How the audit works
How the AI GTM Audit works
Most B2B companies don't have an AI adoption problem. They have a data and workflow problem dressed up as one. The AI GTM Audit is a two-week, async diagnosis of your revenue engine that finds which workflows are worth handing to agents and which will fail because the data underneath is broken. Every finding is priced in pounds of pipeline exposure. You own everything.
The spine of every audit
- 01
CRM hygiene first — trust the data.
Agents built on fragmented data don't fail loudly; they answer confidently and wrongly. So the first port of call is CRM hygiene: close-reason coverage, lifecycle integrity, duplicate rate, activity recency, attribution completeness, stakeholder coverage on deals, and a reconciliation of CRM closed-won against the accounting ledger.
- 02
Close-rate decomposition — the telltale metric.
Close rate is the telltale sign of a healthy sales process; everything cascades from it. It's decomposed by stage, segment, rep and source, and turned into a required-vs-actual pipeline coverage figure.
- 03
Four pillars.
Data, Workflows, Signal Capture, Metrics — each scored, each finding priced.
Principle throughout: deterministic maths, narrative LLM. The code computes every metric; the model only interprets. That's what makes the findings hold up under scrutiny.
The four pillars
Data
Can your agents actually answer the questions that matter?
Checks: close-reason coverage, lifecycle stage integrity, duplicate rate, attribution completeness, activity recency, CRM-to-ledger variance, and a retrieval test on five questions that matter.
Findings priced as £ pipeline running on data an agent would misread, £ double-counted through duplicates, and £ of closed-lost you can't diagnose.
Workflows
How much of your team's week is robot work?
Checks: hours per workflow across enrichment, follow-ups, CRM admin and reporting, costed at loaded salary; manual handoffs and the errors they introduce; which sequences are rules-based enough to automate now.
Findings priced as hours × loaded cost × team size, annualised — and the selling capacity that time represents.
Signal Capture
Where does your proprietary data go after the call ends?
Checks: % of calls recorded and mined; whether win/loss is structured or anecdotal; buying-committee capture on deal records; whether objections and intent signals reach targeting and follow-up.
Findings priced as £ of closed-lost with no structured reason, £ exposed to single-threading, and the win-rate uplift a structured win/loss loop is worth.
Metrics
Are you measuring AI adoption or AI outcomes?
Checks: what AI is measured on today; whether cost per SQL and payback are calculable at all; whether baselines exist to tie an agent's action to a revenue outcome.
Findings priced as the AI spend that currently proves nothing.
Scored per pillar, not averaged away
Each pillar is scored 0–10 separately. There's no single overarching readiness score — weaknesses are visible per pillar so the priority area is obvious. Every pillar score connects to £ pipeline exposure.
See it worked in full.
Scroll the sample below — a complete audit, every finding priced, the close rate decomposed, four pillars scored, and three agent builds specced with a £ exposure and build effort each. The first pages are open. To get the full go-to-market audit PDF, submit your email.
Sample subject: Northbeam Software — an illustrative mid-market B2B SaaS. 9 pages. Pages 1–3 open; pages 4–9 unlock with your email.
No-value, no-pay guarantee: If the audit doesn't surface at least three quantified, buildable agent opportunities — each with a £ pipeline exposure and a full build spec — you don't pay.