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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Great fun speaking yesterday at Semrush spotlight. If you want to see what a high standard for a great event looks like make sure to check it out next year.

Lovely to connect to wonderful familiar & new faces. Wonderful speakers & subject matter experts:
Elena Verna
Kevin Indig
Aleyda Solís
AJ Eckstein 🧩
Katie Street
Leigh McKenzie
Max Woelfle
Sara Stella Lattanzio
Jo Bird ✨
Marcel Melzig
Diandra Escobar
Hillary Bush
Gus Pelogia
Shlomo Genchin
Azeem Ahmad
Alex Moss
Beatrice Vladut
Christian Desert
Eli Schwartz
🦖 Hector Forwood 🦕
Yannick Khayati

And hats off to Madhav Bhandari for doing two speaking events with me, two days in a row. Not easy as full on!

Wish I had more time to chat to more amazing people.

Many thanks to Margo Laz & Marina Nikolaeva for inviting me and making this happen.

Didn't expect another invite in Antwerp next year for another speaking gig on AI marketing during this event - but there you go. Life is nothing but a box of probabilistic surprises.

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

Boom, another speaking event. Be lovely to see you.

Speaking about go-to-market engineering. If you're scratching your head, it's a new hot buzz term that best captures the work I'm doing with clients.

Data science + gen AI + engineering principles applied to go-to-market.

Includes automation and data stitching different platforms together (often with duct tape).

Examples:
- Rigorous ICP analysis using statistical models (see my post on my profile for my free process on this)

- Data-driven customer targeting. This isn't just setting up a conversion event in G ads or Meta. This is enrichment and curation for prioritised prospecting since TAMs are usually large and resources are constrained.

- AI-powered marketing systems with a centralised context layer for consistent, scalable execution. If you aren't sure what this is you can chatGPT "context engineering". Or read Anthropic's blog post on this.

Now there's some confusion with methodologies/approaches such as growth hacking or growth marketing. These were both the original inceptions of rapid experimentation. Same with growth loops. GTM engineering builds on these principles but it isn't often as focused on paid ads. GTM engineering can apply to product-led, sales-led or product-led-sales motions. 

What GTM Engineering isn't:
1. "Just marketing automation"
Automation executes predefined workflows deterministically
GTM Engineering uses probabilistic AI systems that adapt and learn from context

2. "It's replacing marketers with AI"
GTM Engineering augments and necessitates strategic thinking; it doesn't replace it. It's sometimes used synonymously with sending mass spam via cold email.
That's the lower end of the market - real GTM engineers are working in the fastest growing companies in the world.

3. "It's only for enterprise companies"
The methodology works for startups to enterprise. I personally focus more on mid-market & scale-ups as there are fewer archaic systems and red tape.

4. "It's just better prompt engineering"
A. Prompt engineering has evolved into 'context engineering' (this happened a month ago so fresh).
B. GTM Engineering builds persistent infrastructure (context hubs, data pipelines, feedback loops).

5. "It means hiring data scientists"
You need analytical thinking, not PhDs.
Modern tools (Claude, Python, Cursor etc') make statistical analysis accessible.

It's about applying the scientific method to GTM, not complex mathematics.

p.s.
Tom Hunt, another speaking gig we're on together...you'll be sick of me at this rate

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.

Are you a B2B marketer looking to automate some of your work via AI?

Amir Ashkenazi from Airtop and myself are running a series of free workshops as part of a joint research project.

- Small groups with a handful of B2B marketers
- 3 meetings together

The goal: help you to scale your effectiveness with AI web automations you need.

Leveraging AI agents to auto fetch info from publicly available sources e.g.
- Account based research
- Competitor analysis
- List building for lead gen

Marketers I’ve spoken to don’t want to mess around with complex workflows, so using natural language (the way you use ChatGPT) is a lot easier which I like about Airtop.

It’ll be hands-on and focused entirely on your challenges.

Those selected will also get a month of Airtop Pro so you can build the automations you need.

If you’re interested drop a comment to join waitlist (send connection request if we aren’t linked up).

Know how you feel overwhelmed and can't stay on top of AI?

Well, you're in luck.

Because you're not one of the 4.4 billion people who don't have access to basic digital skills, let alone AI (that's half the human population).

There's big problems out there. Let's fix them.

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.

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.

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

I put SEO in for contrast.

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.

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

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