there's a change of hands at the leadership level. When lots of legacy issues have accrued.

You can't fix prioritisation without understanding your GTM engine.

You can't optimise your GTM without clear prioritisation.

They're connected parts of the same system.

What I want to leave you thinking about: which part of your GTM engine are you ignoring?

It almost always happens - getting 2 (or more) conflicting views about GTM.

How is that possible? How can you get different experts saying completely different things?

Surely Meta / G / LinkedIn ads & AEO / SEO etc, are all constrained systems with a defined set of variables. Meaning there is variability, but it's fairly predictable.

Especially for an expert who must have seen this pattern over and over again. 

Isn't that the definition of expertise?
Pattern recognition.

This contradiction often occurs (from what I've seen) because those experts are guilty of a cardinal sin.

I know what you're thinking.
Ego, a self-indulgence of sorts.

And yeah, that certainly is common, especially amongst the more technical sort.

But that's not it.

I'm talking about: projecting a past experience onto a current situation. Without reference to:
- That company's brand reputation / notoriety
- That company's audience (ideal company / buyer profile)
- That company's messaging
- That company's prospect journey (trying to avoid saying the F word here - funnel) etc.

Once you start introducing 1 or more variables, the past projected experience has a large error margin.

If you have an expert who says, 'This is the way' - be wary. 
It's often best to go with the person who says: 'let's test it'.

And yeah, sometimes that's a cop out for 'I don't know'.

The holy grail for executives is certainty.
They crave it.
So they chase it.
And they end up putting their chips on stuff that's bunk.

A pretty package devoid of substance e.g.
- Benchmarks from companies nothing like yours
- Attribution models that need volume you don't have
- Intent data for topics too niche to track

It's a Venus flytrap.
Gotta relate to the executive here to put this in perspective. When you've got a ton of direct reports, and you're back-to-back in meetings with hard numbers you need to hit, that's a big challenge.

Finding the right people to rely on is key.

Trust the person who's confident because they have current data - not the one speculating from past data. And if there's no current data, put your faith in the person who's working hard to get it.

p.s.
Playing around with adding visuals to 'dense' topics. Tacky or does it work?

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.

Interesting research from Claude (Anthropic). Interviewing 1,250 professionals using AI.

The 3 things I found most curious:

1. The funniest to me was the level of frustration data scientists have. 

When you look at companies that build tools like Cursor, it's engineers building for engineers. While AI is primarily comprised of data scientists you'd think they scratch their own itch first.

2. Designers are frustrated. This isn't surprising. The tools aren't quite  there yet to help make their jobs more efficient as with other professions.
Across all disciplines, trust remained consistently low, suggesting shared uncertainty about AI’s long-term implications for creative work.

3. I was surprised there was a pessimistic view of prompt engineering. I can't quite explain the rationale for that, especially with the advancements in context engineering (the new prompt engineering).

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