10x
account coverage per marketer
Case study · GTM x AI · GTM OS
The GTM function, rewritten from zero.
Backbase is a €2.5B digital banking platform that more than 120 banks run on. Over the past year I architected and led its GTM x AI transformation, alongside the team. Not ChatGPT bolted onto the old workflow: the go-to-market function rebuilt from the ground up into a GTM OS the company owns and runs. What it produces in half a year is below. The rest of this page is the how.
GTM OS · shared brain, distributed interfaces
The outcomes
What we achieve in half a year.
The result the operating model delivers, not the activity. Backbase's numbers are public. The B2C figures come from a comparable anonymized engagement.
+85%
new qualified pipeline, year on year
+60%
average deal size, year on year
$100K+
annual software licensing retired
4.2x
organic traffic, 12,000+ owned pages
-41%
cost per acquisition on paid
-35%
blended CAC, the cost curve bent
~80%
of each new build reused
Backbase, public, shared by CMO Tim Rutten. The B2C figures are from an anonymized engagement, outcomes across comparable clients. Read his account →
Where it started
A growth machine that had run out of road.
- Pipeline coverage was thin and growth leaned on a reactive motion.
- Hundreds of accounts managed by hand, thousands of others never touched.
- Marketing and sales ran on separate scorecards, with no shared signal.
- AI showed up in pockets: a prompt here, an automation there, nothing that compounded.
- A CMO who had already decided that incremental was not going to cut it.
The instinct was right. You don't reach an order of magnitude more accounts by asking the same team to work harder or by sprinkling AI on a process built for a smaller, slower world. The function itself had to change.
The decision that made it work
All the way, or not at all.
Every company hits the same fork the moment AI gets serious. Backbase took the harder side and it is the reason the numbers moved.
The easy side
Bolt AI on.
Keep the org chart, the tools and the workflow. Add ChatGPT and a few automations on top. It feels safe, it ships fast and it changes nothing structural.
- The legacy workflow stays in charge
- AI is a feature, not the foundation
- Adoption stalls, the old process reasserts itself
- Gains are marginal and easy to reverse
The side that compounds
Rewrite from zero.
Design the GTM function as if you were building it today, with no legacy to defend. Then build that design into the real operating environment. Roles, architecture, operations, all of it.
- A blank-page design, AI-native by default
- The operating model changes, not just the tooling
- The team rebuilds around the new motion
- Gains that compound instead of fade
The moment you start from zero, you realize how much of the current marketing operating model exists for reasons that no longer hold.Tim Rutten · CMO, Backbase
What we built
One GTM OS. A shared brain, distributed interfaces.
Not a tool. An operating system for go-to-market: one intelligence layer underneath and the interfaces each team actually works in on top. Every new capability reuses what is already there, so the next one ships cheaper and faster than the last.
The engine in motion: noise pulled in, the shared brain running, playbooks shipping, revenue up and cost down. Illustrative.
The layer underneath
The shared brain
One intelligence layer holds the accounts, the signals, the definitions and the institutional knowledge. Every interface above it reads from the same source, so the whole function sees one version of the truth.
Interface 01
Signal Engine
Who is worth a move this week
Interface 02
ABM Engine
Research and plays on demand
Interface 03
Mission Control
Next best action, pushed to the owner
Signal Engine
Watches every account across first-party and third-party signals, scores intent on a rolling model, then surfaces who is worth a move this week. The team stops guessing where to spend attention.
Live
ABM Engine
Turns a target account into research, a point of view and ready-to-run plays, on demand. What used to take a marketer days now takes minutes. That is most of where the 10x coverage comes from.
Live
Mission Control
Account-level intelligence and next best actions pushed to whoever owns the deal, plus the pipeline view leadership runs on. The system comes to you, you do not go digging for it.
Hardening
The pattern underneath
Push, don't pull. Reuse, don't rebuild.
Two principles run through the whole system. The work comes to the person who owns it and every new capability stands on the same foundation as the last.
01
Signal detected
→
02
Context assembled
→
03
AI reads the account
→
04
Rules decide the move
→
05
Draft ready
→
06
Human sends
Why it keeps getting sharper. Every outcome feeds back into the score, so the system learns which moves work. And because each capability sits on the same shared brain, each new one reuses roughly 80% of what is already built. The first capability is expensive. The tenth is nearly free. That is the difference between a pile of tools and an operating system.
The economics
Built, not rented. Owned, not leased.
A GTM OS absorbs work the company used to license one tool at a time. Backbase has retired six figures of annual software spend and it owns the thing that replaced it.
$100K+
Licensing retired
The GTM OS took over work the team used to pay an ABM platform, reporting tools and integration middleware to do. That software line goes away.
↓
Marginal cost falls
Every capability sits on the same shared brain, so each new one costs a fraction of the first. Buying tools works the other way: every new use case is another contract.
100%
You own it
It runs in the company's own environment, on the company's data, under the company's control. No vendor owns the workflow your revenue depends on.
Don't take my word for it
Backbase's CMO wrote the story himself.
Tim Rutten published a full public account of the transformation: the blank-page rewrite, the operating model, the numbers. It is the clearest outside proof of what this work does.
We didn't get here by bolting ChatGPT onto the workflow. We rewrote the GTM function, including architecture, operations, structure and roles, from scratch.Tim Rutten · CMO, Backbase
Alignment moves from orchestrated to architectural.Tim Rutten · CMO, Backbase
Bring it to your company
This is GTM x AI. Here's how I build it.
Backbase is not a one-off. It is the pattern I run. I have built GTM x AI engines across B2B and B2C and the steps are always the same: diagnose, prove one capability, then compound.
01
A sponsor at the top
Someone senior who wants this and gives air cover when something breaks. At Backbase that was the CMO. Without it the work stalls in committee.
02
A GTM strategist who knows the business
The person who designs the operating model and drives the build with your team. This is where I sit.
03
An AI-native engineer
One builder who turns the design into working software. I bring this capacity if you don't have it in-house.
Weeks 1 to 4
Diagnose. I read your data, your stack and your team, then pick the one capability that proves the model fastest.
Weeks 5 to 12
First capability live. Built on the edge, in real hands, with real numbers moving before anything rolls out wide.
Months 3 to 6
Compound and harden. More capabilities ship on the same brain and the proven ones move into your own stack.
Month 6+
You own the OS. I step back to fractional steering. The team runs the system and keeps building on it.
GTM x AI built with
Google · Backbase · SkyShowtime · Raiffeisen Bank · GoodHabitz · Marktlink · Tribes Media · Everconvert · Growth Tribe · and more
Build your GTM OS
Want this inside your company?
- A 30-minute call to see if the pattern fits your go-to-market.
- I map your first capability, the one that proves the model in 90 days.
- A written plan within a week, scoped to your stack and your team.