Does this replace our sales people?

No – and the data says that would be the mistake. 69% of B2B buyers look for a human to check what the AI said. AI takes the preparation, the human takes the trust.

We already have tools. What does this add?

Then the Reality Map is the interesting part. Tools are rarely the bottleneck; CRM data quality often is – and no agent pulls a good result out of a broken CRM.

How does this relate to the AI GTM Lab on gtm.science?

Same framework, same four phases, same twelve projects. The lab on gtm.science is the open cohort; the AI GTM Lab is the in-house version on your own funnel and CRM. Price, cohort logic and module sequence stay in sync.

Is voice AI even allowed in the DACH market?

Partly. We cover the line concretely – UWG, EU AI Act, consent – and also show the cases where the answer is «don't». This is not legal advice.

Do we end up with working agents?

In the Decision Lab you get the blueprint and the autonomy rules. We only commit to working agents in the Execution Track once data and process readiness are confirmed – anything else would be a promise on spec.

What does the lab cost?

The scope is fully described on this page. We fix the price in a 30-minute scoping call and confirm it in writing.

AI GTM Lab · CRO · Sales · Marketing · Revenue

Don't automate bad sales. Redesign it.

In two days to a named GTM bottleneck, a human-AI autonomy matrix and a system plan built around real customers, your CRM and the buying signals that matter.

Test agent autonomy

Thirty minutes with an operator. Then you get scope and a fixed price.

For

CRO and chief commercial officer
VP Sales and Head of Sales
GTM and revenue leaders
Founders carrying revenue
Marketing and sales leads in B2B software companies

Direct gtm.science compatibility: software companies with product-market fit, roughly 20–500 employees, DACH and EU.

The starting point

Buyers want self-service and human validation at the critical decision points. More outreach automation doesn't resolve that tension. The actual job is to design autonomy deliberately.

Evidenced by

69% of B2B buyers go to sales reps to validate AI-generated insights. At the same time, research in sales increasingly starts with AI. Both hold at once – and that's where the work sits.

Dream outcome

The revenue team knows its bottleneck, works from a shared Context Workspace and knows, for every relevant task, what an agent does alone, what a human checks and what stays human.

Your first result

55 working days after a complete intake

GTM heatmap with a bottleneck hypothesis

Within five working days of a complete intake, the team receives a GTM heatmap with the most likely bottleneck and the three most important questions for the system lab.

What the Decision Lab includes

Every component names the obstacle it removes, the artefact you receive and when it is delivered.

Lead time: about 10 working daysLab: 2 intensive daysParticipants: ideally 5–10Roles: revenue leadership, sales, marketing and RevOps

We work on the CRM and pipeline extract and on your messaging and content artefacts.

Obstacle
Teams optimise symptoms rather than the bottleneck
Artefact
Heatmap from customer, CRM, positioning, process and stack signals
Delivered
before the lab

Included accelerators

Agent registry template
Signal dictionary template
Human approval review checklist
Growth loop review agenda

Templates created during the lab that your team keeps using afterwards. We don't attach invented individual values to them.

When the evidence holds: Execution Track

100-Day AI GTM Operating System

The Decision Lab plus 100 days of execution along the twelve gtm.science projects.

Execution along the twelve gtm.science projects
Signal-based workflows
An orchestrated GTM stack
Agreed working agents, where data and process readiness are confirmed
Agent org chart and autonomy rules
Growth loops and review cadence
Updated GTM Context Workspace

Price, cohort logic and module sequence stay in sync with the canonical gtm.science source. We only commit to working agents once data and process readiness are confirmed.

Proof

Problem proof

6 in 10
also reach for personal AI tools – even though 87% are given tools by their employer.
Source: AI Monitor 2026 – Teklens mit ETH Zürich und Universität St. Gallen (2026)
Basis: 162 valid responses, self-reported tool usage
Limitation: Self-reported. It shows personal tools are in use, not what data flows through them.
69%
of B2B buyers go to sales reps to validate AI-generated insights.
Source: Gartner, B2B Buyer Survey (2026)
Basis: Buyer survey
Limitation: Buyers self-reporting their own behaviour.
75%Projection
of B2B buyers will prefer sales experiences that put human interaction ahead of AI by 2030.
Source: Gartner (2025)
Basis: Analyst projection for 2030
Limitation: A projection, not a measured figure.

ETH Zurich and the University of St. Gallen are research partners on the AI Monitor. They are not Teklens reference customers.

Mechanism proof

Anonymised exampleHuman-AI Autonomy Matrix

Anonymised example · B2B software, around 60 employees

Task	Level	Review	Metric
Signal monitoring and list building	Autonomous	Weekly spot check	Hit rate
First-contact sequence	Human checks	Before sending	Reply rate
Proposal draft	Human checks	Before sending	Turnaround time
Price negotiation	Human only	—	Margin

Delivery proof

What we have actually delivered: cases, customer quotes or an anonymised before-and-after. Where nothing is cleared yet we show a clearly labelled anonymised example – not an empty placeholder.

We do · You provide

We do

GTM diagnostics and synthesis
Preparing the heatmap and bottleneck hypothesis
Facilitation and system design
Agent blueprint, autonomy matrix and roadmap
Context Workspace structure

You provide

CRM and pipeline extract
Relevant positioning, messaging and content artefacts
Two lab days with the core team
About three hours a week during the Execution Track
Named GTM and RevOps owners

Investment

Two options, one scope. The Execution Track sits first because it shows the full extent – the recommended entry is the Decision Lab.

