The Journal
Field note3 June 202614 min read

Anatomy of a defended number: one £2.4M assessment, line by line.

We took an anonymised engagement apart on the table — every pillar, every range, every source. This is what a figure that survives a board meeting is actually made of.

The output is not a deck. It is a number, with a stated range and a confidence the room can interrogate.

A board does not reject a governance programme because the work is thin. It rejects it because the headline figure cannot be traced. Somebody asks where the number came from, the room goes quiet, and a quarter of effort evaporates in a sentence. So when an assessment closed at £2.4M of quantified exposure and survived ninety minutes of questioning intact, we kept the working — and here we lay it out in full.

The firm is anonymised; the structure is not. What follows is the actual shape of the number: the four pillars it decomposes into, the ranges behind each, the method that produced them, and the half-dozen sources a sceptical CFO pulled on first. Nothing here is theatre. It is the arithmetic, exposed on purpose.

01Where the number comes from

Exposure is not a single quantity. It is the sum of four pillars, each modelled separately and each carrying its own confidence. Aggregating too early hides the line that matters; here, that line is revenue.1 Procurement friction and failed security questionnaires accounted for more lost pipeline than every other category combined.

Quantified exposure by pillar £m · annualised
RevenuePipeline & renewal
£0.91M
TechnologyModel & data
£0.58M
RiskRegulatory
£0.49M
OperationalProcess
£0.42M
Revenue carries 38% of total exposuren = 14 systems assessed
Each pillar is modelled independently before aggregation.Source: engagement working paper, §32

Read that chart the way the board did. The instinct is to attack the largest bar, so the largest bar has to be the best-sourced. If revenue cannot be defended, the whole figure is suspect; if it can, the rest follows. That is why the method front-loads the work where the money is.

A figure of "£2.4M" invites a fight about precision. A figure of "£2.1M to £2.7M, 80% confidence" invites a conversation about assumptions — and that is the conversation you want. From the engagement working paper

02The method, in four moves

Every pillar passed through the same sequence. The discipline is that no figure advances to the next stage until it can name its source. A line that cannot be sourced is not conservative — it is simply absent.

  1. Inventory the exposure surface
    Fourteen AI systems in production, pilot, or quietly shelved — each mapped to the contracts, processes, and obligations it touches.
    Days 0–14 · evidence only
  2. Attach a financial mechanism to each gap
    A control gap is not a risk until it has a path to a pound. Each one maps to lost revenue, remediation cost, or a probability-weighted penalty.
    Days 15–35 · mechanism & source
  3. Model in ranges, never points
    Low, expected, and high for every line, with the confidence stated. Point estimates are an invitation to argue about the wrong thing.
    Days 36–60 · range & confidence
  4. Try to break it before the board does
    An uninvolved reviewer attempts to move the headline using only the cited sources. If they can shift it beyond the stated range, the model is not finished.
    Days 61–75 · adversarial review

The mechanism in move two is the part most programmes skip. It is also the part the board cares about most, because it is the only part that connects a governance word to a financial one. Here is the form it takes for a single line:

Exposure=P(event)×Impact×Exposure windowMitigation
P(event) annual likelihood, sourced  ·  Impact revenue or cost at stake  ·  Window months unaddressed  ·  Mitigation value of controls already in place
Why ranges, not points
A single figure implies a precision the underlying data does not have. Ranges make the uncertainty explicit, which is what makes them harder to dismiss — you are no longer defending a guess, you are defending a method.

03The ledger, in full

This is the table the working paper opens to. Every pillar carries its expected figure, the 80%-confidence range around it, and the single source a reviewer would pull first. The total is the sum of expecteds; the range on the total is the sum in quadrature, not the sum of the extremes.3

PillarWhat it capturesRange (80%)Expected
Revenue
Pipeline & renewal
Procurement reviews stalled on AI assurance; tenders failing the security questionnaire.£0.74–1.08M£0.91M
Technology
Model & data
Untracked model drift and unlabelled training data carrying remediation cost.£0.44–0.72M£0.58M
Risk
Regulatory
Probability-weighted exposure under the EU AI Act’s high-risk obligations.£0.36–0.63M£0.49M
Operational
Process
Manual review effort and rework attributable to absent governance tooling.£0.31–0.55M£0.42M
Total quantified exposure£2.10–2.71M£2.40M
Figures annualised. Ranges at 80% confidence. Total range combined in quadrature.

Plotted as ranges rather than a column of numbers, the same ledger tells the board where the uncertainty lives. Revenue is both the largest pillar and the widest band — which is exactly where the next quarter of evidence-gathering should go.

Estimate ranges by pillar £0 ——— £1.2M
Revenue
£0.91M
Technology
£0.58M
Risk
£0.49M
Operational
£0.42M
Band = 80% confidence interval · dot = expectedWidest band: Revenue
The widest band marks where the next evidence should go.Source: working paper, §3.2
Checkpoint · before it leaves the building
Have someone who was not involved try to break it. If they can move the headline figure by more than the stated range using only the sources cited, the model is not finished. On this engagement, the first attempt moved it £0.3M — and sent us back to the revenue pillar for a fortnight.

04How a single line is computed

Transparency means the board can recompute any line themselves. The revenue pillar, for instance, is not a judgement call dressed up as a number — it is a function of three sourced inputs. Stripped of presentation, it looks like this:

revenue_pillar.calcworked line
# Lost pipeline attributable to AI-assurance friction
stalled_deals      = 7          # flagged in CRM, FY25
avg_contract       = 185_000    # £, trailing 12-mo mean
attributable       = 0.70       # share lost to assurance, sourced

expected = round(stalled_deals * avg_contract * attributable)
# → £905,500  ·  band ±18% at 80% confidence

Every figure on that line traces to a system of record: the deal count to the CRM, the contract value to finance, the attributable share to the post-mortems on each lost tender. None of it is invented. That is the whole trick, and it is not a trick at all.

05In the room

The number was not delivered as a slide. It was walked through at a table, one pillar at a time, with the working paper open and the sources within reach. The questions were sharp and the figure held — because it had already been broken once, in rehearsal, by people on our own side.

What survives a board meeting is rarely the most elaborate analysis. It is the one whose every figure can be traced back to something the room already trusts. The £2.4M held not because it was precise, but because it was legible — and a legible number is one a board can defend in its own words after you have left the room.

A number that cannot be argued with is not credible; it is opaque. The goal was never a figure beyond question — it was a figure worth questioning. The Govscape team
On anonymisation. Figures have been proportionally rescaled and the sector withheld to protect the client. The method, the pillar structure, the ranges, and the sourcing discipline are reproduced exactly as run.

Sources & notes

  1. Aggregation order. Pillars are modelled and reviewed independently, then summed. Early aggregation was the single most common error we saw in prior assessments of the same firm.
  2. Engagement working paper, §3. Internal document; figures rescaled for publication. Pillar weights computed on n = 14 in-scope AI systems.
  3. Combination in quadrature. The total range is √(Σ σᵢ²), not Σ of the extremes — assuming pillar errors are largely independent, which the review tested and accepted.
The Govscape teamAI governance, quantified
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