Go-to-market strategy consulting, AI-enabled
Rebuild the revenue engine from the ideal customer outward, segmentation, channel economics, sales motion, and an AI-enabled GTM stack underneath it that actually gets used.
Rebuild the revenue engine, not just the pitch.
You are probably here because of one of these.
None of these is the problem. Each is a symptom, and the diagnostic exists to find out which underlying constraint is producing it, because the obvious answer and the correct one are frequently different.
- Pipeline looks healthy and conversion does not. Coverage is reported; accuracy is not.
- There is no written ICP, or there are three of them and they contradict each other.
- Sales blames marketing for lead quality, marketing blames sales for follow-up, and both are partly right.
- CAC is rising and nobody can attribute the increase to a specific channel or segment.
- Every rep sells differently and the top performer’s method has never been documented.
- You bought AI tooling for the GTM team and adoption stalled at the pilot.
What you actually receive
Artefacts, not impressions. Everything below is yours to keep, rerun and hand to a board.
ICP and segmentation
Who you actually win with, by revenue potential, win rate, sales cycle and retention, derived from your data, not from a persona workshop.
Channel economics
CAC, payback and contribution by channel and segment. The channels that look cheap and are not, quantified.
Sales motion design
Stage definitions with exit criteria, qualification framework, the documented version of what your best rep does instinctively.
Funnel and forecast architecture
Definitions that mean the same thing across teams, and a forecast method with measurable accuracy.
AI-enabled GTM stack
Where agentic workflows genuinely reduce cost per opportunity (research, enrichment, outbound personalization, call synthesis, pipeline hygiene), and where they are theatre.
90-day GTM roadmap
Sequenced, owned, with the metric that proves each move worked.
The shape of the engagement
The 4D Method →Diagnose
CRM data extract, win/loss review, funnel decomposition, rep and customer interviews. Output: where the engine actually leaks.
Decide
ICP and segment prioritization, channel portfolio choice, motion options scored against your cost structure.
Design
Motion documented, stage gates defined, AI workflow pilots specified, roadmap built with owners.
Drive
Enablement session with the revenue team, cadence installed, forecast accuracy baseline set.
Fixed fee, agreed before work starts. Scope boundaries, assumptions, change control, IP ownership and the AI-use clause are written into every SOW. The fee is quoted after the complimentary audit, because the audit is what establishes which of these problems you actually have.
When this works, and when it does not
This engagement fits when
- You have at least a few quarters of CRM data, however messy
- Sales and marketing leadership will both sit in the room
- You are willing to deprioritize a segment or channel
- Revenue is between roughly $1M and $1B
Look elsewhere if
- You have not yet found product-market fit in any segment
- You want more leads without examining conversion
- The CRM has no usable history and nobody will reconstruct it
- The expected outcome is a script, not a system
The right-hand column is not modesty. A poorly matched engagement costs you a fee and costs this practice the only asset it has, which is a record of work that landed.
Go-to-Market Strategy, AI-Enabled
What does “AI-enabled GTM” actually mean here?
It means specific agentic workflows inserted at the points in your revenue process where they measurably lower cost per opportunity or raise conversion, account research and enrichment, outbound personalization at volume, call synthesis into CRM, pipeline hygiene enforcement, forecast anomaly detection. Each one is scoped with a baseline metric and a kill criterion. It does not mean buying a platform and hoping.
We already have a GTM strategy deck. Why would we need this?
Most GTM decks describe a target market and a value proposition. Far fewer define stage exit criteria, quantify channel contribution margin, or measure forecast accuracy against outcome. The deck is usually not wrong. It is not operational. This engagement produces the operating layer underneath it.
How do you handle it if the data is a mess?
The state of the data is itself a finding, and usually a load-bearing one. No documented ICP, no win/loss tracking and no forecast accuracy measurement are among the most diagnostic things observable in a revenue organisation. They get flagged as findings, not papered over, and reconstruction of the minimum viable dataset becomes part of the work.
Related
Thirty minutes on your version of this problem.
A working session, not a sales call. If the honest answer is that you do not need an advisor, that is what you will hear.