Recommended entry

2-Day AI GTM System Lab

Diagnosis, a shared decision and a concrete execution plan. For teams that need clarity before they commit capital or capacity.

Role-specific diagnosis
First-value snapshot
One to two workshop days
Decision Pack
Context Workspace
90- or 100-day plan

First result: 5 working days after a complete intake
Full result: 5 working days after day 2
Effort on your side: 2 lab days, then about three hours a week

Fixed price after a 30-minute scoping call

The scope is set; the price is fixed in the scoping call and confirmed in writing. No line items appearing later.

Decision Pack guarantee

When the agreed inputs are complete, we deliver the defined Decision Pack by the agreed date. If an agreed component is missing, we keep working at no extra fee until the pack is complete.

The revenue team knows its bottleneck, works from a shared Context Workspace and knows, for every relevant task, what an agent does alone, what a human checks and what stays human.

Review your GTM system

Full system

100-Day AI GTM Operating System

The Decision Lab plus real execution, reviews and embedding into how the team works.

Everything in the Decision Lab
Execution on the real portfolio, backlog or GTM system
Agreed working assets or agents
Quality and governance gates
Review cadence
Handover and ongoing development in the Context Workspace

First result: 5 working days after a complete intake
Full result: 5 working days after day 2
Effort on your side: 2 lab days, then about three hours a week

Fixed price after a 30-minute scoping call

The scope is set; the price is fixed in the scoping call and confirmed in writing. No line items appearing later.

Decision Pack guarantee

When the agreed inputs are complete, we deliver the defined Decision Pack by the agreed date. If an agreed component is missing, we keep working at no extra fee until the pack is complete.

The revenue team knows its bottleneck, works from a shared Context Workspace and knows, for every relevant task, what an agent does alone, what a human checks and what stays human.

Review your GTM system

Decision Pack guarantee

A decision you can act on – or we keep working.

When the agreed inputs are complete, we deliver the defined Decision Pack by the agreed date. If an agreed component is missing, we keep working at no extra fee until the pack is complete.

We guarantee defined outputs and dates that we control. No revenue, transformation or autonomy guarantee – that depends on your execution, and we can't stand behind it.

A Decision Pack counts as complete with

a validated baseline
prioritised decisions
reasoned wait and stop decisions
named owners
success criteria or metrics
a roadmap and the next review point
an updated Context Workspace

Prerequisites

Complete inputs by the agreed date
The agreed stakeholders take part
Decisions stay within the agreed scope
Access and permissions are in place
No external blocker prevents the work

Fit gate

After Decode and Shape, you decide – not us.

The Execution Track does not start automatically. A reasoned wait or stop is a valid result – and often the most valuable one.

Amplify
The evidence holds. We start the Execution Track.
Experiment
A bounded test resolves the open uncertainty before capacity is committed.
Wait
Fix the groundwork or the input first. With a criterion and a review date, not as a postponement.
Stop
No sensible business case. Recording that with reasons saves more than any pilot.

Shared operating model

Decode → Shape → Amplify → Evolve.

A loop, not a transformation with an end date.

01
Decode
Understand reality, context and the bottleneck
Where do we actually stand?
02
Shape
Set decisions, priorities and limits
What do we change – and what don't we?
03
Amplify
Turn decisions into real work
How does measurable impact happen?
04
Evolve
Embed learning, governance and routine
How does the system get better?

The Decision Lab covers Decode and Shape and hands over to Amplify and Evolve. The Execution Track runs the whole loop.

Your company leaves with a system, not a slide deck.

Every lab leaves behind a versioned Context Workspace: evidence, decisions, work, rules and what you learned, in one place. Human-readable, machine-readable for approved agents, exportable.

Minimum requirements

Versioned, with traceable sources and assumptions
An owner per artefact; decisions dated and reasoned
Human-readable and machine-readable for approved agents
Exportable – no artificial lock-in
Permissions and data residency settled, update rhythm defined in Evolve

Structure

/gtm-os/
  00_context/
  01_goals/
  02_projects/
  03_skills/
  04_agents/

Common questions

Not sure which Lab fits?

Thirty minutes with the founding team. You get a clear recommendation, the right scope – or a reasoned no.

All labs

Don't automate bad sales. Redesign it.

The starting point

Dream outcome

Your first result

What may an agent decide on its own in your team?

What the Decision Lab includes

01GTM Reality Mapbefore the lab

02GTM System Labdays 1–2

03Positioning and Buying Process Decisionday 1

04First Agent Blueprintday 2

05Human-AI Autonomy Matrixday 2

06100-Day GTM Roadmapwithin five working days of the lab

07GTM Context Workspacewith the roadmap

Included accelerators

When the evidence holds: Execution Track

Proof

Problem proof

Mechanism proof

Delivery proof

We do · You provide

We do

You provide

Investment

2-Day AI GTM System Lab

100-Day AI GTM Operating System

A decision you can act on – or we keep working.

Fit gate

Decode → Shape → Amplify → Evolve.

Decode

Shape

Amplify

Evolve

Your company leaves with a system, not a slide deck.

Common questions

Does this replace our sales people?

We already have tools. What does this add?

How does this relate to the AI GTM Lab on gtm.science?

Is voice AI even allowed in the DACH market?

Do we end up with working agents?

What does the lab cost?

Not sure which Lab fits?